esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Experimental Methods and Evaluation Metrics  
The experiment was divided into the training and testing phases.  
Training phase: Ship images within training sets of both datasets were trained to obtain an optimum model.  
Testing phase: Testing was performed in two cases. In the first case, all samples in the testing set were tested to obtain the average classification accuracy, whereas the second case involved testing of different classes of ship images to obtain classification accuracy per class of ship images.  
In this study, the classification accuracy, F1-score, confusion matrix, and the average time consumption used for feature extraction per image were considered evaluation metrics concerning image-classification results.  
Classification accuracy corresponds to the ratio of the number of correctly classified samples to the total number of samples. The F1-score was considered a comprehensive measure of classification performance of the proposed method. By definition, the F1-score corresponds to the weighted average of the precision and recall, and its value lies in the range of 0 and 1. Mathematically, the F1-score can be expressed as:  
\[F1=\frac{2\times P\times R}{P+R}. \tag{5}\]  
Precision concerns the ratio of the number of true positives to the number of predicted positive samples. Recall can be defined as the ratio of the number of true positives to all positive samples. For the two-class problem, precision and recall can be evaluated as:  
\[P=\frac{TP}{TP+FP}, \tag{6}\]  
and  
\[R=\frac{TP}{TP+FN}, \tag{7}\]  
respectively, where \(TP\), \(FP\), and \(FN\) denote the number of true positives, false positives, and false negatives, respectively.  
The confusion matrix, as the name suggests, represents the confusion caused by the classifier when dealing with multiclass problems. Herein, the rows and columns represent the prediction and true classes, respectively, whereas diagonal elements denote the correct quantity of each ship class.
# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 2 Materials and Methods
### Dataset
Besides, the fast Fourier transform (FFT) was calculated, and 15 descriptors were applied to the result of FFT: min, max, mean, median, high median, low median, variance, standard deviation, Shannon entropy, kurtosis, skewness, energy, spectral centroid, spectral flatness, and crest factor. The matched filter was also applied to the original segments, and a Boolean feature was generated with the results. Then, we obtained 142 features: 126 features calculated from the original segment and 5 wavelet coefficients (21 \(\times\) 6), 15 features extracted from the FFT calculation, and the matched filter. Considering the number of segments that could be analyzed in a single EEG record (1 EEG with 21 channels and 30 min of duration could generate more than 37,800 segments of 200 samples), it is necessary to reduce the number of features not only to reduce the complexity of describing the segments but also to avoid the introduction of noise and redundant information into the classification process and increase the stability of the classifiers.
## 4 Case Study: Damage Identification of a Jacket Platform
### Damage Scenario  
Supposing that the mass of the structural components is unchanged, damage of the components is simulated by reducing their elastic modulus. The damage value for each damage component is a random value between 0.0% and 50.0% (i.e., \(\mathrm{E}_{di}/\mathrm{E}_{ui}=\) 0.5-1 as shown in Equation (9)).  
Four main tubes support the entire platform structure. These main tubes are the most important components for a jacket structure. The main pipes near the mean water level are vulnerable to damage because they always suffer serious corrosion together with complex external loading. Therefore, this paper selects eight members of the platform near the sea level as the damage components which  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Element** & **Element Name** & **Elastic Modulus (Pa)** & **Poisson’s Ratio** & **Density (kg/m\({}^{3}\))** & **Section Type** & **Dimension (mm)** & **Finite Element Type** \\ \hline E1 & Dominant tube above the soil & \(2.1\times 10^{-11}\) & 0.3 & 7850 & Circular steel tube & \(\mathrm{\Phi}\)1200 \(\times\) 50 & PIPE99 \\ E2 & Dominant Tube beneath the soil & \(2.1\times 10^{-11}\) & 0.3 & 7850 & & \(\mathrm{\Phi}\)1200 \(\times\) 50 & PIPE20 \\ E3 & Main pipe on deck section & \(2.1\times 10^{-11}\) & 0.3 & 7850 & & \(\mathrm{\Phi}\)4780 \(\times\) 38 & PIPE99 \\ E4 & Horizontal support & \(2.1\times 10^{-11}\) & 0.3 & 7850 & & \(\mathrm{\Phi}\)4780 \(\times\) 38 & PIPE99 \\ E5 & Diagonal piece & \(2.1\times 10^{-11}\) & 0.3 & 7850 & & \(\mathrm{\Phi}\)6508 \(\times\) 25.5 & PIPE99 \\ E6 & Deck beam & \(2.1\times 10^{-11}\) & 0.3 & 7850 & Square steel & \(\mathrm{\Phi}\)400 \(\times\) 400 & BEAM4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the structural model.  
