esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrete-Time Design of Dual Internal Model-Based Repetitive Control Systems
## 3 Discrete-Time RC
### A Higher-Order Modified RC  
Here, the control problem for the LTI system (1) is disturbance rejection only, implying that the reference \(r(k)\) is set to zero. The following input-output relation expresses a discrete-time higher-order modified repetitive controller (HOMRC) [15]:\[\frac{U_{h}(z)}{E(z)}=\left[\frac{q_{v}(z)\sum_{j=1}^{m}w_{j}z^{-jN_{v}}}{1-q_{v}(z )\sum_{j=1}^{m}w_{j}z^{-jN_{v}}}\right]F(z), \tag{9}\]  
where \(U_{h}(z)\) is a control input generated by HOMRC, \(q_{v}(z)\) is the q-filter used for cutting off higher frequencies of the disturbance \(v(k)\), \(F(z)\) is the stabilizing controller, and \(N_{v}\) is the number of samples per disturbance period. In contrast with (5), \(N_{v}\) is an integer number given by  
\[N_{r}=\frac{\bar{T}_{v}}{\bar{T}_{s}}=\frac{1}{\bar{f}_{v}\bar{T}_{s}}, \tag{10}\]  
where \(\bar{T}_{v}\) is a nominal disturbance period which is also known based on Assumption 2.  
Similar to (4), we also determine the internal model of the HOMRC given by  
\[\mathcal{I}_{h}(z)=\frac{\mathcal{N}_{h}(z)}{\mathcal{D}_{h}(z)}=\frac{\left( q_{v}(z)\sum_{j=1}^{m}w_{j}z^{-jN_{v}}\right)}{1-\left(q_{v}(z)\sum_{j=1}^{m}w_{ j}z^{-jN_{v}}\right)}, \tag{11}\]  
It can be seen from (11) that HOMRC employs a sum of the weighted delays, i.e, \(\sum_{j=1}^{m}w_{j}z^{-jN_{v}}\). The weights \(w_{j}\) for \(j=1,2,\cdots,m\) are chosen to satisfy conditions as follows [15]:  
\[\sum_{j=1}^{m}w_{j}=1, \tag{12}\] \[\sum_{j=1}^{m}j^{m-1}w_{j}=0. \tag{13}\]  
The conditions (12) and (13) are added to ensure that the high gains of HOMRC at the fundamental frequency and its harmonics remain infinite. In addition, the high-gain peaks of HOMRC at the harmonics are extended to wider regions. This behavior can be seen from the magnitude responses of GMRC and HOMRC at the targeted fundamental frequency 1 Hz shown in Figure 1. Hence, the disturbance-rejection performances at the neighboring frequencies are improved by using the HOMRC.  
Figure 1: Magnitude responses of the GMRC and the HOMRC.
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 2: Develop and Down-Select Nanogrid Architectures  
In this step, potential nanogrid architectures are developed and down-selected based on site-specific and stakeholder-specific conditions.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 3 Results
### Analysis Findings for Scenario-2 Classification Models
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** & **Specificity** & **MCC** & **Error Rate** & **AUC** \\ \hline KNN & 0.931 & 0.921 & 0.867 & 0.876 & 0.954 & 0.841 & 0.069 & 0.978 \\ \hline SVM & 0.954 & 0.983 & 0.867 & 0.909 & 0.988 & 0.889 & 0.046 & 0.992 \\ \hline RF & 0.954 & 0.958 & 0.875 & 0.899 & 0.976 & 0.881 & 0.046 & 0.986 \\ \hline LR & 0.907 & 0.780 & 0.792 & 0.776 & 0.943 & 0.727 & 0.093 & 0.952 \\ \hline XGBost & 0.954 & 0.950 & 0.900 & 0.904 & 0.965 & 0.889 & 0.046 & 0.979 \\ \hline DT & 0.885 & 0.833 & 0.802 & 0.797 & 0.925 & 0.734 & 0.115 & 0.864 \\ \hline
10-layer DNN & **0.969** & 0.976 & 0.948 & 0.956 & 0.948 & 0.921 & 0.031 & 0.978 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Metric analyses of models used in binary classification with K-10 cross-validation. Figure 3: Binary classification graphs of the 10-layer DNN model based on K-10 cross-validation: (**a**) 10-layer DNN confusion matrix; (**b**) 10-layer DNN model accuracy; (**c**) 10-layer DNN model loss; (**d**) ROC curves for all models.
