esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Parameters Setting for Different Algorithms  
As detailed in Section 2, the optimization ability does not rely on any parameter settings in HALO, thus it is prevented from becoming trapped in the local optimal solution to a great extent. In addition, some commonly used swarm intelligence algorithms, such as PSO algorithm [40], DE algorithm [41], CS algorithm [42] and gray wolf optimizer (GWO) [43], were used for an intuition comparison between the optimization abilities. All of these algorithms are based on hybrid decimal and binary coding. Moreover, other types of Gabor filter were used for further comparison. For a relatively fair comparison, the number of function evaluations was used as the terminal criterion; that is, all algorithms stopped when the iteration number reached 20 combined with part of experimental results as the fitness value  
Figure 4: Samples of the 30 categories randomly selected from the Brodatz database.  
Figure 3: Samples of the 20 categories randomly selected from the Kylberg database.  
did not improve, and all algorithms performed 30 independent operations. Our primary interest was the integrated optimization of parameters and features of Gabor filter, and this was shown by the fitness value of the objective function and the classification accuracy for the testing samples in each database. Table 1 shows the parameter settings of the above algorithms.
# Dirichlet Process Prior for Student's t Graph Variational Autoencoders
## 5 Experiments
### Link Prediction
After adding a DP-St prior, the various information can be screened, and those useless features that have a negative impact on the target task can be discarded to produce appropriate latent representation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**8 Dimensions**} & \multicolumn{2}{c}{**16 Dimensions**} & \multicolumn{2}{c}{**32 Dimensions**} & \multicolumn{2}{c}{**64 Dimensions**} \\ \cline{2-7}  & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** \\ \hline GVAE & 92.7 & 92.9 & 94.4 & 94.7 & 95.6 & 95.6 & 96.5 & 96.5 \\ St GVAE & 89.9 & 87.8 & 97.4 & 97.3 & 97.8 & 97.5 & 97.8 & 97.6 \\ DP-St GVAE & 96.4 & 97.1 & 97.1 & 97.0 & 97.3 & 97.4 & 97.9 & 97.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Link prediction performance comparison of different dimensions for PubMed. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**8 Dimensions**} & \multicolumn{2}{c}{**16 Dimensions**} & \multicolumn{2}{c}{**32 Dimensions**} & \multicolumn{2}{c}{**64 Dimensions**} \\ \cline{2-9}  & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** \\ \hline GVAE & 91.0 & 91.1 & 91.4 & 92.6 & 92.7 & 93.6 & 93.3 & 94.4 \\ St GVAE & 85.5 & 83.4 & 91.0 & 89.9 & 92.1 & 89.7 & 95.0 & 95.2 \\ DP-St GVAE & 92.3 & 93.9 & 94.1 & 95.1 & 94.4 & 95.5 & 93.5 & 94.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Link prediction performance comparison of different dimensions for Cora. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**8 Dimensions**} & \multicolumn{2}{c}{**16 Dimensions**} & \multicolumn{2}{c}{**32 Dimensions**} & \multicolumn{2}{c}{**64 Dimensions**} \\ \cline{2-9}  & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** \\ \hline GVAE & 89.5 & 90.5 & 90.8 & 92.0 & 91.0 & 92.1 & 91.1 & 92.5 \\ St GVAE & 80.6 & 80.0 & 90.9 & 89.9 & 95.5 & 95.0 & 95.5 & 96.1 \\ DP-St GVAE & 95.5 & 96.2 & 96.9 & 97.2 & 97.3 & 97.6 & 96.9 & 97.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Link prediction performance comparison of different dimensions for Citeseer. Figure 4: Latent representation visualization for Cora dataset. The horizontal axis is the dimension of the hidden variable, and the vertical axis is the number of nodes. (**a**) the latent variable learned by GVAE. (**b**) the latent variable learned by DP-St GVAE. The darker the higher probability nodes possess the latent features.
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 3 Method
### Phase 1--Definition of the \"What's\"
#### 4.1.2 Acceptance of a Bundle of Technologies  
Empirical works grouped under this category measured the acceptance of a set of available technologies and practices analyzed as a whole and without making specific distinctions of any nature. For instance, [PERSON] et al. [PERSON] et al. (2019) recognized the complementarity existing among sustainable agricultural practices and studied the adoption and use of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Defined Categories** & **Number of** \\
**Studies** & **Studies** \\ \hline Technology acceptance: theoretical models & 7 \\ Acceptance of a bundle of technologies & 6 \\ Acceptance of a methodology & 6 \\ Acceptance of a device & 8 \\ Acceptance of a service/platform/system & 30 \\ Comparison of the acceptance among different tasks or technologies & 3 \\ Technology acceptance: the effect of certain moderating variables & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Identified research categories.  