Figure 4: An offshore jacket platform.  
are named as E1,2, E2,3, E7,8, E13,14, E19,20, E8,9, E14,15, E20,21. Here, \(Ei_{j}\) refers to the component between the \(i\)th node and the \(j\)th node. In this paper, the finite element code ANSYS is used to analyze the structural natural frequencies and modal shapes. Generally, for marine structures, the effect of the hydrodynamic added mass on the natural frequencies of the global mode shapes is limited, especially for the lowest modal shapes [51]. Since the focus of this paper is on a new approach for damage identification, we do not consider the effect of hydrodynamic added mass in the analysis for simplicity. During calculation, 12 nodes corresponding to the sensor locations are selected as the test points. As shown in Figure 4, the test nodes are No. 1, 2, 3, 7, 8, 9, 13, 14, 15, 19, 20, 21. After calculation, the first 6 natural frequencies and the corresponding displacement modal values at the 12 nodes in the \(x\)-, \(y\)-, and \(z\)-directions are determined for the damaged structure. Then, the frequency indices (\(i\) = 1-6) and the modal indices (\(l\) = 1-12) are combined according to Equations (5) and (6), and 20,000 sets of training sample data are obtained. Selected sample data are shown in Table 2.
## 3 Case Study: Energy Demand and Economic Parameters  
Both energy system configurations under analysis have been tested in a case study. Energy demand data of a large office building located in Pisa (Italy) have been considered. The office is located in an 8-story building, with a gross volume about 73,000 m\({}^{3}\) and a floor area about 24,000 m\({}^{2}\), having 25-cm concrete walls and double-glazed windows (the window-to-wall ratio is 0.16). DHW demand is met by small local electrically driven heaters, so the plant must satisfy only space heating and cooling requirements. The complete low-temperature district heating and cooling network is not modelled. Just to test the viability of the energy supply plant using reversible heat pumps, the energy demand is simplified to cover the demand of an office building that could be representative of the energy profiles expected to be covered in urban DHC networks. The proposed methodology and system configuration could be easily applied to a bigger-scale system. Figure 5 shows the hourly energy demand data for the case study. Three energy services are needed: electricity, cooling (chilled water from 7 to 12 \({}^{\circ}\)C) and heating (hot water from 45 to 35 \({}^{\circ}\)C). Hourly data of electricity consumption were available from measurements, while thermal load demands were evaluated by a dynamic simulation in TRNSYS 17. The hourly profiles of the external temperature, relative humidity, solar irradiance, and wind speed of the typical meteorological year [36] of Pisa have been adopted.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _PopulationSize_ & _EliteCount_ & _TolFun_ & _CrossoverFraction_ & _MigrationDirection_ \\
250 & 10 & \(1e-6\) & 0.8 & \({}^{\prime}forward^{\prime}\) \\ \hline _StallGenLimit_ & _Generations_ & _TolCon_ & _MigrationFraction_ & _MigrationInterval_ \\
50 & 400 & \(1e-3\) & 0.2 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Settings and parameters adopted for the Genetic Algorithm.  
Figure 4: Optimization procedure.  
Table 2 shows the values of all the economic parameters adopted in the simulations. The considered lifetime for all technologies is 20 years [13].
