esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model
### Multi-Objective Optimization and Knee Point
In multi-objective optimizations, all points on the Pareto front are optimum points, as opposed to the single optimum value given by single-objective optimization. In other words, in multi-objective optimizations, the designer can directly see the entire optimum operating state of the model. Here, which point the design will take as optimum depends entirely on the importance the designer attaches to the objectives. While there are subjective situations in which the designer selects a determined value or makes a comparison according to the initial situation in order to select the optimum point, there are also objective selection methods such as knee point. In this study, in determining the optimum points in multi-objective optimization, in addition to selecting separate semi-optimal points for both CO\({}_{2}\) and Cost, the knee point method, which objectively gives the best situation for both CO\({}_{2}\) and cost objectives, is also used. The knee point method is to normalize the values obtained  
Figure 2: The flowchart of NSGA-II. in both objective functions and then calculates the shortest distance to the \"utopia point\". The \"utopia point\" represents the best solution where both objectives are combined. \"Knee point\" on the Pareto front is calculated with the following formulation [53; 54]:  
\[\text{min}D=\sqrt{\left(\sum_{\tau=1}^{n}\left(\frac{f_{\tau\tau}}{\text{min}(f_ {\tau}(x))}-1\right)^{2}\right)} \tag{22}\]  
where \(n\) symbolizes the number of the objective functions, \(f_{\tau\tau}\) indicates the \(\tau\)th objective value in the \(c\)th result, and \(D\) displays the distance from the knee point to the utopia point.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 2 Energy-Efficient IoT
### Energy Consumption Factors in the IoT
#### 2.1.2 Machine-to-Machine (M2M) Communication  
Communication protocols in the IoT, such as short-range LAN communications, cellular networks, and satellite communications, ensure the real-time data transmission between devices. However, these communication protocols also pose various energy-management challenges. For example, communication operations, signal transmission, and network maintenance all place significant demands on power resources. WiFi or Bluetooth technologies used in LAN communications, as well as cellular networks and satellite communications on a wider scale, require stable communication connectivity, which places tremendous demands on power resources. This power demand phenomenon becomes more severe in machine-to-machine (M2M) communication scenarios, where'smart devices' communicate with each other autonomously and make collaborative decisions without human intervention [13]. Even worse, since these devices are battery-powered and often require constant and uninterrupted connections, frequent charging becomes inevitable, thereby leading to increased energy costs. Therefore, carefully managing the charging cycles of devices is essential to maintaining reliable yet high-speed data transmission while minimizing power consumption.
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 3 Antenna/Detector Array Design  
### Modular Antenna/Detector Array  
A perspective view of the proposed modular antenna/detector array is shown in Figure 3a. In Figure 3b, an exploded view shows the modular antenna/detector array consisting of an array frame, 12 individually installed unit antenna/detector modules, and a pair of DC bias PCBs (printed circuit board). The array frame, made of aluminum, holds the 12-unit antenna/detector modules and the bias PCBs.  
By adopting the modular design for the antenna/detector array, each unit antenna/detector module can be attached to or detached from the array frame without interference  
Figure 3: Proposed modular antenna/detector array: (**a**) perspective view, (**b**) exploded view, (**c**) installation of the unit antenna/detector module.  
Figure 2: Block diagram of the KSTAR ECE imaging system.  
with adjacent channels, as shown in Figure 3c. The bias PCBs (designed with the 40-mil FR-4 substrate) are assembled on the bottom of the array frame. The total size of the proposed antenna/detector array is 425.7 \(\times\) 104.5 \(\times\) 38.8 mm.
## 3 Result and Discussion
### CO\({}_{2}\) Concentration Measurement Experiment  
#### 3.6.1 Configuration  
The field experiment was conducted to examine the performance of the developed sensor node in acquiring CO\({}_{2}\) concentration. The experimental configuration is shown in Figure 17. The developed sensor node was used to measure indoor CO\({}_{2}\) data sent to the Adafruit dashboard in the monitor terminal over the IoT system. In the terminal monitor, the CO\({}_{2}\) data be visualized in the real-time and continuous graph, current numeric data, and updated logging data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Process** & **Voltage (V)** & \
\begin{tabular}{c} **Current** \\ **(mA)** \\ \end{tabular} & **Power (mW)** & **Time (ms)** &
\begin{tabular}{c} **Energy Capacity** \\ **Required (mWh)** \\ \end{tabular} \\ \hline Initializing & 3.22 & 42.1 & 135.56 & 7 & 0.0003 \\ CO\({}_{2}\) Reading & 3.22 & 120.07 & 387.83 & 25 & 0.0027 \\ CO\({}_{2}\) Transmitting & 3.22 & 121.4 & 392.12 & 1319.33 & 0.1437 \\ Sleep & 3.22 & 33.2 & 106.9 & 10,000 & 0.297 \\ \hline \hline \end{tabular}  
Total Energy Required (one cycle)  
\end{table}
Table 6: Energy capacity.  
