esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 2 Materials and Methods
### Diseases and Pests Multi-Source Image Dataset of Apple Canopy
#### 2.1.2 Construction of Multi-Source Image Dataset of Apple Diseases and Pests Canopy  
To optimize the canopy-scale apple diseases and pests regional classification model, a self-built multi-source image pest dataset based on the RGB and multispectral canopy images taken by UAV was constructed. After that, the original image was divided into area images that had the same size, and each area image had multiple tags, which respectively indicated that the area was suffering from one or more types of apple diseases and pests, healthy, or had a soil background. The specific image preprocessing and labeling process is shown in Figure 2.  
The original image needed radiation correction and geometric correction beforehand to eliminate the remote sensing imaging error caused by environmental factors and was then spliced to obtain a multispectral reflectance map. Angle correction and channel splicing were also performed on the reflectance map. Then, manual annotation was performed on each area image. Firstly, the entire orchard was divided into a grid-like coarse-grained area shown in Figure 2, and the image was cut into an image block with a size of \(256\times 256\) pixels. Secondly, under the guidance of experts in the field of plant protection, each individual fruit tree was identified as the smallest unit to determine which type or types of diseases and pests it was suffering from, or whether it was healthy, and these were recorded. Based on this information, multi-label annotation was performed on the regional images. Finally, a total of 1901 area images were obtained, including 5 common apple diseases and pests. As shown in Figure 3, they are Aphids, Alternaria, mosaic, brown spot, and gray spot. Furthermore, it was necessary to clean the original dataset to eliminate the impact of outliers on the training effect. Traditional image data augmentation techniques such as flipping, rotation, sharpening, and whitening were adopted to simulate the possible shooting effects encountered in reality, enhance the robustness of the model, and prevent overfitting during the model training process. Finally, the dataset was divided into a training set and testing set in a ratio of 8:2, and the multi-source apple canopy image dataset of apple diseases and pests with multi-label information was constructed. The total number of RGB and multispectral images in the augmented data is 28,515, and the distribution information is shown in Table 2.  
Figure 3: Five common apple diseases and pests images.  
Figure 2: Image segmentation method and manual labeling process.
## 2 RIS-Assisted System and Channel Model  
In this section, the RIS-assisted system, channel modeling, and selected precoder structure are described.
## 1 Introduction
#### Blockchains Transforming the Media Experience: The Example of Bloomen  
The increasing use of digital technologies for media content production, distribution and delivery has enabled the design of new solutions. Blockchain technology can provide a novel framework allowing privacy preservation, copyright protection and new models for direct compensation of certified content creators. An example of such an approach for disrupting the media industry with blockchain technology is the Bloomen project (European Union's Horizon 2020 research and innovation program under grant agreement No. 762091 Bloomen: Blockchains in the new era of participatory media experience. website: [[http://bloomen.io/](http://bloomen.io/)]([http://bloomen.io/](http://bloomen.io/))). The main goal of the Bloomen project is to extend the use of the blockchain technology to handle different online user transactions, providing an innovative way of content creation, sharing, personalized consumption, monetization and copysrighting. A key part of this approach is the KYC process, in order to link the cryptographic identities to real-world identities and to have access to a permissioned blockchain network.  
Identity management issues handled within Bloomen include both identity verification across different networks (outside and inside the blockchain) and identity management over closed silos systems and open public ledgers. Given the decentralized nature of the architecture proposed by the project, it addresses how to distribute and decentralize the identity management functionality, including the options for users and other third parties to set this functionality themselves, as opposed to depending on central systems administrators. Moreover, it addresses how to distribute the identity management functionality across the different levels of the Bloomen architecture (e.g., within the blockchain and the application level) and investigates how, with the complexity induced by the decentralization, identity management can be made to be universal, i.e., how it can extend across very dissimilar use cases. The context of the research work presented in this paper has been implemented in the frame of the Bloomen project as a way to implement a lightweight, efficient and decentralized KYC process on a Quorum blockchain in order to facilitate the provision of media applications.  