## 3 Results
### Model Outputs: Grain Yield  
DSSAT CERES Rice was used to simulate grain yield in the Kharif season using weather and soil data. Model-simulated grain yields in each field under varied management practices were compared with the observed yields. Observed yields across the selected villages in Karimnagar ranged from 4100 kg ha\({}^{-1}\) to 5800 kg ha\({}^{-1}\), whereas the simulated yields ranged from 4300 kg ha\({}^{-1}\) to 6000 kg ha\({}^{-1}\). Among the selected villages, the highest yields were in Vedurugattu (observed: 5200 kg ha\({}^{-1}\) to 6300 kg ha\({}^{-1}\); simulated: 4100 kg ha\({}^{-1}\) to 5800 kg ha\({}^{-1}\)). The model results were statistically analyzed, revealing an overall correlation of 0.80 with an RMSE of 658 kg ha\({}^{-1}\), D-Index of 0.88, and NSE of 0.70 (Figure 5). From the results, a slight deviation was detected between the observed and simulated yields, where the simulated yields were more than the observed. This was due to the model not considering the loss due to biotic factors, like pests and diseases, and abiotic factors, like lodging, waterlogging, etc.  
Individual village-wise statistical analysis was also performed (Table 3). From the statistical results, it is clear that model-simulated yields were reliable and agreed with the observed yields in all selected villages.  
Figure 4: Spatial distribution of the rice area in the Karimnagar district during the 2021 wet season.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Village Name** & **R\({}^{2}\)** & **RMSE** & **D-Index** \\ \hline Elbaka & 0.80 & 374 & 0.86 \\ \hline Gangipalle & 0.87 & 238 & 0.93 \\ \hline Rukmapur & 0.76 & 400 & 0.73 \\ \hline Vedurugattu & 0.72 & 270 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical analysis of the selected villages.  
The differences did not lead to a significant decline in the correlation, and the model successfully simulated grain yield during both growing seasons within acceptable limits of error.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 9 Lessons Learned, Suggestions, and Recommendations
### Suggestions and Recommendations
#### 9.2.2 Network Segmentation for Smart Grids  
Network segmentation is a critical strategy for enhancing the security and resilience of smart grids by dividing the network into smaller, isolated segments or zones. This approach limits the spread of potential cyberattacks and confines breaches to a single segment, preventing lateral movement across the entire network. Each segment can be tailored with specific security policies and controls that address that zone's unique needs and vulnerabilities. The effect of cyberattacks and unauthorized access is reduced by separating critical assets, such as data centers and control systems, from less vulnerable areas, including administrative networks. This strategy protects the availability and integrity of smart grid operations by narrowing the attack surface, tightening access constraints, and facilitating more targeted monitoring and response capabilities inside each segmented zone.
# A Secured Intrusion Detection System for Mobile Edge Computing
## 2 Literature Review
### Edge Computing  
The rapid evolution of computing paradigms, coupled with the pervasive integration of Internet of Things (IoT) devices [5], has led to a paradigm shift in network architecture. Edge computing, with its emphasis on decentralized data processing at the network periphery, has emerged as a transformative approach to address the challenges posed by the massive influx of data and the demand for low-latency, high-throughput applications. However, the distributed nature of edge computing introduces new security concerns, necessitating innovative solutions to safeguard critical data and infrastructure. In the context of edge computing, the security architecture must evolve to address the unique characteristics and challenges associated with this decentralized paradigm [6]. Traditional security models, primarily designed for centralized architectures, may prove insufficient to protect the diverse and dynamic edge computing environments. Intrusion detection systems (IDSs) play a pivotal role in identifying and mitigating potential threats, ensuring the integrity, confidentiality, and availability of data and services.