Figure 16: Power storage charging and discharging voltage.  
The measurement targets were the CO\({}_{2}\) concentration of an indoor laboratory and outdoor environment. The entire laboratory comprises laboratory equipment, three students, computers, and furniture with a room dimension of 6.0 m \(\times\) 3.5 m. The outdoor environment was around the student dormitory. The field experiment for indoor measurement was conducted in three and a half days from 19 May to 22 May 2022, and outdoor measurement was conducted in three and a half days from 25 May to 27 May 2022
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 2 Remote Sensing for Agriculture
### Spectral Resolutions and Useful Vegetation Indices for Sentinel-2 Features
More in depth reviews and extensive databases (e.g., [[https://www.indexdatabase.de](https://www.indexdatabase.de)]([https://www.indexdatabase.de](https://www.indexdatabase.de))) on the spectral regions most useful for agricultural monitoring are suggested for a more exhaustive coverage of spectral indexes in general [31; 32]. Regarding the chosen VIs relevant to Sentinel-2 imagery presented in Table 2, for the visible spectrum the Normalized Green-Red Difference Index (NGRDI) compares the differences between the green and red visible bands for vegetation, together with the well-known Normalized Difference Vegetation Index (NDVI) have shown to be able to remotely sense total crop green biomass [33], which is often indicative of general crop vigor and may be closely correlated with crop nitrogen (N) content and may provide good predictions of yield at specific growth stages. Another index that uses the visible bands of the spectrum is the Triangular Greenness Index (TGI), which has been shown to estimate correctly chlorophyll concentration at leaf and canopy level [34]. In some cases, a combination of two VIs may allow for more specific measurements, given that the sensor has enough spectral bands for both calculations. Many of these more advanced calculations were taken into account in the specific design of Sentinel-2 as part of its development specifically for precision agriculture and environmental/vegetation monitoring in general. For example, the ratio of the Transformed Chlorophyll in Reflectance Index and the Optimized Soil-Adjusted Vegetation Index (TCARI/OSAVI) has been shown to estimate chlorophyll leaf content [35], basically by providing a canopy chlorophyll measurement (TCARI) and then correcting for biomass (OSAVI). In some instances, the spectral indexes were originally developed for a prior satellite with similar spectral but coarser spatial resolution that did not make them amenable to smaller-scale agricultural applications, such as in the case of the MERIS (Medium-spectral Resolution Imaging Spectrometer) Terrestrial Chlorophyll Index (MTCI) [36]. Based on the red-edge bands, many of which are newly featured at higher spatial resolutions with the Sentinel-2 satellite, MTCI has showed that Sentinel-2 derived indexes using red-edge bands perform very precisely in estimating chlorophyll and N content [36]. It is also the case of the chlorophyll index red edge (CI red edge) [37], effective for estimating leaf area index (LAI). The Chlorophyll Vegetation Index (CVI), a broad-band VI sensitive to leaf chlorophyll content at canopy level, has also shown good results for estimating chlorophyll content [38]. Another index of interest, in this case based on NIR and RE bands is the Inverted Red-Edge Chlorophyll Index (IRECI), which has shown good abilities to estimate chlorophyll content at the canopy level [39].
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 3 Basics
### Named Data Networking in the Internet of Things  
The Internet of Things (IoT) is a term first coined by [PERSON] in 1999. It generally refers to scenarios where network connectivity and computing capability extends to objects, sensors, and everyday items not normally considered computers, allowing these devices to generate, exchange, and consume data with minimal human intervention [2].  
