esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 4 Experiments and Results
### Experimental Results
#### 4.3.4 Comparative Results of Relevant Studies  
In this paper, the BiGRU and CNN models were used with dual-emotion features, and the data show that dual emotions can better enhance the performance of the rumor detector. However, the results of the two models alone cannot comprehensively summarize the characteristics of dual emotions. Therefore, this section will refer to the previous experimental results on the dataset of Weibo as a supplement. The detection data for DTRank, SVMRBF, and DTC initial models are compared with the results of the two models introduced in this paper. DTRank, SVM-RBF, DTC, and BiGRU+ models have their own advantages and characteristics in the field of rumor detection. Choosing these models as baseline models enables a comprehensive evaluation of the performance of the newly proposed rumor detection models and promotes the continuous development and innovation of rumor detection technology.  
Table 6 evaluates the effectiveness of the detectors from four aspects: accuracy, precision, recall and F1 value.  
Figure 10: Detection results of BiGRU model on Weibo-16 and Weibo-16 (t).  
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Tag** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{DTRank} & F & \multirow{3}{*}{0.732} & 0.738 & 0.715 & 0.726 \\ \cline{2-5}  & T & & 0.726 & 0.749 & 0.737 \\ \hline \multirow{3}{*}{SVMRBF} & F & \multirow{3}{*}{0.818} & 0.822 & 0.812 & 0.817 \\ \cline{2-5}  & T & & 0.815 & 0.824 & 0.819 \\ \hline \multirow{3}{*}{DTC} & F & \multirow{3}{*}{0.831} & 0.847 & 0.815 & 0.831 \\ \cline{2-5}  & T & & 0.815 & 0.847 & 0.830 \\ \hline \multirow{3}{*}{BiGRU+} & F & \multirow{3}{*}{0.879} & 0.830 & 0.839 & 0.835 \\ \cline{2-5}  & T & & **0.907** & 0.902 & 0.905 \\ \hline \multirow{3}{*}{CNN+} & F & \multirow{3}{*}{**0.883**} & 0.873 & 0.795 & 0.832 \\ \cline{2-5}  & T & & 0.889 & **0.934** & **0.911** \\ \hline \hline \end{tabular}  
1. After adding bi-affective features, the accuracy of BiGRU and CNN models on the Weibo-16 dataset reached 87.9% and 88.3%, which increased the accuracy by at least 4.8-5.2 percentage points compared with the DTC model with good detection performance;
2. In the classification of \"rumor\" and \"non-rumor\", the accuracy, recall rate, and F1 value were significantly improved, and the various indicators reached more than 90%.  
The above data analysis shows that dual-emotion features play a prominent role in rumor detection, which can enable the model to obtain higher accuracy. The above data are sufficient to show that bi-affective features serve an important auxiliary function in the rumor detection task.
# State-of-the-Art Survey on Deep Learning-Based Recommender Systems for E-Learning
###### Abstract
This survey checked and classified research papers on RSs for learning that were published between 2015 and 2021 based on what they are recommending to the user (supporting the user tasks), the platforms (e.g., mobile, chatbot, or web-based), the recommendation techniques used, and categories of users (students or teachers). Therefore, this study makes contributions to research on RSs for learning by demonstrating the following:  
* Overview of traditional and nontraditional recommendation techniques;
* Synthesis of prior work on RSs for learning based on:
* Recommendation techniques;
* Recommendation goals (supporting user tasks);
* Platforms (mobile or web-based);
* Kind of users (For students, teachers, or both students and teachers). * Proposing potential future research direction on RSs for learning and other related application domains. At the end of this survey, our results should be able to answer the following seven essential questions:  
* To what extent is research in deep learning-based RSs in e-learning? * What is the frequency of usage of each recommendation technique? In other words, which techniques are used more often to implement RSs for learning? * Which user tasks (recommendation goal) do people pay more attention to? * Are RSs for learning capable of making unexpected and fortunate recommendations? * What is the ratio between chatbot, mobile, and web-based RSs for learning? * Technology-enhanced learning concerns teaching and learning, which received considerable attention from researchers on RSs?With new recommendation techniques (deep learning, context-aware, and multi-criteria rating techniques), has research in this domain shifted toward those new techniques? The overall structure of this paper takes the form of five sections, including this introductory section. Section 2 contains a brief review of RSs for learning, related works, and evaluation methods; Section 3 explains the classification methodologies and an overview of recommendation techniques; Section 4 contains the findings of our work. Additionally, a conclusion and some potential future research directions are presented in Section 5.