The rest of the paper is structured as follows: Section 2 presents a description of work related to blockchain technology, distributed applications and traditional KYC approaches. A detailed analysis of our proposed architecture and implementation details are provided in Section 3. Finally, Section 4 is devoted to discussion and conclusions of our work, while Section 5 presents future extensions of our system.
## 3 DC Circuit Breakers
### Solid-State Circuit Breakers
#### 3.3.2 Recent Developments of SSCBs
There are a number of 10 kV-class SiC switches that are currently at various stages of development at Cree, Inc., Durham, NC, USA [83]: (1) SiC MOSFETs, (2) SiC GTOsand hybridors, and (3) SiC IGBTs, including n-IGBTs and p-IGBTs of this family. Table 1 compares several physical properties of different semiconductors according to the type of material. It is observed that the electric breakdown field of Si is considerably smaller than that of GaN at SiC [84]. In the Ref. [85], a short cathode metal oxide semiconductor controlled thyristor (CS-MCT) was proposed in a 400 V SSCB, which achieved a 30% reduction in energy loss compared to using a Insulated Gate Bipolar transistor (IGBT). However, this kind of SSCB is only able to interrupt unidirectional fault currents and requires an additional DC source to pre-charge the commutating capacitor, thus increasing the circuit complexity and limiting its applications. In the Ref. [35], a novel SSCB which uses the CS-MCT was proposed for DCMG protection. The SSCB offers interruption capability before the main switch is turned on. When a fault takes place, the switch opens, bringing the current to zero in 2-3 \(\upmu\)s. The practical value and the feasibility of the SSCB were validated by a 600 V/15 kW prototype. Table 2 compares different switches. Note that the CS-MCT provides lower conduction loss than that of the IGBT, the SiC mosfet, and the thyristor. Compared with the IGBT or the SiC Mosfet, the CS-MCT and the thyristor can offer lower conduction losses and stronger tolerance to current and voltage surges, but need additional forced commutation technology. As a result, the breakers based on thyristors or the CS-MCT are attractive in terms of higher power efficiency and more robust fault isolation [35]. In the Ref. [86], it was indicated that thyristors are superior to IGBTs in terms of their rating, cost, drive circuit design, and reliability. Moreover, thyristor-based circuit breakers are a common approach to tackle the conduction losses of solid-state circuit breakers. In fact, thyristors constitute one of the best power electronic types of switches from the point of view of conduction losses, the rating, cost, symmetric blocking capability, and reliability aspects; however, this breaker needs additional commutating circuits.
# Machine-Learning-Based Rollover Risk Prediction for Autonomous Trucks: A Dynamic Stability Analysis
## 2 Methods
### Machine-Learning-Based Driving Simulation Scenarios
#### 2.3.1 Machine Learning Dataset Generation  
Data were collected by simulating DLC and SWD scenarios using _TruckSim_. Three speed conditions were tested:  
* Low-speed: 40 km/h;
* Medium-speed: 50-70 km/h (variable);
* High-speed: 80.5 km/h.  
To use road curvature and lateral acceleration as independent variables, the simulation data included vehicle speed and trajectory coordinates. The road curvature \(k\) was calculated as follows [10]:  
Figure 4: Track specifications for DLC test (m).  
Figure 5: Steering wheel angle for SWD test [16].  
Figure 3: _TruckSim_ Simulation: (a) DLC test, (b) SWD test.  
\[k=\frac{\left|\dot{x}(t)\ddot{u}(t)-\ddot{x}(t)\dot{u}(t)\right|}{\left(\left(\dot{x }(t)\right)^{2}+\left(\dot{y}(t)\right)^{2}\right)^{3/2}} \tag{2}\]  
where x and y are the longitudinal and lateral coordinates (m), and \(t\) is the time (s).  
Using the computed curvature and vehicle speed data, the lateral acceleration was determined as follows [10]:  
\[a_{y}=kV_{x} \tag{3}\]  
where \(k\) is the road curvature and \(V\) is the longitudinal speed (m/s). All input features were normalized using Min-Max scaling before model training.  