Thus, the IoT refers explicitly to devices with severe resource limitations, such as smartwatches, smart home devices, or traffic control devices. Particularly, such devices face memory, energy, and computation power restrictions [10]. The available resources are orders of magnitude smaller than for classical computers like desktop PCs or laptops.  
If we want to use the NDN protocol in IoT scenarios (called _IoT-NDN_), NDN faces specific constraints, specifically the packet length, data aggregation, and packet naming [25] problems. In the original definition of the NDN protocol [3], the _packet length_ is not fixed but variable.  
However, in the IoT, the MTU limiting the maximum packet size is often quite small, so the packet length has to be restricted. Unfortunately, NDN does not provide a fragmentation mechanism like IP. _Data aggregation_ refers to the problem that requesting a series of packets at once, e.g., with suffixes named 1 through \(n\), is impossible. For each Interest packet in the series, the consumer has to signify the packets it has already received in the so-called exclude parameter. This may create major overhead if many names are included in this exclude parameter [10]. Finally, _naming_ can be problematic since NDN names are typically intended to be human-readable. If such names are used, and the names are very long, the name itself can add dozens of bytes of overhead. While this problem can be compensated by abbreviating names, it would be at the cost of readability.
## 5 Applications and Challenges
### Applications
#### 5.1.4 Infrared Imaging  
By detecting infrared radiation emitted by an object and converting it into a temperature distribution image through an infrared detector, the infrared imaging system breaks through the human naked eye's dependence on visible light. It becomes an indispensable sensory tool in modern science and technology [90]. Its unique detection capability in complex environments and non-contact measurement advantages have made it occupy a central position in military, security, industrial, medical, and scientific research. [PERSON] et al. [91] developed a dielectric elastomer-driven long-wave infrared Alvarez lens based on DE as shown in Figure 13e, with a zoom ratio that continuously covers a dynamically adjustable focal length from 5\(\times\) to 15\(\times\). Combined with an infrared detector, the system is capable of high-resolution imaging under compact and low-power conditions, providing an innovative solution for miniaturised infrared zoom applications such as unmanned airborne devices and thermal imaging cameras. In the future, the dielectric elastomer-driven infrared imaging system can be applied in UAV reconnaissance, thermal fault warning, driving environment sensing, etc. DETL still needs to improve its performance further in optical performance optimisation and environmental adaptability.
# Performance Evaluation of Graph Neural Network-Based RouteNet Model with Attention Mechanism
###### Abstract  
Graph representation is recognized as an efficient method for modeling networks, precisely illustrating intricate, dynamic interactions within various entities of networks by representing entities as nodes and their relationships as edges. Leveraging the advantage of the network graph data along with deep learning technologies specialized for analyzing graph data, Graph Neural Networks (GNNs) have revolutionized the field of computer networking by effectively handling structured graph data and enabling precise predictions for various use cases such as performance modeling, routing optimization, and resource allocation. The RouteNet model, utilizing a GNN, has been effectively applied in determining Quality of Service (QoS) parameters for each source-to-destination pair in computer networks. However, a prevalent issue in the current GNN model is their struggle with generalization and capturing the complex relationships and patterns within network data. This research aims to enhance the predictive power of GNN-based models by enhancing the original RouteNet model by incorporating an attention layer into its architecture. A comparative analysis is conducted to evaluate the performance of the Modified RouteNet model against the Original RouteNet model. The effectiveness of the added attention layer has been examined to determine its impact on the overall model performance. The outcomes of this research contribute to advancing GNN-based network performance prediction, addressing the limitations of existing models, and providing reliable frameworks for predicting network delay.  
