esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of Epileptic EEG Signals Using Convolutional Neural Networks
###### Abstract  
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.  
epilepsy; deep learning; convolutional neural networks; electroencephalography +
Footnote †: journal: Applied Intelligence Research Centre, Department of Computer Engineering, Near East University, North Cyprus, 10 Mersin, Turkey; [EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (J.B.I.);[EMAIL_ADDRESS] (A.I.)
## 2 Materials and Methods
### Crop Model--Dssat
#### 2.5.1 Weather Data  
Weather inputs, such as daily minimum and maximum temperatures, solar radiation, and rainfall, were necessary for the DSSAT model simulation. For each selected village over the season, meteorological information was acquired from automated weather stations near the villages, except for solar radiation. Solar radiation was computed through the Hargreaves equation [46] using the maximum and minimum temperatures, as follows (Equation (1)):  
\[R_{s}=K_{RS}\sqrt{(T_{max}-T_{min})\ R_{a}} \tag{1}\]  
where \(R_{a}\) = extra-terrestrial radiation, \(T_{max}\) = maximum temperature, \(T_{min}\) = minimum temperature, and \(K_{RS}\) = adjustment co-efficient (0.16 for interior locations and 0.19 for coastal locations).  
\begin{table}
\begin{tabular}{c c c} \hline
**Satellite Imagery** & **Band** & **Significance** \\ \hline \multirow{2}{*}{Sentinel-2} & \multirow{2}{*}{B4 (Red)} & Helps in recognizing the difference between vegetation and other classes \\ \cline{2-3}  & & B8 (NIR) & Helps in identifying Water and vegetation \\ \hline \multirow{2}{*}{NDVI} & \multirow{2}{*}{(B8–B4)/B8 + B4} & Identifies greenness. \\  & & (vegetation identification) \\ \hline \end{tabular}
\end{table}
Table 1: Details of Sentinel 2.
# A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines
## 3 Results  
### Dataset Introduction  
To investigate the fine-tuning capability of the model on small-scale datasets, we constructed a relatively small custom dataset. The training set consists of 400 images, including 100 images each from the engine bay, APU bay, EE bay, and cargo hold, with 25% of the images used for validation during training. The test set contains 100 images, with 25 images from each of the same four compartments, which collectively represent the typical environments where aircraft fire detection lines are located. All images were captured under natural lighting conditions, from a human inspector's perspective, perpendicular to the plane formed by the two fire detection lines. Interactive annotations were performed using EISeg, and standard data augmentation techniques were applied.  
Unlike conventional datasets, our dataset is task-specific. During the image acquisition process, we applied standard inspection procedures to measure the minimum distance between the fire detection lines within the camera's field of view. These measurements were recorded alongside each image. In the subsequent training process, the recorded minimum distance was used as part of the input to the fusion loss function. During testing, in addition to computing the Dice coefficient for segmentation accuracy, the model's accuracy in estimating the minimum distance between the fire detection lines was also evaluated. This design enhances the model's applicability to the target task.  
Figure 4: Fire detection line spacing calculation method.  
Figure 3: The framework of the proposed ASAM-based segmentation and distance measurement method.
## 4 Results
### Performance Evaluation of Landslide Models  
The model evaluation was performed using both the training set and validation set. The former shows the model's fitting skill and the latter reflects its generalization ability. The accuracy (ACC), Kappa coefficient (K), and the receiver operating characteristic (ROC) curve technique were employed as performance metrics in this study. The ACC is the ratio of landslide and non-landslide grid cells that are correctly predicted, while the K measures the reliability of a constructed model. As for the ROC curve, it is a popular method to evaluate the performance of the landslide model [78,79]. The area under the ROC curve (AUC) value is the area under the ROC curve, and a higher AUC value indicates the model is better.  