# Intelligent Caching with Graph Neural Network-Based Deep Reinforcement Learning on SDN-Based ICN
## 6 Conclusions  
In this paper, we introduced GNN-DDQN, an intelligent caching scheme designed for the SDN-ICN scenario. GNNs have gained significant attention recently for their ability to handle graph-structured data. Leveraging this capability, we applied GNNs to process network topologies, enabling cooperative caching among nodes and promoting a wider variety of cached content. By integrating GNNs into DRL, our proposed approach empowered the DRL agent to make caching decisions for all nodes in the network with only one forward pass through the neural network. This integration not only streamlined the caching decision-making process but also harnessed the power of GNN-DRL synergy in optimizing the caching strategies.  
Firstly, we generated user preferences for content based on a real-world dataset. This step ensured that the evaluation reflected realistic user behaviour and content demand patterns. Next, we developed a GNN-DDQN agent within the SDN controller, enabling the agent to make intelligent caching decisions for all router nodes equipped with caching capabilities in the ICN network. Finally, we compared the performance of our proposed GNN-DDQN caching scheme with the state-of-the-art MLP-DDQN strategy and several classical benchmark caching schemes, including LCD, PROB_CACHE, CL4M, and LCE. The extensive evaluation revealed that GNN-DDQN consistently outperformed MLP-DDQN in most scenarios. Notably, in the best-case scenario, GNN-DDQN achieved a remarkable 34.42% higher CHR, a 4.76% lower ALT, a 3.77% lower APS, and a 5.21% lower ALL com  
pared to MLP-DDQN. Furthermore, GNN-DDQN demonstrated superior performance compared to classical caching schemes. To assess the robustness of our proposed scheme, we conducted experiments on benchmark network topologies, including GEANT, ROCKETFUEL, TISCALI, and GARR. GNN-DDQN consistently delivered outstanding performance across these diverse network topologies, reinforcing its reliability and applicability in real-world scenarios.  
Some potential directions for future research include the following.  
* Latency Consideration: Investigating the latency of the SDN controller and exploring techniques to mitigate the latency issue when dealing with a large number of network nodes.
* IoV-Based Environment: Integrating the proposed caching strategy in an IoV environment. This may involve studying the unique characteristics of vehicular networks and exploring how the methodology can be adapted to optimize content caching and delivery in such dynamic and mobile scenarios.  
Conceptualization, J.H., T.T., H.L. and A.N.; Methodology, J.H., T.T. and H.L.; Software, J.H. and T.T.; Supervision, A.N.; Validation, T.T. and H.L.; Visualization, J.H.; Writing--original draft, J.H. and T.T.; Writing--review and editing, H.L. and A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data will be available on request from the corresponding author.  
The authors declare no conflict of interest.
# Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
## 4 Analysis, Results, and Discussions  
This section includes a comparison of SAD classes in terms of gaze interaction/avoidance. The interaction and avoidance data for each class were computed separately. As can be seen from Figure 5, participants in the \"No SAD\" class had the highest gaze interaction, with an average percentage corresponding to 82.9%. This shows that people who do not have SAD are more likely to keep gaze interaction. The gaze interaction average percentage tends to decrease as the severity of the SAD tends to increase. As Figure 5 shows, the marked,  
\begin{table}
\begin{tabular}{c c c} \hline
**Conversation Domain** & **Task Explanation** & **Time Frame** \\ \hline Introduction Conversation & The observer and participant were introduced to each other. & 1 min \\ \hline Social Support Conversation & The participants were asked to choose something that they & 2 min \\  & want to change about themselves. & \\ \hline Conflict Conversation & The participants were asked to tell something about their & Minutes \\  & best friends and what they want to change in them. & \\ \hline \end{tabular}
\end{table}
Table 3: The conversation contents and procedure.  
moderate, severe, and very severe average gaze interaction percentages decreased to 64.7%, 46.6%, 31%, and 27.4%, respectively. The largest gaze interaction percentage of a participant in the \"No SAD\" group was 88%, in the Marked group it was 70.50%, in the Moderate group it was 53.70%, in the Severe group it was 37.80%, and in the 'Very Severe' group it was 34.60%. An overall analysis revealed a linear relationship between gaze interaction and increasing severity of SAD.  