## 4 Practical Experiments  
_Planet4 Stereo_ was tested for change detection in two glacier areas of varying scales and characteristics. The first case study focused on the Shisper glacier (Karakoram, Pakistan), selected for its susceptibility to surges and prior use by [16] to validate PlanetScope DEM processing methods. Using the same PlanetScope scenes as provided by the authors, we evaluated the accuracy of the DEM through DoD calculations against reference data. Our results were similar to [16] in a stable region near Ali Abad/Karimabad village. Additionally, we performed an independent change detection analysis and benchmarked our findings against their published results. The second case study focused on the Boverbena glacier in the Smorstabb Massif, Jotunheimen, Norway. Glaciers in this region have experienced significant retreat in recent years, prompting ongoing monitoring by Norwegian authorities (e.g., [34; 35]). For our analysis, we utilized nearly cloud-free PlanetScope scenes from August and September 2021 to generate a DEM. We evaluated this model by comparing it to a LiDAR dataset captured in 2020. Additionally, we conducted change detection by comparing the PlanetScope DEM with a dataset generated from aerial images by aerotriangulation in 2013.
# Adsorption of Metolachlor and Its Transformation Products, ESA and OXA, on Activated Carbons
## Materials and Methods
### Isotherms and Kinetics Models  
[PERSON]'s pseudo-second order [23; 24] model was applied to describe the kinetics adsorption:  
\[q_{t}=\frac{k_{2}\times q_{e}^{2}\times t}{1+k_{2}\times q_{e}\times t} \tag{2}\]  
where \(k_{2}\) is the rate constant of pseudo-second order adsorption in g/mg h and \(q_{e}\) is the amount adsorbed at the equilibrium in mg/g. The values of the rate constants and the amounts of adsorption at equilibrium were determined by the non-linear method using Origin software.  
The equilibrium experimental adsorption data were fitted to Langmuir and Freundlich models. The Langmuir equation describes the monolayer adsorption and allows the calculation of the maximum amount adsorbed to complete the monolayer (\(q_{max}\)) in mg/g [25]:  
\[\frac{C_{e}}{q_{e}}=\frac{1}{b\times q_{max}}+\frac{C_{e}}{q_{max}} \tag{3}\]  
where \(C_{e}\) is the liquid phase solute equilibrium concentration in mg/L and \(b\) is the Langmuir adsorption constant in L/mg.  
The Freundlich model is an empirical model which assumes that the adsorption occurs on a heterogeneous surface. The expression is [26]:  
\[q_{e}=k_{f}\times C_{e}^{1/n_{f}} \tag{4}\]  
where \(k_{f}\) in (mg/g)(L/mg)\({}^{1/nf}\) and \(n_{f}\) are Freundlich constants that depend on the solute-adsorbent interaction.
## 4 The DSEC Method for TSSP  
### Database Generation and Preprocessing  
In order to generate a statistical database for TSSP, the Monte Carlo method is utilized and the main steps of data generation and preprocessing are described as follows:  
Step 1: Utilize the Monte Carlo method to randomly generate a new operating condition of the power system based on the base condition. Check the feasibility of the new operating condition and then randomly generate a disturbance scenario. Obtain the response trajectories of generators after given the disturbance scenario using the TDS method.  
Step 2: Construct the initial features for the TSSP consisting of system-level features and single machine-level features. The electrical variables closely related to the transient stability characteristics, such as load level, generator active power output, rotor angle, kinetic energy, etc., are considered. A detailed feature description can be found in Reference [19].  
Step 3: Determine the transient stability status of the power system. If the power system is unstable, it is labeled as 1; otherwise, it is stable, labeled as \(-1\).  
Step 4: Combine the initial features with the corresponding label to form a sample and put the sample into the database.  
Step 5: Repeat steps 1-4 until the predefined number of samples is obtained.  
Step 6: Employ the two-stage feature selection method [19] to eliminate the irrelevant and redundant features in the original database and, finally, obtain the classification database.
# Automated and Controlled System for Analysis of Residual Limbs Thermograms of Transtibial Amputees
## 2 Problem Statement  
The current process for capturing thermal images to analyze the skin temperature in transtibial residual limbs is nonautomated and mostly manual, and the workspace is not isolated. To the authors' knowledge, there are not commercial devices for the automation of this specific task. The development of an automated cabin to capture and analyze the thermal images is proposed to improve the actual process. For the development of such a device, the following points were considered:  
* The first step is to set the requirements for developing the system, including hardware, software, system, and design criteria.
* The second step is to validate the requirements, then, the CAD design for the structure and mechanisms.
* Parallel to the CAD, the electronic instrumentation should be considered.