The overall performance is shown in Table 4 and Figure 10. All ensemble models performed well in goodness of fit as well as generalization ability. Note that the SANL model, the SANL model, and the ANL model yielded almost equal values with respect to the ACC, K, and AUC, but in detail the SAN model (with the ACC, K, and AUC values of 86.95%, 0.739, and 0.951) and the ANL model (with the ACC, K, and AUC values of 86.58%, 0.732, and 0.944) gained slightly better goodness of fit than the SANL model (with ACC, K, and AUC values of 86.40%, 0.728, and 0.945, respectively). The SAL model had a relatively lower performance especially in generalization ability, with values of 82.33%, 0.647, and 0.886 for ACC, K, and AUC, respectively, on the validation set.
## 3 Event Monitoring by Sensor Systems for Multiple Satellites  
### Case Study 1: Enhancement of High-Energy Electron Flux by a Coronal Hole  
We compare the performance of the forecasting systems based on the four-sensor (from two satellites) and eight-sensor (from four satellites) models. Figure 8 shows the time development of the credibility predicted by the network based on the eight sensors from the four satellites. Figure 9 shows the snapshot of the nodes in the dynamic relational network at the initial snapshot (Figure 9a) and at step 84 (Figure 9b). Each node outputs high credibility until step 84. These sensors are evaluated as normal. This network also fails to detect a warning level of the high-energy electron flux between steps 60 and 80. At step 84, the credibility of all the nodes decreases to half except the two sensors on high-energy electron fluxes. The credibility of the high-energy electron flux is evaluated as entirely abnormal at this step. After this step, all nodes retain high credibility except the high-energy electron flux. The high-energy electron flux 24 h ahead shows a little change in the credibility between steps 80 and 100. Subsequently, however, the high-energy electron flux 24 h ahead shows low credibility, because it predicts the warning condition of high-energy electrons at GEO. The eight-sensor model by four satellites is successful for prediction of warning level of the flux. Furthermore, the credibility of the flux 24 h ahead does not lead to the oscillation observed in the network by the four-sensor model.  
Figure 9: Snapshot of nodes in the dynamic relational network (eight-sensor model). The nodes evaluated as faulty from all sensors are colored red. The nodes evaluated as faulty from half nodes are also colored red. The plus and minus signs on arcs represent a diagnosis result (plus = normal/minus = abnormal). Rectangles represent regions of satellites equipped with sensors for observation. The solid lines indicate two nodes diagnose each other. The dashed lines indicate unidirectional diagnosis from one node to another one. The symbol \(\mathbf{V}_{i}\) and \(\mathbf{B}_{\vec{\sigma}}\) (\(i=1\), 2, 3) respectively represent solar wind speed and north-south component of interplanetary magnetic field. The symbol \(\mathbf{E}\) and \(\mathbf{E}_{\mathbf{24}}\) respectively represent high-energy electron flux and its 24 h ahead. (**a**) Initial state (Step = 0); (**b**) Step = 84.
# Simulation Study of Thermal-Mechanical Coupling Fretting Wear of Ti-6 Al-4V Alloy
## 3 Finite Element Model
### Validation of the Model  
In order to validate the finite element model, the finite element model is compared to the contact pressure of the analytical solution of Hertzian theory [15], as depicted in Figure 5a. It can be found that the finite element results and the theoretical calculations basically agree when the normal load F is 185N. There is a small deviation between the finite element results and Hertzian theory at the edge of the contact region, which is caused by the mesh size. Then, the energy model and Archard model [19] were applied to the established finite element model to illustrate the correctness of the wear model by comparing the results of the two models, as shown in Figure 5b. As can be seen in Figure 5b, the wear depth of the energy model is almost the same as that of the Archard model when temperature is considered. However, the wear depth of the energy model is somewhat smaller because the friction coefficient decreases when the temperature increases. The Archard model cannot take into account the change in the coefficient of friction, so the energy model has a smaller depth of wear. Temperature increases as the wear progresses, causing the difference between the two models to increase with the amount of fretting. For the energy model, the depth of wear is greater when temperature is considered. This is because the wear rate rises as temperature rises.