The next analyses were conducted to analyze the relationship between subgroups such as females vs. males. In addition, data analysis in the context of parameters like accuracy, precision, and recall was also conducted.
## 3 Results  
### Screening and Data Synthesis  
The search string returned 1782 hits, of which 72 articles reached the final screening, i.e., full text screening. Figure 1 shows the quantity of papers removed from the review at each stage of the screening. Out of 1782 records identified, 855 were automatically excluded by language and publication year and 3 were excluded due to duplication during the initial screening. The title screening further narrowed down the pool to 350 articles, excluding studies not related to productivity or eye tracking. At the abstract screening stage, 277 articles were excluded, predominantly because their focus was outside the scope of reading productivity-related eye-tracking metrics. Following the completion of the full-text screening process, 42 articles were deemed relevant for the study.  
As shown in Figure 2, the majority of articles were published in the last five years. This trend aligns with advancements in machine learning techniques and the increased availability of affordable eye-tracking devices.  
The articles were found to have four main themes: general eye metrics and tracking, measuring comprehension, measuring attention, and typography and typesetting (Figure 3). Eye metric classification focuses on identifying patterns like fixations and saccades, while comprehension-related studies analyse reading performance. Articles on attention explore gaze fixation durations, and typography studies examine how text formatting affects reading performance.  
Figure 1: PRISMA flow diagram for study screening and inclusion.  
Figure 2: Number of papers published by year.  
ability. Overlap between categories, such as attention metrics influencing comprehension studies, highlights the interconnected nature of these themes.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 3 Materials and Methods  
The proposed EOR incorporates two types of spatial feature descriptions: SDF and SSC. The SDF provides insights into the overall feature distribution, guiding the registration process, while the SSC enforces specific geometric constraints to refine and optimize the registration results. This section will introduce the system overview, elaborate on key steps including image segmentation, SDF of objects from the HD map and images, SSC of HD map objects, and show the enhanced pose estimation.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 3 Results and Discussion
### Biodegradation of DR23 Dye
#### 3.3.4 Reusability  
Both nanoparticle-immobilized laccase (Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\)) were investigated for the degradation of DR23 dye several successive times, as shown in Figure 6. The reusability has great importance in minimizing the overall cost of treatment, minimizing the enzyme wastes, and increasing its industrial applicability [44; 45; 46]. According to Figure 6, there is higher relative reusability of both the nanobiocatalysts towards the degradation of DR23 dye, with a gradual decrease in the efficiency of decolorization from cycle 1 to cycle 5. Cycle 1 showed the highest efficiency for both the nanobiocatalysts, while the last cycle showed the lowest efficiency.  
This could attribute to the freshly used nanobiocatalyst in the first cycle. Reusing the nanobiocatalysts led to the protein leaching to the reaction medium, which caused the gradual decrease in the decolorization efficiency [47]. Also, the results indicated the higher reusability results for CuFe\({}_{2}\)O\({}_{4}\) immobilized increase than Fe\({}_{3}\)O\({}_{4}\) immobilized increase, indicating that CuFe\({}_{2}\)O\({}_{4}\) nanoparticles are a better support for laccase than Fe\({}_{3}\)O\({}_{4}\) nanoparticles. The decolorization efficiency of both the nanobiocatalysts was still over 70.0% after the  
Figure 5: HPLC analysis of blank solution (**a**) and biodegraded DR23 dye solution using immobilized laccase (**b**).  
sixth cycle, indicating the effective recovery and reusing of nanobiodatalysts. However, the CuFe\({}_{2}\)O\({}_{4}\) nanoparticles as support caused enhanced catalytic activity for the laccase enzyme. Future studies must be focused on the determination of thermodynamic parameters [48; 49; 50] for enzyme immobilization, to get a full image about the nanobiocatalyst.