Integrated Production technologies by Brazilian common beans' farmers. In an attempt to measure the potential acceptability of smart farm technologies for guava farmers in Thailand, [PERSON] and [PERSON] [47] developed and validated an extension of TAM. Analyzing these technologies as a bunch was fundamental to determine the impact of their implementation in the farming supply chain. [PERSON] and [PERSON] [48] explored the experts, i.e., personnel and consultants' intention to use precision agricultural technologies (PA). Since PA is defined as a combination of information and communication technologies allowing the technical and environmental optimization of resources in agriculture [49], we classified this study belonging to this category. Another bundle of techniques is that consisting of the available tools for food manufacturing. The paramount role of the food processing sector to overcome economic development challenges of the increasingly growing world population aroused the interest of [PERSON] et al. [50] whose study focuses on the acceptance of food processing technologies by micro and small entrepreneurs in Kenya. A recent work by [PERSON] et al. [51] assessed the adoption of Integrated Pest Management (IPM) practices by Iranian tomato growers. In the context of the study, PEST practices include cultural, mechanical, chemical, and biological strategies to reduce and refrain the negative impact of plant pests, improving farm sustainability [52]. As for the e-learning sector, [PERSON] et al. [53] applied the TAM to investigate the ultimate effect of its constructs on the acceptance of assistive technologies by special education teachers. According to the definition provided by [PERSON] and [PERSON] [54], assistive technology covers any device or system that makes possible the execution of a task safely and facilitates the performance, impossible to obtain otherwise.
## 3 Description of Research Methods
### Adversarial Examples
#### 3.2.1 Threat Model  
Adversarial attacks can primarily be categorized into two types based on their generation methods: gradient-based attacks and optimization-based attacks [32].  
**Gradient-Based Attacks**: These attacks exploit the gradient information of the neural network's loss function to generate adversarial examples. Such methods typically compute the gradient of the loss with respect to the input data to craft the adversarial perturbations. The main idea is to adjust the original input in the direction that maximizes the model's loss, making the model more likely to misclassify the perturbed input. Examples of gradient-based attacks include FGSM, BIM, and PGD.  
**Optimization-Based Attacks**: These attacks involve solving an optimization problem to find the smallest perturbation that can lead to misclassification. Such methods do not directly rely on the gradients of the neural network's loss function but focus on other optimization criteria to craft adversarial examples. The optimization process is generally more computationally intensive than gradient-based methods but can produce highly effective adversarial examples. The C&W attack and Deepfool algorithms are typical examples of optimization-based attacks.  
In this work, we focus on the white-box attack scenario. In the white-box model, attackers have access to the complete structure and parameter information of the target model. They can directly generate adversarial examples on the target neural network, compute the true or approximated gradients of the real model, and adjust their attack methods based on the defense mechanisms and parameters they encounter.
## 2 Literature Review
Second, a blind algorithm was used to extract the watermark image; third, the hologram was used to improve the invisibility of the watermark, provide the best performance against noise attacks, and perform encryption. A proposed watermarking system that ensured all the requirements simultaneously was proposed in [43]. Different researchers that were dependent on blockchain for digital image watermarking to prove ownership [44; 45; 46] could not rely on a relational database because it was different from another database format, such as the image format. Another algorithm that used blockchain to detect tampering in the database was presented in [47]. Another algorithm that used blockchain for the digital watermarking of big data was presented in [48]. Particle swarm optimization, gray wolf optimization, genetic algorithms, firefly algorithms, bat optimization, the artificial bee colony, and ant colony optimization are a few examples of nature-inspired algorithms that employ swarm intelligence ideas to achieve optimal outcomes. To get the best possible results, several authors have turned to these methods. In [49], a novel and optimal function was introduced that was secure against a wide range of attacks while retaining the desired level of quality. In this method, the embedding factor was determined by using the PSNR and bit correction rate (BCR), and optimization algorithms were utilized to perform the calculations. Numerous hybrid methods, including the use of various algorithms inspired by nature, have been employed by researchers to successfully embed watermarks. The watermarking of color photographs with a combination of the bat method and the firefly algorithm was presented in [50]. A second DCT-based watermarking system was described in [51], which employed the PSO algorithm to find the optimal strength factor value. In [52], the author proposed a watermarking system that used a special variant of the Whale algorithm. To determine the multiscale factor, this approach employed DWT-SVD-based watermarking with a twist: modified whale optimization. In [53], the watermark embedding position is determined by using the firefly method. In [54], an optimization strategy based on the ABC algorithm was used to incorporate the watermark using the IWT and SVD. In [55], the notion of an artificial bee colony was proposed; this system was based on the intelligence of real honey bees. The dipper-throated optimization (DTO) and stochastic fractal search (SFS) optimization methods were proven to perform better than many other optimization algorithms. Still, these algorithms have not been addressed for the problem of watermarking [56; 57]. Therefore, these methods were adopted in developing the new algorithm proposed in this paper in order to improve the performance of a standard watermarking method to preserve the security and privacy of medical images while being transmitted over networks.