# Passive Vibration Control of a Semi-Submersible Floating Offshore Wind Turbine
## 2 Modeling the Dynamics of Semi-Submersible FOWT
### Simplified Dynamic Model for Semi-Submersible FOWT
In order to be consistent with the FAST program, the reference point located in the intersection of sea level (SL) and the centerline of platform was chosen for output analysis. The inertial coordinate system was established with the reference point P as the origin. 2. Unlike the spar foundation, the heave motions of the semi-submersible-type foundation resulted in a negligible impact on the pitch motion. Therefore, the simplified dynamic model of OC4-DeepCwind was built using only four DOFs: the platform surge, pitch, tower tilt, and TMD motion. Moreover, since the TMD device was relatively small in mass to the overall FOWT, its rotation was ignored and only the translation was considered. 3. The tower was hinged to the foundation at the tower bottom, and both interactions between the foundation and the tower, and between the TMD device and nacelle were treated as a linear spring and a linear damper. 4. Because only the free decay motion was used to identify the model parameters and optimize the TMD parameters, the external forces like incoming wind and wave loadings were not accounted for in the simplified model. However, the system coupled effects with the hydrodynamics and mooring loads were fully considered in the model. Furthermore, even under extreme conditions, the platform pitch responses do not exceed \(10^{\circ}\). Two common small angle approximations, i.e., \(\sin\theta\approx\theta\) and \(\cos\theta\approx 1\) were introduced throughout the study. Based on the above assumptions, [PERSON] principle can be employed to describe the overall dynamics of rigid bodies. The key point of [PERSON] principle is that a dynamic problem can be treated as a static problem by introducing the inertia forces. As mentioned above, point P was adopted as the origin of all the motion and the quasi-static equilibrium equations can be expressed as follows:  
\[ \begin{array}{c}\sum m_{i}\mathbf{a}_{i}=\mathbf{F}\\ \sum\mathbf{r}_{i}\times m_{i}\mathbf{a}_{i}+l_{i}\mathbf{\alpha}_{i}=\mathbf{ T}\end{array}  \tag{1}\]  
where \(\mathbf{F}\) and \(\mathbf{T}\) denote the sum of all external forces and the sum of all moments about P, respectively. While \(m_{i}\) represents the mass of body \(i\), namely platform, tower, and TMD, and \(l_{i}\) denote inertia. \(\mathbf{a}_{i}\) and \(\mathbf{\alpha}_{i}\) are the acceleration vector and angular acceleration for rigid body \(i\) respectively. \(\mathbf{r}_{i}\) is the position vector from P to body \(i\). \(\sum m_{i}\mathbf{a}_{i}\) and \(\sum\mathbf{r}_{i}\times m_{i}\mathbf{a}_{i}+l_{i}\mathbf{\alpha}\) are vector sums of inertial forces and torques about P, respectively. In Equation (1), the left side of the equation is the terms related to the second derivative of the displacement, while the right side of the equal sign is the rest of forces and moments. Therefore, the next major task is calculating all force components of the model.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 7 Conclusions  
Network lifetime maximization of wireless in-body area networks is one of the critical challenges in WBANs. In this paper, an MI-based incremental relaying communication protocol was proposed. The network topology is as follows: nine implanted sensors are located inside the human body while relay nodes and the coordinator are attached to a patient's clothes. Once the MI value is smaller than the predetermined threshold, data transmission is allowed. A minimal transmission length algorithm is utilized to decrease the network energy consumption. Considering the maximization of the network lifetime, various selected QoS mathematical models and the related subjective functions were derived. The results justify that the introduction of the MI-based incremental relaying technique decreases the average energy consumption per bit to around 0.7 mJ which yields a network lifetime extension as compared with the non-MI-based incremental relaying method at approximately 0.76 mJ and the two-relay-based routing scheme at 2.1 mJ.  
