esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable MRI-Based Ensemble Learnable Architecture for Alzheimer's Disease Detection
## 3 Methodology
### Model Interpretation  
This study employs three prominent explainable artificial intelligence (XAI) techniques, GradCAM, Local LIME, and Saliency Maps, to enhance the interpretability of the proposed ensemble framework. These methods provide distinct yet complementary insights into the model's decision-making process, thereby improving transparency and fostering trust in the predictions. GradCAM generates class activation maps by identifying regions of input images that significantly influence predictions, focusing on feature localization in convolutional layers. Local LIME constructs a model-agnostic surrogate model, which is typically linear, to approximate the contributions of specific features to the prediction, offering interpretability irrespective of the underlying architecture. Saliency Maps utilize gradient information to directly compute the sensitivity of the prediction with respect to input features, enabling a visualization of critical input regions influencing the output.  
The distinction among these techniques lies in their methodological focus and application scope. GradCAM emphasizes the spatial importance of features within convolutional neural networks, making it particularly effective for visualizing the decision-making process of CNNs. In contrast, Local LIME operates independently of the model architecture, explaining predictions through local approximations and feature attributions. Saliency  
Figure 3: Hard voting ensemble architecture for multi-model classification.  
Maps provide a more direct gradient-based analysis, highlighting input regions of high predictive relevance without relying on intermediate feature representations. By integrating these methods, this study ensures a comprehensive and interpretable understanding of the ensemble framework, aligning with the principles of transparency and accountability in AI systems.
## 6 Conclusions  
This paper proposes a novel modified hybrid integration method for the detection of moving weak targets in a DFRC OFDM system. The method achieves coherent integration within subfragments and incoherent integration across different fragments. Initially, the proposed method combines intra-frame and inter-frame integration within a single subfragment. Subsequently, leveraging the output characteristics of these subfragments, the GRT is applied between different subfragments. This paper provides detailed derivations and analyses of the proposed method across three key aspects: the integration output response, probability of detection, and communication performance. Finally, we have adapted traditional radar signal processing algorithms into OFDM IRCS signals and describe comprehensive numerical experiments to validate the theoretical derivations and analyses. The results demonstrate that, without increasing the computational load, the proposed method can effectively improve the detection probability, thereby enabling the better detection of weak maneuvering targets.  
Methodology, W.J.; Software, W.J.; Writing--original draft, W.J. and N.Z.; Writing--review & editing, W.J., T.L., Y.S. and H.Y.; Visualization, W.J.; Supervision, B.T.; Project administration, B.T.; Funding acquisition, B.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 13: Computational complexity of different methods.
## 4 Experimental Section
### Simulation Experiments
#### 4.2.3 Exploration Strategy Improvement  
Because of the unexpectedly low success rate in scenarios classified as difficult, this study improved the exploration strategy and introduced masking. Masking can effectively limit the size of the area being explored and reduce ineffective exploration along the edges of images. The focus can thus be placed on the exploration of objects and areas surrounding them, as illustrated in Figure 9.  
Improving the exploration strategy by introducing masking led to a rapid increase in the success rate, which was 80% after 600 steps, 1100 fewer steps compared with those required for the VPG system [13]. Therefore, the training was more rapid and the exploration time was less. The corresponding results are presented in Table 2.  
Table 3 compares the learning speed between the VPG method, normal mask use, and the \"OURS\" method. Learning speed is measured by the number of steps required to achieve an 80% success rate. The results show that the \"OURS\" method requires a much smaller number of steps, specifically 600 steps, to achieve this level of success, compared to 1700 steps for VPG and 1250 steps for normal masks.  
The faster learning speed in the \"OURS\" method indicates that the integration of custom masks in the DQN learning process significantly improves the learning efficiency. This shows that customized masks can help the DQN model learn more quickly and effectively, thereby enabling the robot to achieve a high success rate in a shorter time. Therefore, the table highlights the importance of using customized masks in improving the performance and learning efficiency in DQN applications for driving and understanding tasks on robotic arms. Pushing and grasping with a mask in the CoppeliaSim application is shown in Figure 10.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Method** & **Number of Steps Required to Obtain a Success Rate of 80\%** \\ \hline VPG & 1700 \\ Normal mask & 1250 \\ OURS & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of learning rate.  
Figure 9: Predicted pushes and grasps after masking.  
In this study, Deep Reinforcement Learning (DRL) is used to teach a robot to perform pushing and grasping tasks in a CoppeliaSim simulation. Image masks are used to detect and identify objects to be manipulated. Simulation environments such as CoppeliaSim are useful because they allow repeated testing under safe and flexible conditions, without the risk of hardware damage. Simulation accelerates the robot's learning process, helping researchers adjust the DRL algorithm more quickly and efficiently. Previous studies, such as [35; 36; 37], have also shown that simulation can accelerate development and reduce testing costs, as well as provide optimal results before being applied to physical robots in the real world.