The lateral load transfer ratio (LTR) was selected as the dependent variable. The LTR index, representing the ratio of the vertical loads acting on the left and right sides of the vehicle, was computed using the vertical load values from each tire [14]:  
\[LTR=\frac{F_{L}-F_{R}}{F_{L}+F_{R}} \tag{4}\]  
where \(F_{L}\) is the vertical load on the left tire (N) and \(F_{R}\) is the vertical load on the right tire (N).
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Evaluation
#### 6.4.4 Ensemble Evaluation  
In the case of algorithm ensembles, their algorithms need to be assessed on an individual basis and in terms of their contribution to the portfolio. More specifically, most works differentiate between measuring group composition and group performance. The accuracy of single classifiers and classifier differences are crucial for increasing the ensemble's performance as a whole [5, 84, 126].  
Group composition can be measured by evaluating the ensemble's diversity and, thus, complementarity. Intuitively, the diversity of the ensemble measures the heterogeneity of its members, i.e., the benefit which can potentially be obtained by using several classifiers. High classifier heterogeneity leads to uncorrelated votes of the ensemble's members, increasing performance. Nevertheless, note here that no correlation between ensemble diversity and accuracy exists. It might happen that a highly diversified ensemble still performs poorly. The relation between diversity and accuracy in this setting is further discussed by [176].  
In many works, the ensemble diversity in the streaming scenario is measured using batch processing metrics [176, 177, 178]. For example, ref. [126] calculated standard diversity measures and visualized them over time to meet streaming scenario challenges. Standard measures are, for example, [PERSON]'s \(Q\) and the disagreement measure \(D\). Both take values in the range of \([-1,\,1]\). [PERSON]'s \(Q\) assesses whether the number of equal predictions of two ensemble members matches the number of divergent predictions. The measure of disagreement D is the ratio of the number of cases where one classifier is correct, and the other is incorrect relative to the total number of cases. Ref. [179] published diversity measures for streaming scenarios. He introduced two new trend diversity measures for data stream classification ensembles, the pair and pool error trend diversity measures. These diversity measures were developed based on the direction and magnitude of changes in the error trends of the base classifiers as they process subsequent samples and cope with stream changes.  
In the end, the ensemble's performance is measured with the same metrics as for the single classifier case. In their respective studies, for example, refs. [177, 178] used accuracy, the \(F1\)-score, and the prequential AU-ROC next to other standard evaluation measures to assess ensemble performance. A more detailed overview of these evaluation metrics is given in Section 6.4.2.
## 4 PM2.5 Prediction Modeling Based on Stacking  
### Ensemble Learning  
Ensemble learning is a powerful machine learning paradigm that enhances prediction accuracy and robustness by constructing and combining multiple learners. The fundamental concept is that the collective performance of multiple learners often surpasses that of any individual learner [23]. Key ensemble learning techniques include Bagging, Boosting, and  
Figure 5: Same as Figure 4, but for spring (**a**), summer (**b**), autumn (**c**) and winter (**d**). An asterisk (*) indicates the \(p\)-value is less than 0.05, and a double asterisk (**) indicates the \(p\)-value is less than 0.01.  
Stacking, which, through the integration of diverse models, effectively boost the overall performance and reliability of the system.  
We employ the Stacking ensemble learning method. Stacking is a multi-level ensemble learning technique that uses the predictions from multiple base learners as new features to train a meta-learner for final prediction. Stacking typically involves two stages: the first stage trains a set of base learners and obtains their predictions on the training set using cross-validation; the second stage uses these predictions as new features to train a meta-learner, such as linear regression or Random Forest, to learn how to optimally combine these predictions. Stacking leverages the complementarity of different learners by enabling the meta-learner to learn the best way to integrate their predictions, thereby enhancing the ensemble's performance. This method not only combines the strengths of different algorithms, enhancing model diversity and generalization capability, but also improves overall performance by learning the complex relationships between different model predictions. Furthermore, Stacking uses cross-validation predictions to train the second layer model, effectively reducing the risk of overfitting and further enhancing the model's stability and reliability.