Our future work is focused on QoS provisioning for mobility support WBAN protocol design on a real experimental testbed such as Delsys EMG sensor networks [36]. Moreover, new networking technology for WBANs is also taken into consideration.
## 1 Introduction
Deep learning (DL) enables automated feature extraction without human intervention [17]. RGB imagery as inputs have been coupled with deep learning models in number of pre- and post-harvest agricultural applications [18]. One such study by [PERSON] et al. [17] consisted of the application of DL models (AlexNet, GoogLeNet, ResNet-50, and Inception V3, among others) with RGB imagery inputs for classifying water-stressed and non-stressed field crops, namely soybean, maize, wheat, and okra. So far, most computer vision techniques have used RGB images to predict crop water stress status [19; 20], while thermal imagery is limitedly explored through DL. Recently, both thermal and RGB images have been used as inputs to identify water stress in wheat [12] using DL. ResNet-50 is widely recognized for its deep structure and residual learning skills, enabling it to effectively catch intricate patterns and complex traits in high-resolution images. This feature makes it well-suited for differentiating nuanced differences in thermal and RGB images that are essential for identifying water stress in crops. However, MobileNetV2 presents an efficient and effective architecture, specifically designed to maximize performance for devices that have limited processing capabilities. This is particularly crucial for on field applications in agriculture, where rapid evaluation and usage of mobile or hand-held devices are frequently necessary. The integration of these two models offers a wide range of practicality, striking a balance between profoundness and intricacy, while also ensuring effectiveness and expandability. Exploration in this direction is further needed to validate the implementation of thermal-RGB imagery and DL for non-invasive and water stress detection in other crops. Conventionally, RGB imagery has been mostly used as inputs to DL models for crop characterization and phenotyping, including water stress. It has however been well reported that thermal imagery can be a better characterizer of water content in crops but has been limitedly explored with DL models for water stress prediction. Alleviating this gap is the goal of this study. In addition, most explored DL models are heavy in architecture and need heavy computing resources for deployment. Therefore, in addition to a heavy DL model, this study will evaluate a light DL model that can be deployed on a resource-constrained device such as a smartphone or microcomputer. These unexplored aspects will be applied to okra, which is an economical vegetable crop consumed worldwide. Okra has been limitedly explored to predict water stress using thermal-RGB imaging and AI techniques. This study is intended to create potential for prompt and efficient irrigation management; therefore, its specific objectives are (i) assessment of the performance of okra with different irrigation methods (flood and sprinkler) and levels with respect to crop evapotranspiration in terms of yield and water use efficiency and (ii) to identify water stress in okra using thermal-RGB imaging and AI techniques.
# Technology Identification, Evaluation, Selection, and Optimization of a HALE Solar Aircraft
## 2 HALE Solar Aircraft Concepts
### Aerodynamic Analysis
Use of low-fidelity methods (XFLR5) can cause aerodynamic modeling errors. However, for fast and low-cost analysis, we used XFLR5 and OpenVSP. The produced aerodynamic data were applied to the self-developed solar aircraft design program. Since the Oswald span coefficient varies depending on the aspect ratio, errors occur in the calculation of the induced drag (\(C_{D,\,\mathit{induced}}\)) and the form drag (\(C_{D,\,\mathit{form}}\)), by the estimation method. Therefore, XFLR5 is used to increase the accuracy. To understand the aerodynamic characteristics of the previously selected airfoil DAE-11, two-dimensional lift, drag coefficient curve, and drag polar were analyzed as shown in Figure 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Fuselage** & **Main Wing** & **Horizontal Wing** & **Vertical Wing** \\ \hline Notation (\(i\)) & \(1\) & \(2\) & \(3\) & \(4\) \\ Wetted area (\(S_{\mathit{level}}\)) & \(4.82\) & \(61.1\) & \(6.86\) & \(1.67\) \\ Reference area (\(S_{\mathit{ref}}\)) & \(5.44\) & \(30.3\) & \(3.36\) & \(1.92\) \\ Form factor (\(FF\)) & \(1.02\) & \(1.27\) & \(1.21\) & \(1.21\) \\ \(t\)/\(c\) & \(-\) & \(0.13\) & \(0.10\) & \(0.10\) \\ \(f\) & \(19.88\) & \(-\) & \(-\) & \(-\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: HALE solar aircraft wetted and reference area parameters. Figure 2: DAE-11 airfoil aerodynamic analysis. Using the aerodynamic analysis results of the DAE-11 airfoil, the vortex lattice method (VLM) for the finite wing (3D) is used to calculate the induced drag (\(C_{D,\text{ }induucl}\)) and the form drag (\(C_{D,\text{ }form}\)) as shown in Figure 3.