###### Abstract  
Hand gesture-based sign language recognition is a prosperous application of human-computer interaction (HCI), where the deaf community, hard of hearing, and deaf family members communicate with the help of a computer device. To help the deaf community, this paper presents a non-touch sign word recognition system that translates the gesture of a sign word into text. However, the uncontrolled environment, perspective light diversity, and partial occlusion may greatly affect the reliability of hand gesture recognition. From this point of view, a hybrid segmentation technique including YCbCr and SkinMask segmentation is developed to identify the hand and extract the feature using the feature fusion of the convolutional neural network (CNN). YCbCr performs image conversion, binarization, erosion, and eventually filling the hole to obtain the segmented images. SkinMask images are obtained by matching the color of the hand. Finally, a multiclass SVM classifier is used to classify the hand gestures of a sign word. As a result, the sign of twenty common words is evaluated in real time, and the test results confirm that this system can not only obtain better-segmented images but also has a higher recognition rate than the conventional ones.  
Keywords:human-computer interaction; hybrid segmentation; sign word recognition; convolutional neural network (CNN); support vector machine (SVM) +
Footnote †: journal: Applied Sciences
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
## 1 Introduction
Therefore, in this study, an index called the ramping capability shortage probability (RSP) is used to quantify the individual effect of variability and uncertainty on flexibility. The RSP is able to consider not only the trade-off between the available amount of flexible resources and the operating cost, but also the major uncertainties including generator failure [29]. Based on the RSP, this study presents a method for separating the variability and uncertainty, and determining which is more influential in terms of flexibility. Various scenarios for variability and uncertainty based on a modified IEEE-RTS-96 are generated. In these scenarios, the net load variation and net load forecast error are used as parameters to represent the variability and uncertainty, respectively. The results of sensitivity analysis show that the variability (uncertainty) is more effective than the uncertainty (variability) for high and medium (low) net loads. The remainder of this paper is organized as follows. Section 2 introduces the RSP flexibility index, which is capable of quantifying flexibility according to variability and uncertainty. Section 3 explains scenario generation to compare the effect of variability and uncertainty on flexibility. In Section 4, a case study for a modified IEEE-RTS-96 is discussed. Conclusions and future work are discussed in Section 5.
## 3 MFCS Model
### Air Supply Subsystem  
The MFCS air supply subsystem is made up of an air filter, air compressor, hand valve, intercooler, common rail buffer, proportional valve, humidifier, and backpressure valve, with the functions of air supply and discharge, as shown in Figure 3.  
For the MFCS, the air compressor is different from an SFCS and needs to satisfy the demands of three stacks at the same time. In previous studies [20], the advantages and disadvantages of several types of air compressors for SFCSs and MFCSs were compared, and a reciprocating piston air compressor was selected in this paper. The function of the intercooler is to cool the high-temperature air that has been compressed by the air compressor so that it is within a reasonable temperature range for the electrochemical reaction.  
The common rail buffer is inspired by the common rail technology for the engines of fuel vehicles [21] and is designed for the MFCS air supply subsystem under the integrated optimized stacking method to provide the case of one device supplying multiple fuel cell stacks, which can use one set of pressurized air supply equipment and an intercooler to complete the supply of air to the cathode side of multiple fuel cell stacks. In addition to solving the problem of air supply for one set of multiple fuel cell stacks, the common rail buffer is designed to store a certain amount of air to supplement the air supply in case of an air supply shortage from the air compressor or to serve as a buffer in case of a large variable load.  
For high-power PEM fuel cell systems, the cathode exhaust gas usually contains a large amount of water vapor, which can be recycled for thermal mass recovery and humidification of the cathode into the stack air. A humidifier model is established considering the characteristics of the high-power output of the MFCS, and a gas-gas humidifier is selected as the air humidification equipment.
# Thermal Control of CubeSat Electronics Using Thermoelectrics
###### Abstract  
A feasibility study is presented exploring the possibility of using thermoelectric devices for the thermal control of CubeSat on-board electronics. A simple thermoelectric architecture is devised and an empirical model for how such a system would perform is constructed, using the performance data of a commercially available thermoelectric module. This is used to calculate the temperature to which the system could cool a computer chip, as a function of thermal resistance and heat rejection. As a baseline scenario, the temperature of the system without the thermoelectric device is compared and the benefit, or otherwise, of using a thermoelectric module is calculated. Analysis shows that in some circumstances introducing a thermoelectric device would actually increase the temperature of the electronics being cooled. This is most common when the quantity of heat being removed, or the thermal resistance of the system, is high. Nevertheless, thermoelectric cooling is beneficial for a range of conditions, such as for cooling the computer chip below ambient temperature, however a good quality radiator is required. This constraint could undermine the thermoelectric device's potential benefit in many cases, due to the need for an unrealistically large radiator.  