## 3 Results
### Raman Spectrum of GaN Epilayer  
From Figure 3, it is clearly indicated that the scattering peak at 417 cm\({}^{-1}\) represents the typical characteristics of sapphire (Al\({}_{2}\)O\({}_{3}\)) and the five characteristic phonon scattering peaks symbolize the structure of wurtzite crystal GaN with the different buffer layers, which were A\({}_{1}\) (TO) phonon at 532 cm\({}^{-1}\), E\({}_{1}\) (TO) phonon at 557 cm\({}^{-1}\), E\({}_{2}\) (high) phonon at 568.2 cm\({}^{-1}\), A\({}_{1}\) (LO) phonon at 736 cm\({}^{-1}\) and E\({}_{1}\) (LO) phonon at 750 cm\({}^{-1}\) in the range between 300 and 800 cm\({}^{-1}\), respectively [25; 26; 27]. According to the selection rule, only the A\({}_{1}\) (LO) phonon and E\({}_{2}\) (high) phonon should appear in the Raman spectrum of c-plane GaN, but the crystal grains might be twisted during the epitaxy process of GaN, which caused lattice deformation or structural disorders to some extent, resulting in the loosening  
Figure 2: Raman spectra of graphene transferred on the sputtered AlN layer (red line) and MOCVD AlN (blue line) layer in the high-wavenumber region of 1200–2800 cm\({}^{-1}\). The D, G, and 2D peak positions of graphene are indicated.  
of the selection rule and the appearance of phonon modes that should be prohibited. Thus, both the Raman spectrum of samples 1 and 2 displayed a low intensity peak between 500 cm\({}^{-1}\) and 550 cm\({}^{-1}\) along with a shoulder in the E\({}_{2}\) (high) mode in Figure 3. We thought that the appearance of the A\({}_{1}\) (TO) phonon (532 cm\({}^{-1}\)) and E\({}_{1}\) (TO) phonon (557 cm\({}^{-1}\)) was interesting. We found clearly that the intensity of the E\({}_{2}\) (high) phonon became increasingly strong and that there was a shift to lower frequency with the introduction of the graphene layer between the GaN epitaxial layer and the sapphire substrate, which compared well with previous results [3], revealing that the quality of the GaN epilayer had been greatly improved with the help of graphene. Furthermore, it is also meaningful for us to investigate the phonon lifetimes of the A\({}_{1}\) (LO) phonon based on the principle of wave vector conservation using the A\({}_{1}\) (LO) phonon line widths [28]. Studying the relationship between phonon lifetime and FWHM of peaks was very important to characterize the degree of disorder within the GaN, and it was beneficial to use the Raman spectrum to characterize the properties of GaN materials in our subsequent research.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.2 Fault Management  
Fault management includes detection, identification and mitigation of any abnormal status of networks. Fault management in future 6G network needs to be effective, due to their heterogeneous, complex and dynamic nature. The authors in [77] compared five different unsupervised learning approaches (including K-means clustering, Fuzzy C-means clustering, Local Outlier Factor- LOF, Local Outlier Probabilities- LoOP and Kohonen's Self Organizing Maps-SOM) for fault detection in 6G networks. The results show that SOM-based approach outperforms Fuzzy C-means and K-means in detecting and predicting faults/abnormalities in 6G networks.  
In [78], an extension of the conventional K-Means clustering algorithm, named K-Aware K-means, is used for fault detection in 6G network systems. In this extended version of K-means, the model uses an unsupervised learning phase to acquire a temporary expert knowledge of what the smallest cluster of the current data is like and then labels them as outliers, while updating the temporary knowledge. In this way, the model self-optimizes the K value (K \(\leq\) 1). and achieves a prediction accuracy of 99.7%. The authors in [79] propose an unsupervised learning approach with a SOM algorithm as the centerpiece for both fault recognition and recovery, achieving great accuracy results.