## 2 Intelligent Compaction System
### System Software  
The software used in this research is a Web-GIS-based civil engineering program developed on Google Map and JAVA script API technology that was developed in previous research [28]. It can locate and provide maintenance data and monitor the work performance management of earthwork equipment in real time. In this research, an addition has been made to the current Web-GIS-based earthwork equipment management system. It not only provides the GPS location of the roller but also the compaction information such as the number of passes, the roller path, the strength of the compaction material, etc. The design algorithm and Entity-Relation Diagram (ERD) for the system software are shown in Figures 3 and 4, respectively. The number of passes is determined from the GPS using roller movement information, and the accelerometer data are transmitted for time matching analysis. According to CMV standards, the CMV in the range of 0 to 100 can only be considered for analysis, and any values above 100 are excluded [29].  
The software's user interface indicates the location, the number of roller passes as IC control, and the quality information as the CMV. It shows the compaction information in different color bands. The number of roller passes is expressed in 12 color bands, and the compaction quality is indicated in 5 color bands in terms of CMV. These color bands provide real-time visual information about the compaction information to the roller operator and construction manager. It stores not only the compaction information but also the project information such as the project's name and address, the project manager, the machine operator, the crew, the instruments, and the photographs. The stored information can also be recalled by project name or date. It also uses the Geo-fencing function to draw the polyline mark, the project area, and the restricted area for the roller. It also allows for the addition of the roller specifications and the recalling of the previous compaction record of the current or any previous project.  
Figure 3: Design of the system software algorithm for compaction information.
## 5 Discussion
### Technical Analysis and Comparative Effectiveness of ML and DL Models
#### 5.2.1 Long Short-Term Memory (LSTM) Networks  
Technical Details: An LSTM network is a type of recurrent neural network (RNN) that uses memory cells to learn and retain long-term dependencies across broad sequences. This design enables LSTM networks to handle sequences of varying lengths and capture complex time series, making them more efficient for sequential time forecasting.  
\begin{table}
\begin{tabular}{l l l} \hline \hline \# & **Supply Chain Application** & **AI Methods** \\ \hline
1 & Blood supply chain management & Machine learning, time series forecasting \\ \hline
2 & Automated blood bank management & Automated machine learning, forecasting \\ \hline
3 & Blockchain-enabled demand forecasting & Blockchain integration, machine learning \\ \hline
4 & Supply chain optimization & Evolutionary algorithms, support vector regression (SVR) \\ \hline
5 & AI in small- and medium-sized enterprises (SMEs) & Artificial intelligence (AI), machine learning, deep learning \\ \hline
6 & Supplier selection and order allocation & Machine learning, optimization models \\ \hline
7 & E-healthcare supply chain management & Deep learning, reinforcement learning \\ \hline
8 & Predictive analysis in supply chain management & Machine learning, predictive analytics \\ \hline
9 & Retail demand prediction with machine learning & Tree-based ensembles, long short-term memory (LSTM) \\ \hline
10 & AI-based time-series forecasting for inventory & AI-based time-series forecasting, anomaly detection \\ \hline \hline \end{tabular}
\end{table}
Table 5: Supply chain applications and corresponding AI methods.  
Performance: Studies, such as those by [PERSON] and [PERSON], have shown that LSTM networks outperform traditional models in capturing nonlinear relationships in demand data, resulting in higher prediction accuracy. LSTM models reduce forecast errors by 15-20% compared to ARIMA models in terms of retail demand forecasts.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 4 Research Methodology
### Evaluation Approach of the Experimental Analysis  
The evaluation metrics of precision, recall, and F1-score were used for the experimental analysis conducted for this research. Precision is the percentage of correctly identified positive cases from the whole data sample [84], and recall is the percentage of correctly identified positive cases from the positive samples only [85]. The formulas used are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
The F1-score considers both the positive and negative cases combined, and the formula used to calculate the F1-score is set out as follows [86]:  
\[F1-Score=\frac{2*(Precision*Recall)}{Precision+Recall} \tag{6}\]  
A confusion matrix [87], as shown in Table 11, will also be generated for each experiment that is conducted. The confusion matrix calculates the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) scores for each dataset.