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 3 Sensor Activation and Related Work
In addition, it was reported that the harvested energy by the receiver may decrease with increasing distance between the receiver and the transmitter. The authors in [66; 67] showed that the hardware setup could only reach a maximum distance of 4 m for a successful wake-up. Considering the AG-WSN scenario proposed in this work, the passive RF-based wake-up design is not suitable. Regarding the active RF wake-up design, as mentioned in [46], there are 13 active RF-based wake-up methods using discrete components, whereas there are 29 methods using complementary metal-oxide-semiconductor (CMOS technology. The most significant parameters relevant to these designs and prototypes for the interest of the proposed AG-WSN configuration are power consumption, range, address decoding capability, wake-up latency, and balancing. For example, the author in [68] configured the wake-up receiver using discrete components and claimed to achieve 120 m of communication range. However, the receiver consumed 1620 \(\upmu\)W at the state of idle-listening, which is too high for the battery-powered nodes. There is a low-power design in [69], which only consumed 52 \(\upmu\)W. Unfortunately, the authors did not provide a range test. A favorable design was presented in [70] recently. It achieved a communication range of 50 m at idle-listening with a power consumption of 1.2 \(\upmu\)W. Unfortunately, at the time of our experiment, there was no market-ready product or porotype based on this design. In this paper, the use of off-the-shelf components is stressed in our prototyping and experimental validations, with the goal of rapidly putting the proposed AG-WSN into practice. As many researchers have similarly done, the AS393X wake-up receiver has been used in many efforts and designs [71; 72; 73; 74; 75]. Among these researchers, the author in [74] used AS3933, which is the same chip in our experiment, to prototype the receiver circuit to have an 87 m communication range, at the cost of more than 5000 \(\upmu\)W of power consumed when decoding the wakeup signal. Also, in a recent paper, the authors compared the RF wake-up mechanism and the low-power listening techniques [76]. They concluded similarly what we achieved in our energy evaluation results in this paper. However, the authors of this paper did not measure the delay caused by the RF wake-up transmission, and their power consumption measurement was not based on batteries, but a constant power supply, hence, it lacked a realistic configuration.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 7 Experiments and Results
### Finding the Best Number of Clusters and Their Corresponding Models  
The number of clusters in our final model will be determined based on the G-mean results of the training phase. Cross-validation approaches can help avoid overfitting in model selection [76]. Therefore, the training process is conducted based on two-folds cross-validation to avoid overfitting. Four settings are experimented to determine the required number of clusters: 3, 5, 7 or 9. The number of clusters that yield the highest G-mean value will be selected to be applied for the final model in the testing phase. Tables 3 and 4 show the G-mean results of the cross-validation training phase of our approach based on the basic classifiers and ensemble ones, respectively. As it can be seen in the tables, the best number of clusters varies from one dataset to another. This confirms that the software defect benchmark datasets are varied in their nature, where different groups can be identified, and these groups have a number of similar patterns. Tables 3 and 4 also show the distribution of classes in each cluster. It is important to note here that sometimes the clustering process produce clusters that contain only class. For such cases, any new instance that is closer to the center of a one-class cluster will be simply given the same class of the cluster. For each cluster that results from the previous step, the best performing classifier is assigned. Demonstrating the best performing models for KMB and KME, Figures 3 and 4 show the frequency of the best models over all datasets. In the case of KMB, we can see that the DT is the most frequent classifier in most of cases, followed by NB and \(k\)-NN. In the case of KME, Bagging is the most frequent model, followed by XGB, RF and AdaBoost, respectively. This supports the idea that there is no dominating classifier for all the data patterns, and each group of similar patterns needs a model that fits its particular characteristics.
## 4 Conclusions  
This study aims to investigate the impact of hyperparameters, regularization parameters, correlated features, dataset sizes, and imbalanced datasets on the performance of six machine learning models in detecting GPS spoofing attacks: ANN, SVM, RF, GNB, CART, and LR. The evaluation was performed using four metrics: probability of detection, probability of false alarm, probability of misdetection, and accuracy. The simulation results indicate that using inappropriate of hyperparameters, dataset size, features, and imbalanced datasets adversely affect the models' performance. Although this study was performed on GPS spoofing attacks, the results apply to any applications that use machine learning models. AS future work, we plan to extend this investigation to evaluate the effectiveness of other widely used approaches, such as deep learning and deep convolutional models, considering additional evaluation metrics, such as computational complexity, memory usage, and detection time.  
**Author Contributions:** Conceptualization, T.T.K. and S.I.; software, T.T.K.; data curation, T.T.K.; formal analysis, T.T.K. and S.I.; investigation, T.T.K.; methodology, T.T.K. and S.I.; visualization, T.T.K. and S.I.; writing--original draft, T.T.K., S.I. and N.K.; writing--review and editing, T.T.K., S.I. and N.K.; project administration, K.A.S. and V.K.D.; validation, K.A.S. and V.K.D.; supervision, N.K.; funding acquisition, N.K. All authors have read and agreed to the published version of the manuscript.  