thermoelectrics; thermal control; Cubesat +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: email: {[[https://doi.org/10.3390/app13116480](https://doi.org/10.3390/app13116480)]([https://doi.org/10.3390/app13116480](https://doi.org/10.3390/app13116480))}
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 2 Related Work
### Dark Channel Prior for Fog Removal and Generation  
The Dark Channel Prior (DCP), introduced by [PERSON] et al. [7], is a seminal method in fog removal that exploits the observation that, in most non-sky patches of a clear image, at least one color channel has a very low intensity. Beyond fog removal, the DCP has been adapted for fog generation by reversing the process [7], producing realistic fog effects, particularly in scenes with strong depth cues.  
In contrast to these methods, our approach integrates a high-performance dehazing network (GridNet) directly into the fog generation process. This integration enables the generation of synthetic fog that closely reflects the degradation characteristics targeted by modern dehazing networks. Additionally, our method employs perceptual and dark channel consistency losses during training--techniques rarely explored in prior fog synthesis studies--resulting in improved realism and downstream restoration performance.  
The reviewed methods--GridNet [8], Simple Fog Generation [17], Depth-Aware Fog Generation [18], and Dark Channel Prior [7]--represent a spectrum of approaches to fog removal and generation. A notable research gap exists in leveraging high-performance dehazing networks for fog generation [16], which could unify the strengths of these approaches.
## 5 Discussion
### The Importance of Remote Sensing in Large-Scale NSP Intensity Mapping
#### 5.2.1 Crop Spatial Information Mapping in Large-Scale Area from Remote Sensing  
Remote sensing technology has made great achievements in large-scale crop information recognition. NSP intensity estimation depends on large-scale remote sensing data. In all remote sensing models, the index method is widely used in ground object identification because the approach is simple, with fast processing speeds and high accuracy [72]. We identified cultivated land by constructing various indices into decision tree. We determined the segmentation threshold of each index with a large number of samples, thus making a reliable distribution map of cultivated land. This method can be extended to other areas, because the study area in this paper covers the vast majority of ground objects. The vegetation index thresholds should be determined based on local conditions. We used the second difference method [59] to detect the peak of NDVI time series images to map the distribution of multiple cropping areas. Before peak detection, it was necessary to have a preliminary understanding of the crops in the study area, as well as a general understanding of the area's crop types and cultivation modes. For example, maize is the main crop in our study area, and two cultivation modes are used: multiple cropping and single cropping. Winter crops grow best in April and May, while summer crops grow best in August. Therefore, using these critical time nodes to constrain the time when the peak occurs can reduce the misclassification of multiple crops. When this method applied to other regions, it is only necessary to obtain the phenology of general crops and multiple crops in the research area, then obtain the key phenological nodes of vigorous crop growth to identify multiple crops. As shown in Table 6, the classification results of this method are generally reliable. For the study of cultivated land quality, the NDVI [73], Enhanced Vegetation Index [74], and GPP [51] are used in remote sensing mapping of crop growth. Among them, the GPP reflects the total amount or fixed total inorganic energy that plants synthesize into organic matter per unit time and per unit area, which is more suitable for crop growth monitoring. This experiment focuses on the remote sensing the identification of crop types, multiple cropping, and growth information and uses them as parameters to modify the existing NSP intensity model. The existing remote sensing technology is mature enough to obtain larger-scale geographic data, and this paper only takes the catchment area of the Yuecheng Reservoir as a case study. The method mentioned in this paper can be applied to larger scale regions.
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 3 Network Model and Design Requirements
### Design Requirements
#### 3.3.1 Load Balance  
Load balance in WLANs is different from that of the cable distributed system, which is mainly embodied to control AP association and handoff, and makes the load balanced among APs [20]. In the IEEE 802.11 standard, the conventional AP association scheme based on RSSI often leads STAs to make associations with congested APs, while leaving adjacent APs to carry very light load or even to be idle, which results in significant bandwidth waste [13]. Thus, some load balance algorithms are often utilized to improve the AP utilization. Therefore, we define an average AP utilization to evaluate the load balance, which is expressed as the following:  
\[\alpha=\frac{\sum_{j=1}^{N}\left(\sum_{i=1}^{M}x_{ij}t_{ij}\right)/T_{j}}{N} \tag{7}\]  
where, \(N\) is the number of APs, \(M\) is the number of STAs, the coefficient \(x_{ij}\) that is a binary variable, indicates the association relationship between STA \(i\) and AP \(j\), \(t_{ij}\) denotes that the transmission time of client \(i\) obtained in fact from AP \(j\) and \(T_{j}\) is the total allocated transmission time of AP \(j\).