* Another important issue is to obtain the mathematical representation or to validate that some general model can represent the robotic system to design and implement an algorithm to solve the trajectory tracking of the thermal camera, i.e., generally, robotic systems can be represented by ordinary differential equations (ODEs) using the Euler-Lagrange approach [17]. Such general equation can be described as follows, \[F_{1}(z)\underline{z}+F_{2}(z,z)\underline{z}+F_{3}(z)+F_{4}(z,\underline{z},t )=Q,\] (1) where \(z\in\mathbb{R}^{n}\), \(\underline{z}\in\mathbb{R}^{n}\), and \(\underline{z}\in\mathbb{R}^{n}\) represent the position, velocity, and acceleration for the \(n\) degrees of freedom, respectively, \(F_{1}\colon\mathbb{R}^{n}\to\mathbb{R}^{n\times n}\) is the inertia matrix, \(F_{2}\colon\mathbb{R}^{n}\times\mathbb{R}^{n}\to\mathbb{R}^{n\times n}\) is the Coriolis matrix, the viscous friction vector is denoted with \(F_{3}\colon\mathbb{R}^{n}\to\mathbb{R}^{n}\), \(F_{4}\colon\mathbb{R}^{n}\times\mathbb{R}^{n}\times\mathbb{R}^{+}\to\mathbb{R} ^{n}\) represents bounded perturbations and model uncertainties that might affect the system, and \(Q\in\mathbb{R}^{n}\) represents the generalized external forces. Once the mathematical representation is validated, the following issue is the design of the control algorithm.
* The input for (1) should be designed in such a way that the angular position of the camera follows a desired trajectory;  
* The path for the mobile platform should be smooth and adjusted with the user selection of steps.
* Once the capture of the image in all the steps is completed, the processing to obtain the analysis of the thermograms for the residual limb is required.
* Finally, the integration of all the steps and some class of validation are required.  
The following subsection describes the first step in the development of the system. This step defines a list of critical requirements that must be satisfied by the final design. This practice helps define, understand, and visualize the overall scope of the development process.
## 2 Materials and Methods
### Statistical Approach  
A total number of 300 certificates of leaching tests performed on both CDWs and RAs were acquired from the databases of the two selected treatment plants. The collection period refers to 10 years. The collected data were subsequently elaborated using a statistical approach. Specifically, the \"box-plot\" methodology was applied to identify the variability of pollutant concentrations and the critical parameters according to the regulatory limits imposed by the M.D. 186/2006. In particular, this research methodology was applied using the IBM SPSS software.  
As shown in Figure 1, the box-plot graph visually shows the distribution of numerical data by means of five significant numbers:  
* lower extreme: minimum value identified by a whisker. Points within a distance between Q1 and Q1 minus 1.5 times the interquartile range (IQR--the difference between Q3 and Q1).
* first quartile (Q1): 25% of the data values are less than or equal to this value;
* median (Q2): 50% of the data values are less than or equal to this value;
* third quartile (Q3): 75% of the data values are less than or equal to this value;
* upper extreme: maximum value identified by a whisker. Points within a distance between Q3 and Q3 plus 1.5 times the IQR.  
The graph also shows the presence of anomalous values (i.e., outliers marked with circles and extreme values marked with asterisks) with respect to the majority of the observed data. These data are values that significantly differ from the general trend of the distribution and can be caused by errors (of transcription and measurement), exceptional circumstances, or contamination of data.  
In this context, to better understand the behaviour of pollutants with respect to regulatory limits and to identify any critical parameters, the dispersion of concentrations in terms of IQR and whisker lengths was evaluated. In particular, the greatest criticality was identified when the box (i.e., IQR which contains 50% of all analysed data) exceeded the regulatory limit considered. By means of this methodology, the identified critical pollutant releases were then correlated with the mereological fractions (i.e., EWC code) present in the analysed material in order to identify possible correlations between the characteristics of RAs and those of the original CDWs. In particular, the analysis was developed with reference to the EWC codes most managed by the analysed treatment plants: EWC 170101\"cement\", EWC 170302 \"bituminous mixtures\", EWC 170504 \"soil and stones\", EWC 170904 \"mixed construction and demolition wastes\".
# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 2 Materials and Methods
### Description of the Development Environment  
The cybersecurity training development environment is based on integrating AWS services and OpenAI tools, providing a robust and versatile infrastructure. Amazon (Amazon Web Services, Inc. (AWS); Seattle; EE. UU.) EC2 (Elastic Compute Cloud) is used to deploy scalable and customizable virtual servers, essential for running complex simulations and AI models [29; 30]. For data storage and retrieval, we turn to Amazon S3 (Simple Storage Service), which allows us to handle the large volumes of data generated by the simulations and host training resources.  
Figure 1: Stages of the cybersecurity training environment (source: authors’ elaboration).  