## 8 Experimental Verification  
The experimental laboratory measurement has been carried out to further validate the practicability of the proposed design. For this purpose, the practical VCII- has been realized using commercially available IC-type AD844s, as shown in Figure 14[34]. The DC bias voltages were set to +V = \(-\)V = 5 V. The resistor and capacitor have values of \(R_{1}\) = 1 k\(\Omega\) and \(C_{1}\) = \(C_{A}\) = 1 nF, respectively, to obtain an ideal pole frequency of \(f_{0}\) = 159 kHz. Figure 15 shows the experimentally measured time and frequency responses for the proposed first-order VM inverse filter in Figure 2a along with the corresponding ideal responses. The input voltage was set at 100 mV peak-to-peak with a frequency of 159 kHz. The testing results thus verify the feasibility of the proposed concept. However, discrepancy between the measured responses and the theoretical predictions is mainly attributed to the tolerances of the passive components and the parasitic impedance effects of the AD844s used in hardware implementation. Note that for the AD844, the parasitic components are \(R_{x}\) = 50 \(\Omega\), \(R_{z}\) = 3 M\(\Omega\), and \(R_{w}\) = 15 \(\Omega\)[35].  
Figure 14: Practical realization of the VCII- using commercially available IC AD844s.  
Figure 13: Total harmonic distortion variations against peak input signal amplitude for the proposed inverse filters. (**a**) VM filters in Figure 2; (**b**) CM filters in Figure 3.
## 5 Simulation Results and Discussion  
The suggested HPSO-GWO algorithm is utilized for OPF solutions using MATLAB software. The simulation results are executed on a system with Intel (R) Core (TM) i5-4210U CPU @ 1.70 GHz 2.4 GHz with 4.0 GB RAM.  
Simulations are carried out for several cases of adopted test systems, in which an SPV and two WT are integrated. Simulation results for each case obtained with utilization of the HPSO-GWO algorithm are presented with necessary description in this section. In the first two cases, variation in generation costs for SPV and WT are examined with variation in respective schedule powers and PDF parameters. In the third case, reduction of total generation cost is achieved without considering carbon tax and valve point effects for thermal generators. In the fourth case, total generation costs with valve point effects for thermal generators are considered. The fifth case optimizes the total generation cost with valve point effects of thermal generators, and a carbon tax of 205/ton applied on emission. Each optimization algorithm is run five times, and a total of 500 iterations are executed in a whole run of the algorithm. During the complete run of each optimization algorithm for five times, the minimum cost of generation thus found and consequent control variable values are recorded.
# A New Approach for Deepfake Detection with the Choquet Fuzzy Integral
## 1 Introduction
The proposed approach, which was based on the Choquet fuzzy integral technique, outperformed the single classifiers, achieving the highest accuracy, 95.08% [16]. Another study focused on the detection of COVID-19 using the Choquet fuzzy integral method, including the InceptionV3, DenseNet121, and VGG19 models, achieving a test accuracy of 99.05%. These results were superior to those of commonly used classifier ensemble methods as well as many state-of-the-art techniques [15]. This study proposes a method based on the Choquet fuzzy integral to improve the overall accuracy of the classification of deepfake videos. The proposed approach can collect more meaningful prediction results from multiple classifiers using adaptive fuzzy measures. The rest of this paper is organized in the following way: In Section 2, the methods of creating deepfake videos and the datasets and detection examples that allow us to reach the solution are explained. Section 3 presents the model of the proposed method, the algorithms used, and the key elements. Section 4 includes the experimental results and comparisons with state-of-the-art models. Finally, discussions are included in Section 5, and the paper is concluded in Section 6.
## 4 Theoretical Estimations
### Evaluation of the Model  
The agreement of the model with the measured data was evaluated by determining the relative root mean squared error (\(MSE(\%)\) (Equation (12)) [47] and the Nash and Sutcliffe efficiency index (\(E\)) (Equation (13)) [48]:  
\[MSE(\%)=\frac{100}{\overline{Y}}\sqrt{\frac{\sum_{i=1}^{n}(Y_{i}-S_{i})^{2} }{n}} \tag{12}\]  
\[E=1-\frac{\sum_{i=1}^{n}(Y_{i}-S_{i})^{2}}{\sum_{i=1}^{n}(Y_{i}-\overline{Y}) ^{2}} \tag{13}\]  
where \(n\) is the number of experimental data, \(Y\) is the average of experimentally measured values, \(S_{i}\) are the theoretical values produced by the model, and \(Y_{i}\) are experimentally measured values.