# Modeling of Transmission Compliance and Hysteresis Considering Degradation in a Harmonic Drive
## Abbreviations  
\begin{tabular}{l l} \(N\) & reduction Ratio \\ \(Z_{CS}\), \(Z_{FS}\) & teeth number of CS and FS \\ \(\theta_{e}\) & kinematic error \\ \(\Delta\theta\) & harmonic drive torsional angle \\ \(\theta_{WG}\), \(\theta_{CS}\), \(\theta_{FS}\) & angular position of WG, CS, and FS \\ \(\Delta\theta_{W}\), \(\Delta\theta_{T}\), \(\Delta\theta_{F}\) & torsional angle of WG, FS teeth, and FS cup \\ \(\Delta\theta_{w}\), \(\Delta\theta_{d}\), & harmonic drive torsional angle for increasing and decreasing load \\ \(\Delta\theta_{wj}\), \(\Delta\theta_{dj}\) & estimated hysteresis torsional angle \\ \(\Delta\theta_{Wu}\), \(\Delta\theta_{Wd}\), & wave generator torsional angle for increasing and decreasing load \\ \(T_{WG}\), \(T_{CS}\), \(T_{FS}\) & torque of WG, CS, and FS \\ \(T_{1}\), \(T_{2}\) & torque given by the manufacturer \\ \(T_{f}\) & friction torque \\ \(T_{0}\) (\(T_{0}\)\({}^{\prime}\)) & no-load preload \\ \(T_{i}\) (\(T_{i}\)\({}^{\prime}\)) & piecewise linear threshold \\ \(K_{T}\) (\(K^{\prime}_{T}\)) & combined stiffness of meshing tooth pairs \\ \(T_{T}\) (\(T_{T}\)\({}^{\prime}\)) & piecewise linear threshold \\ \(K_{FL}\) & local Elastic Coefficient \\ \(K_{F}\), \(K_{T}\), \(K_{W}\) & stiffness of FS Cup, FS Teeth, and WG \\ \(K_{1}\), \(K_{2}\), \(K_{3}\) & stiffness Given by the Manufacturer \\ \(F_{s}\) & static friction \\ \(F_{c}\) & minimum value of the Coulomb friction \\ \(F_{v}\) & load parameter \\ \(\Omega\) & lubricant parameter \\ \(A_{i}\) & amplitude for a particular cyclical frequency \\ \(\Phi_{i}\) & phase angle \\ \(\omega_{i}\) & frequency \\ \(a\), \(b(a^{\prime}\), \(b^{\prime})\) & coefficient of combined stiffness \\ \(c\) (\(c^{\prime}\)) & coefficient of no-load preload \\ \(\alpha\) & gear-tooth angle \\ \(\delta\) & additional empirical parameter \\ \(\mu_{c}\), \(\mu_{l}\) & boundary and hydrodynamic friction coefficient \\ \(\mu\) & friction coefficient \\ \(W_{l}\), \(W_{a}\) & load carried by the fluid and asperity \\ \(\lambda_{1}\), \(\lambda_{2}\) & fluid and asperity load ratio \\ \end{tabular}
## 3 DDoS Attack Detection Methods
### DDoS Attack Detection Studies Using Anomaly-Based Methods
#### 3.3.6 Machine Learning-Based Detection Methods
[160] proposed a machine learning framework for IFA attack detection in NDN. In this study, IFA was modelled and simulated to gather attack features. The most prominent features were selected based on information gain-based ranking; thereafter, DT, J48, and MLP with backpropagation machine learning classifiers were used for IFA detection. According to experimental data, MLP with BP is more appropriate in terms of identifying and mitigating IFA, while the J48 classifier works better for large network topologies. In [161], the authors used an SVM classifier to characterize the entropy of interest names, the satisfaction ratio, and the PIT usage of interfaces that are continuously acquired from a router. The Jensen-Shannon divergence was used to extract malicious prefixes, and an IFA activity was notified when anomalies were found. When the SVM was applied without the Jensen-Shannon divergence, a high misjudgment rate was seen. However, with the inclusion of this entropy scheme, experimental results revealed that the approach achieved high accuracy. The fact that this study can only identify one kind of attack in NDN poses a significant constraint. Other attack types cannot be identified and require further development. As with the prior solution [160], the detection process may consume a lot of resources. In [162], the authors set up sample sets with various detection granularities to improve detection accuracy using an RF classifier. Experimental results show that the scheme could detect IFA attacks with a high detection rate. It was possible to attain detection probabilities of 97.5% and false negative probabilities of 1.2%. Additionally, some error cases with a 3% error rate were noted. Table 8 provides a summary of the research papers and thus far in the application of machine learning models for attack detection. These studies were compared based on the method employed, the dataset, and the application domain. A summary of the evaluation findings from each study is also provided. As shown in Table 8, a number of studies have discussed the use of machine learning for detecting network anomalies, with varying levels of accuracy and false alarms.