Additionally, AWS Lambda plays a crucial role in our environment, allowing code to be executed in response to specific events without the need to manage servers, which optimizes the efficiency and scalability of the system. Security and privacy are handled through Amazon VPC (Virtual Private Cloud), which allows us to launch resources in a defined virtual network, and AWS IAM (Identity and Access Management), which ensures secure access to AWS services and resources [31].  
Regarding OpenAI tools, GPT-3 (Generative Pretrained Transformer 3) is used for its advanced natural language processing capacity, essential for generating realistic cybersecurity scenarios and interactively dialogue with users. Additionally, DALL-E is innovatively used to create data visualizations and graphical representations, making it easier to understand complex cybersecurity concepts [32]. The synergy between AWS's scalable and secure infrastructure and OpenAI's language processing and content generation capabilities form the foundation of our development environment. This combination supports an advanced and highly interactive cybersecurity training environment and offers an unprecedented educational experience in this field.  
Figure 2 illustrates the architecture of the cybersecurity training environment, highlighting the integration of AWS services and OpenAI tools. At the base, Amazon EC2 provides simulation processing power, while Amazon S3 manages data storage. AWS Lambda is essential in event-driven code execution, and Amazon VPC ensures a secure and controlled network environment [33]. On the other hand, AWS IAM provides secure access management and authentication. Regarding the integration of OpenAI, GPT-3 generates dynamic cybersecurity scenarios and interacts interactively, while DALL-E creates visualizations and graphical representations. These AI tools connect with AWS services and each other, ensuring a consistent and efficient flow of operations, from data processing to generating content in the simulation environment. The figure reflects how each component interconnects to form a cohesive system, providing an advanced and highly interactive cybersecurity training environment.
## 3 Case Study
### Modeling of Main Diesel Engine Rotational Speed Control by Fuzzy Logic Control Theory
In addition, based on the actual operation situation, the main diesel engine speed controller has been acted appropriately for adjusting the fuel rack handle in aim with keeping engine speed constantly. Although the conventional controller bases on the accuracy of the system model and parameters, fuzzy logic controller uses the different strategies for adjusting diesel engine speed under impacting on the external environment factors. Fuzzy logic controller proposes the experiences and linguistic definitions. Moreover, if there is not enough knowledge about control process, the fuzzy logic controller might give satisfactory results. The aim of fuzzy logic controller is to minimize the speed error. Besides that, the change of speed error plays a virtual role to define the controller input. Consequently, fuzzy logic controller uses the speed error and change of speed error for linguistic knowledge which are generated from the control rules. The speed error is computed with comparison between reference speed and speed signal feedback from speed output of main diesel engine. Both speed error and change of speed error are fuzzy controller inputs. From output signal of fuzzy logic controller will act on actuator and then make the movement of fuel control position on main diesel engine. In reality, the fuel rack handle will decide the diesel engine speed. If the fuel control position is at an increasing level, then diesel engine speed will increase and vice versa. In Figure 12, the model of main diesel engine speed controller has been built on Simulink/Matlab environment. To establish this model, the input signals and output signal of fuzzy logic controller have been defined. The speed error and change of speed error are input signals of controller. The main diesel engine speed is output signal of controller. The output signal of controller will act on actuator through transfer function under form \(\frac{1}{0.5979\%+0.2}\), as well as the main diesel engine with transfer function \(\frac{1}{0.018+0.004}\). The transfer functions of actuator and main diesel engine have been assumed with testing the main diesel engine speed controller based on fuzzy logic control theory. On the other hand, the external load has been added in order to change the diesel engine speed. It likes as the navigation environment factor impacts. From the model of diesel engine speed controller has been established on Simulink/Matlab environment that appropriately verified the fuzzy logic controller for main diesel engine speed control. In this study, the fuzzy membership functions of diesel engine speed controller are optimized by Particle Swarm Optimization (PSO) algorithms. PSO algorithms will find out the optimal positions, as well as the best ranges of each variable in diesel engine speed controller. Based on coefficients of  
Figure 12: Simulation model of marine diesel engine speed Controller.
# Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
## 2 Intrusion Detection in the Internet of Things
### Placement Strategy  
The placement strategy of an intrusion detection system (IDS) is crucial, as it determines where in the architecture the IDS operates, influencing its effectiveness, coverage, and operational cost. For the Internet of Things (IoT) environments, the complexity and diversity of devices and their specific requirements offer unique challenges [39]. The placement strategy determines where the IDS is deployed within the IoT infrastructure.  
* Edge-based IDS: deployed on edge devices or gateways.
* Cloud-based IDS: utilizes cloud resources for intrusion detection.
* Hybrid: combines both edge and cloud-based approaches.