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 1 Introduction
### Motivation  
[PERSON] et al. [3] used six ML techniques as reference benchmarks to assess their effectiveness in identifying Python code smells. They examined the baseline results using the Matthews correlation coefficient (MCC) and accuracy criteria. However, their work did not consider ensemble learning or the impact of preprocessing techniques like feature selection and class balancing.  
This work aims to enhance the results using five ensemble learning approaches, including the FST on the Python code smell dataset. Ensemble learning techniques enable the amalgamation of predictions generated by numerous ML models, possibly producing more precise and resilient outcomes. Six performance measurements were conducted, and the issue of class imbalance was resolved by using the SMOTE class balancing approach. Subsequently, a comparison was made between the performance of the system with and without FST and SMOTE.
# Forklift Tracking: Industry 4.0 Implementation in Large-Scale Warehouses through UWB Sensor Fusion
## 3 Numerical Analysis
### Effect of Initial Uncertainty  
Tracking algorithms should be robust with respect to errors in the filter initialization. Positioning systems as the UWB systems are not affected by this problem. Dead-reckoning, instead, is invalidated by large initialization error. A sensor fusion algorithm based on sequential estimators such as the UKF, should converge even if an initial error on the dynamic state estimation is present. For this purpose, we performed an analysis of the method performance when the matrix \(\mathbf{P_{0}}\) changes. We considered different values of \(\sigma_{\text{UWB}}\)  
Figure 6: Histogram of the orientation error \(\epsilon_{\theta}\) for \(M=100\) test cases with similar trajectories as the one represented in Figure 3.  
Figure 7: Mean distance error \(\tilde{\epsilon}_{d}\) as a function of the forklift speed \(v\). For each value of \(v\), 100 test cases were conducted with similar trajectories as the one represented in Figure 3.  
and \(\sigma_{\theta}\) to perform the method with 11 different configurations of the initial error standard deviation. The values of the changed parameters in each configuration are shown in Table 1. For each configuration, we ran the UKF 100 times and we observed the mean distance error \(\tilde{\epsilon}_{d}\).  
The results are depicted in Figure 8, for a forklift speed of \(v=0.75\) m/s. As it can be noticed, the UKF performance is not influenced by the initial error uncertainty because the mean localization error does not change with different initial conditions.
## 2 Literature Review
### Feature Extraction  
In computer vision, images are often described as vectors or descriptors that represent the task-specific, distinguishable characteristics of an image. These vectors can be extracted by a variety of different algorithms. In the past, features were extracted manually based on the task. Later, computer vision approaches enabled the extraction of general features such as SIFT [60], SURF [61], or ORB [62]. Nowadays, as deep learning is popular in computer vision domain, convolutional neural networks (CNN) are trained for the identification and feature extraction of specific objects, such as buildings [63; 64]. These architectures are typically designed for image classification but can also be adapted for feature extraction. They often have a high-dimensional vector as output which can be reduced to the desired dimension using dimensionality reduction techniques such as a principal component analysis (PCA) [65]. Some models are trained on images of landmarks and buildings to detect distinguishable features between buildings. A currently popular CNN model for feature extraction was published by [PERSON] et al. [64] under the name Citroch. It is a fine-tuned ResNet101 [66] designed for feature extraction from images on which buildings and landmarks are mapped. This model extracts one vector per image, a global descriptor. Other approaches extract the most important keypoints of an image and describe these points in separate vectors, the local descriptors.
## 2 Materials and Methods
### Comparative Model Analysis  
This section evaluated both feature-based and end-to-end models under various noise conditions to determine which approach was more suitable for deployment in noisy, real-world environments. The performance of each model was rigorously tested across a spectrum of simulated noise conditions, with metrics such as accuracy, precision, recall, and F1-score calculated to assess efficacy. The comparative analysis highlighted models that not only performed well across different noise levels but also demonstrated significant resilience to environmental disturbances. Based on these findings, specific models were recommended for practical application in anxiety detection systems, paving the way for robust, real-world deployments.
## 4 Result and Discussion
### Loss Function Based Optimization for Imbalanced Dataset  
In this experiment, the multiclass focal loss was used in the sensor classification task to replace the conventional multiclass loss method, namely cross-entropy loss. The hyperparameter gamma acts as a relaxation parameter that is used to adjust the weight for each class. When the gamma value is high, the model focuses on optimization of the minority classes. The learning process is more difficult, and it is harder to correctly classify all the classes. By using this approach, the model can be trained directly with the imbalanced distribution dataset without the need for random undersampling.  