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
###### Abstract  
The very fast evolution in wearable electronics drives the need for energy storage micro-devices, which have to be flexible. Micro-supercapacitors are of high interest because of their high power density, long cycle lifetime and fast charge and discharge. Recent developments on micro-supercapacitors focus on improving the energy density, overall electrochemical performance, and mechanical properties. In this review, the different types of micro-supercapacitors and configurations are briefly introduced. Then, the advances in carbon electrode materials are presented, including activated carbon, carbon nanotubes, graphene, onion-like carbon, and carbide-derived carbon. The different types of electrolytes used in studies on micro-supercapacitors are also treated, including aqueous, organic, ionic liquid, solid-state, and quasi-solid-state electrolytes. Furthermore, the latest developments in fabrication techniques for micro-supercapacitors, such as different deposition, coating, etching, and printing technologies, are discussed in this review on carbon electrode-based micro-supercapacitors.  
carbon; energy storage; micro-machining; supercapacitor; thin film; wearable electronics +
Footnote †: journal: Applied Sciences
## 2 ISF Coefficient Quantization in AMR-WB  
In AMR-WB, a linear prediction analysis is made as follows. As the first step, a speech frame of 20 ms is applied to evaluate linear predictive coefficients (LPC), which are then converted into ISF coefficients. Subsequently, quantized ISF coefficients are obtained following a vector quantization process, which is detailed below.
## 4 Project Examples
### Experimental Analysis
#### 4.3.4 Ablation Experiment  
In order to explore the effectiveness of the Attention Gate module used in this paper, an ablation experiment was conducted on a dataset of railway subsidence caused by tunnel construction. In order to verify the effectiveness of the Attention Gate, a Bi-Mamba prediction model without the Attention Gate module was constructed and tested using the dataset constructed in this paper. The test results are shown in Figure 13.  
The MSE and MAE of the Bi-Mamba model without the Attention Gate module are 0.307 and 0.334, respectively, which are 0.028 and 0.058 higher, respectively, than those of the complete Bi-Mamba model. The above results show that after the introduction of the Attention Gate module, the comprehensive prediction performance of the model is significantly improved, indicating that the Attention Gate module can effectively improve the model's ability to capture key information and dynamic relationships, and reduce the impact of redundant information on the prediction results.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossless Data Reduction Algorithms
#### 8.2.2 BZip2  
The BZip2 algorithm, implemented using the bz2 Python library [47], encapsulates the underlying logic, consistent with usual practices in the Python ecosystem. This encapsulation simplifies reproduction and provides an accessible and user-friendly approach. In particular, the BZip2 algorithm excels in preserving data value after compression, ensuring complete data integrity. While it has a relatively slower compression speed, potentially less suitable for real-time data transfers, the algorithm's ability to maintain data integrity compensates for this trade-off. In addition, its data type agnosticism proves beneficial, making it a versatile choice for various applications. Table 13 summarizes the key metrics from our implementation and study, revealing the algorithm's performance. BZip2 took less time to compress the Electric Production dataset, a temporal and numeric dataset, taking 0.001 ms to reduce \(-98.63\%\) of the original dataset, processing 1 MB in 0.0137 ms.