This work has the support of the National Science Foundation (NSF), Award Number: 2006674.  
Not applicable.  
**Data Availability Statement:** Dataset will be available in GitHub and other sites.  
The authors acknowledge the support of the National Science Foundation (NSF), Award Number: 2006674.  
The authors declare no conflict of interest.
## 3 The Development of Detection Technology
### Advances in Fluorescent Molecular Probe Method  
When a fluorescent molecule is irradiated by ultraviolet or visible light, it can excite fluorescence, and the fluorescence properties, such as excitation/emission wavelength, intensity, and polarization, can change with the environment, so as to realize the detection of samples. [PERSON] [77] synthesized fluorescent probes for Cl\({}^{-}\) detection, which were named \"M2\" and \"MY\". M2, a derivative of 6-methoxyquinoline, had a high relative fluorescence quantum yield and could be quantitatively detected at pH = 7.4. MY was synthesized with M2 as a recognition group and naphthalene as a reference group. As a ratio fluorescent probe, MY had excellent selectivity and anti-interference and could detect Cl\({}^{-}\) in actual water samples and biological samples. [PERSON] et al. [78] introduced fluorescein into polyacrylamide hydrogel particles to make a fluorescence sensor, and the detection time was 5-7 min. When the sensor encountered Cl\({}^{-}\), fluorescence quenching was induced. The fluorescence signal of the sensor followed the Stern-Volmer equation and showed a negative linear relationship with the concentration of Cl\({}^{-}\) in the range of 0-130 mmol/L. The sensor had good reproducibility and reversibility. [PERSON] et al. [79] studied the ruthenium complex of bis (benzimidazole-2-ethyl) sulfide as a fluorescence sensor for the recognition of Cl\({}^{-}\). The sensor selectively combined with Cl\({}^{-}\) to stimulate fluorescence at 365 nm, and the detection limit of Cl\({}^{-}\) was up to 0.44 umol/L by measuring fluorescence intensity at 453 nm. It has a good anti-interference ability with common ions such as F\({}^{-}\), Br\({}^{-}\), I\({}^{-}\), NO\({}^{3-}\), and SO\({}_{4}\)\({}^{2-}\) plasma. Graphitized carbon nitride (PCN) is a new conjugated polymer semiconductor. The adsorption of Cl\({}^{-}\) in the PCN gap will cause the fluorescence attenuation of the semiconductor, so the material has potential value for the detection of Cl\({}^{-}\). [PERSON] et al. [58] used silver to modify PCN to obtain silver-modified carbon nitrite nanosheets. This material had high porosity and coordination function and was more likely to adsorb Cl\({}^{-}\) in water compared with the single PCN, thus improving fluorescence detection performance. Cl\({}^{-}\) could be quantitatively detected within the concentration range of 0.5-100 mmol/L. It had strong practical application.
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Gaussian Process Regression (GPR)
#### 2.4.5 Machine Learning Model Optimization by Hyperparameter Tuning  
The present study evaluates machine learning models with four different algorithms for predicting geotechnical properties. Optimal model performance can be achieved by assigning the proper parameters for training the model by performing hyperparameter tuning. The optimum hyperparameters were obtained when the trained model reached the best validation performance after performing the iterative training-validation process. Each MLA has its own hyperparameters to be tuned. Thus, optimization needs to be performed for each model. Among available optimization techniques such as Bayesian optimization and grid search and random search [38; 39; 40], the random search optimization algorithm is chosen for this study. The next set of hyperparameters is obtained randomly while continuously improving the quality of the features [33]. This algorithm was chosen due to computational efficiency compared to other methods such as the grid search. The chosen number of iterations for this random search is 60, as suggested by [PERSON] and [PERSON] [41] as an adequate number to achieve the near optimum value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
\begin{tabular}{c} Location Information (EDF/X-Y \\ coordinates/EDFB), Z Coordinate \\ (Elevation), GWL, and \% Finer than \\ 0.075 mm Sieve \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Constructed machine learning models in this study.