This approach was tested with five IMU sensor datasets, as mentioned in Section 4.4. By setting the gamma value to 1.0, both models achieved the best accuracy score of 0.9 and F1-score of 0.9. Although the model using multiclass focal loss was successful in classifying the minority classes, the result was no better than the random undersampling approach. The result of the multiclass focal loss is shown in Table 4.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 4 Forecasting of Chaotic Time Series in Various Applications
### Power and Energy
#### 4.1.2 Solar and Photovoltaic Systems  
The recurrent predictor neural network model presented in [36] is based on an extended algorithm of self-adaptive BP through a time learning algorithm for predicting the annual sunspot time series in Skylab. Similarly, the time-delay neural network model [48] and the multi-layered neural network-based co-evolutionary algorithm [52] are used for predicting the annual sunspot time series of the space laboratory launched by the USA in 1973 and the sunspot index data center in Belgium, respectively. The ANN-based discrete transform using chaos theory [130], ensemble EMD based on optimized chaotic phase space reconstruction [155], SSA tuned CNN [158], and CGO [172] are used for predicting the power, voltage, and current of PV system in Beni Mellal, Morocco, St Lucia campus PV station, Australia. The k-fold cross-validation with GRNN reported in [134] is used for predicting the accuracy of sunspot under different embedding dimensions for phase space reconstruction of chaotic time series according to the Takens theorem in the Solar Influences Data Analysis Center, Belgium.
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 2 Knowledge Graph Key Technologies
### Knowledge Extraction
#### 2.1.1 Entity Extraction  
Entity extraction is the first step of knowledge extraction and refers to the extraction of entity information elements from unstructured data.  
Traditional knowledge extraction methods utilize a fully or partially labeled corpus for training statistical models, such as hidden Markov models, conditional Markov models, maximum entropy models, and conditional random field models. Currently, neural network learning based on named entity recognition (NER) has shown excellent performance in entity extraction, mainly for convolutional neural networks (CNN) [26], recurrent neural networks (RNN) [27], and neural networks that introduce attention mechanisms [28]. Among them, the LSTM-CRF model connects the words in a sentence into word vectors for output [29]. The long short term memory-convolutional neural networks-conditional random field (LSTM-CNNs-CRF) model [30] adds a CNN model in the embedding layer that can fully extract semantic features in combination with the context. The CNN model obtains the character-level vectors of each word connected with the word vectors and then obtains the annotation results through the LSTM and CRF layers [31]. The attention mechanism-based neural network model proposes a combination method of word vectors and character-level vectors. Such a combination enables the model to dynamically determine the importance of word vectors and character-level vectors of each word in the final features, again improving recognition accuracy [28].
## 4 Discussion
### Heart Rate Comparison between the Gown and Coverall Protocols
#### 4.3.1 Comfort Perceptions  
For most participants, the higher the sense of comfort in the gown protocol, the lower it was in the coverall protocol. Studies show that the use of the coverall protocol makes it difficult for medical personnel to communicate with each other. Two major reasons for this are visual limitations caused by condensation that accumulates on the face shield and hearing impairment because the overall covers up the ears [8; 9; 17; 18]. Other studies have shown that wearing the coverall may result in excessive sweating and fluid loss during use [8; 9]. In addition, other studies have shown that, even though the use of protective equipment that covers more body parts does offer optimal protection, there is also morediscomfort during use and even difficulty in removing the equipment [19]. In addition, because the gown protocol does not cover the back of the head, it was hypothesized that this protocol would be rated as more comfortable to use compared to the overall protocol.  
We found that, for longer time differences between the two protocols, the comfort perception difference value was smaller. Only six participants (about 9%) rated the coverall protocol as more comfortable compared to the gown protocol, and the rating difference for these participants was very low (0-2). The literature regarding the comfort of using the coverall protocol compared to the gown protocol is also consistent with the results of the current study, according to which use of the coverall protocol requires a longer duration compared to the gown protocol.  
Greater comfort perception in the gown protocol was correlated with more difficulty in providing medical care using the coverall protocol. This result is consistent with the existing knowledge in the literature that a higher comfort perception reduces difficulty in providing medical treatment [20].  
However, it is difficult to draw conclusions based on the relationship found between the variables of comfort perception and safety perception since the correlation coefficient (r) was very low (r = 0.293). This indicates a relationship that is indeed significant but, in practice, is very weak.
## 5 Result and Discussion  
Using FFT frequency analysis, we demonstrated the ability of the U-Net model to generate RTI brushstroke images. Next, we quantitatively reported the suitability of the trained PatchGAN as a classifier model, comparing it to the ImageGAN and PixelGAN models, that learned the parameters to discriminate the original RTI brushstroke style from forgeries. All the results are based on experiments conducted on a test dataset composed of original painting A, as well as counterfeit paintings B and C.