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 2 Small-Scale Refrigeration Technologies
### Active Technologies
#### 2.2.2. Spray Cooling and Jet Impingement  
Spray cooling is another active technology where a high-pressure liquid is atomized directly into the heat source through a nozzle, as can be seen in Figure 2. The droplets, upon coming in direct contact with the hot surface, evaporate and some others form a thin liquid film on it, combining several heat transfer mechanisms to obtain high heat removal rates. Fluids such as water, alcohols, fluorocarbons, refrigerants, and others are used in spray cooling, possibly in combination with additives to improve their physical properties, such as nanofluids [28].  
Similarly to spray cooling, jet impingement is also used for high heat flux removal, with the particularity that high-speed liquid jets instead of an atomized mix flows through the nozzle. Impinging jets can be classified according to three device surface scenarios: free, submerged, and confined jets, as shown in Figure 3. Free jet impingement occurs when the jet exiting the nozzle encounters ambient gas before commonly impacting the  
Figure 1: Schematic of forced convection heat transfer over a hot device.  
Figure 2: Schematic of spray cooling.  
target surface, as opposed to when the nozzle is submerged in which the jet impacts a layer of the same fluid resting on the surface, and finally, confined jets occur when the jet remains limited by the target surface and nozzle plate. These cooling methods are generally used in diverse applications such as the steel industry, cooling of heat engines, nuclear power processes, cooling of gas turbine components, material processing, and the most representative small-scale application: electronic devices [29].  
Several publications on spray and jet cooling strategies for high heat flux applications have demonstrated heat fluxes of the order of \(10^{6}\) W/m\({}^{2}\). Heat fluxes over 3 \(\times\) 10\({}^{6}\) W/m\({}^{2}\) have been achieved through thermal inkjet-assisted spray cooling technique by controlling the timing and location of jet impingement on the hot surface [30]. In another study, the water jet impingement strategy was used to cool three surfaces with different wettability, reaching heat flux values over 3.5 \(\times\) 10\({}^{6}\) W/m\({}^{2}\) for specific conditions [31].  
An important aspect of spray and jet cooling techniques is that their effectiveness is very sensitive to changes in the position and geometry of the nozzle, the temperature and geometry of the impact surface, the characteristics of the fluid used, and the jet conditions.
## 1 Introduction
Additionally, this model relies on centrally deployed AI models, making it difficult to support real-time AI applications with stringent latency requirements, such as intelligent control and autonomous driving. Therefore, AI should be natively embedded into the 6G network from the initial system design. This requires a holistic approach across infrastructure, network architecture, and interface design to enable 6G technology to support both communication and AI-driven services. AI deployment is expected to evolve into a hybrid model combining centralized and distributed intelligence. By leveraging the network's connectivity capabilities, AI functions at different locations within the network can be efficiently orchestrated, providing users with ubiquitous AI services. However, the path to AICT in 6G networks is fraught with challenges. Dynamic resource allocation across the cloud, the edge, and devices requires advanced scheduling algorithms to balance diverse and conflicting requirements. The lack of standardized protocols and interoperability complicates the integration of AI-driven functionalities within communication systems. Furthermore, privacy and security concerns arise due to data sharing and distributed computation, necessitating robust protection mechanisms. Motivated by the above challenges, this paper aims to provide a comprehensive overview of AI-driven 6G technology by analyzing its network architecture evolution, key enabling technologies, potential use cases, and associated challenges and opportunities. The contributions of this paper can be summarized as follows:  
* This paper provides a potential evolution path for RAN architecture and proposes a new end-to-end (E2E) AI-driven network framework integrating AI capabilities as a native feature to enable efficient and scalable intelligent services. * This study delves into critical enabling technologies, including cross-domain AI collaboration, native computing, and native security, offering practical insights for 6G implementation. * This paper identifies the opportunities presented by AI-driven 6G networks and analyzes the challenges in achieving seamless integration, providing guidance for future research. The remainder of this paper is structured as follows: Section 2 explores the potential evolution of RAN architecture and presents an end-to-end AI-driven network framework. Section 3 discusses key enabling technologies for AI-driven 6G networks, including cross-domain AI collaboration, native computing, and security mechanisms. Section 4 focuses on potential use cases, highlighting Network for AI scenarios such as embodied intelligence, wearable devices, and generative AI for 6G networks. Finally, Section 5 presents a discussion of future opportunities and challenges, and Section 6 presents the conclusions.