RouteNet; graph neural network; attention mechanism +
Footnote †: journal:  
0000-0001-1660-0001-16
###### Abstract
In contrast, servers and clients at lower-security levels are restricted from accessing the data and knowledge of high-security levels clients. The control of information flow is instantiated through the access control encryption (ACE) algorithm [19]. The ACE algorithm administers information flow by introducing a third-party entity called the'sanitizer' between the sender and receiver to'sanitize' the encrypted message. The sanitizer, operating without knowledge of the message content and access policy, controls who can read the encrypted message and regulates who can transmit the message by re-encrypting the received message using a special key [20]. Notably, the sanitizer can be served by edge servers within the IoT. We propose a hierarchical blockchain-empowered cloud-edge orchestrated FL (HBCE-FL) framework in IoT to provide intelligent and distributed data analysis for IoT users. In HBCE-FL, we designed a multi-level access control encryption-based IoT knowledge sharing approach to solving the problem of user privacy leakage in FL. This allows IoT users to participate in FL according to their personal privacy requirements and provides personalized federated learning (PFL) for users of non-IID data to achieve better data analysis results. The main contributions of this paper are as follows:  
* We propose an IoT HBCE-FL framework, where knowledge flows securely among local clients, edge blockchain, and cloud servers. This can provide a secure and privacy-preserving data analysis scheme for IoT users. * We design a multi-level ACE-based IoT knowledge sharing approach in HBCE-FL, which solves the privacy issues in FL by grading security requirements and controlling IoT knowledge flow. * We introduce blockchain to solve the trust problem of servers and users, improving the reliability of the system. The sanitization of knowledge through smart contracts ensures that knowledge is not tampered with. * The effectiveness of our framework is validated by security analysis and experimental evaluation. IoT users with different data distributions can all achieve better model accuracy and convergence speed through secure IoT knowledge access and sharing in our designed PFL. The rest of this paper is organized as follows. Section 2 shows the related work of privacy protection methods in FL. Section 3 describes the overview and main elements of the proposed HBCE-FL architecture. Section 4 illustrates the specific models of the proposed ACE and blockchain-based IoT knowledge sharing scheme. Section 5 describes PFL algorithms with multi-level privacy protection. Security analysis and performance evaluation are provided in Section 6, and Section 7 concludes the paper.
## 2 Methodology of the Study  
The study aims to identify a suitable predictive model which could be used to assess the seismic response of the URM building archetype, representing non-engineered masonry buildings generally, followed by the identification of building damage using ML techniques. First, regression-based ML techniques were used to develop demand models incorporating the input variables (ground-motion intensity measure and material properties) and an output variable (seismic response of the building). The maximum inter-story drift ratio (IDR), which is the ratio of the relative displacement (measured at the roof of the building) to the height of the building, was utilized as the building response. Next, the classification-based ML techniques were explored to classify the masonry building damage, using the material properties, ground-motion intensity, and seismic response as input parameters. The study involved three major steps: (i) database creation, (ii) data partition and pre-processing, and (iii) model training and performance evaluation.
## Appendix A  
This Appendix A contains the outcomes of sensitivity analysis (Figures A1 and A2) and the results of tested methods for removing compression artifacts in the BSDS500, Kodak24, LIVE1, Set14, and Set5 datasets, which contain natural images (Tables A1-A5). Figure 1: Partial Rank Correlation Coefficient (PRCC), which measures nonlinear and monotonic relationships between parameters of each artifacts removal method and gain in image quality metrics (\(PSNR\) and \(SSIM\)) in comparison to JPEG compressed images. Figure 12: Variable importance of MARS model, which estimates the impact of parameters on gain in image quality metrics (\(PSNR\) and \(SSIM\)) for each artifacts removal method. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & \\ \hline
**Method** & **Metric** & **QF = 10** & **QF = 20** & **QF = 30** & **QF = 40** & **QF = 50** & **QF = 60** & **QF = 70** & **QF = 80** & **QF = 90** \\ \hline JPEG & \(SSIM\) & 0.7455 & 0.8272 & 0.8627 & 0.8832 & 0.8977 & 0.9101 & 0.9246 & 0.9413 & 0.9631 \\  & \(PSNR\) & 25.69 & 28.06 & 29.37 & 30.28 & 31.03 & 31.77 & 32.77 & 34.23 & 36.86 \\ \hline EPIIC & \(SSIM\) & 0.7556 & 0.8312 & 0.8662 & 0.8864 & **0.9007** & **0.9129** & **0.9270** & **0.9432** & **0.9637** \\  & \(PSNR\) & 25.94 & 28.27 & 29.59 & 30.52 & 31.27 & 32.00 & 32.98 & 34.36 & 36.67 \\ \hline EPIIC Sobel & \(SSIM\) & 0.7555 & 0.8312 & 0.8662 & 0.8864 & **0.9007** & **0.9129** & **0.9270** & 0.9431 & **0.9637** \\  & \(PSNR\) & 25.94 & 28.27 & 29.59 & 30.51 & 31.26 & 31.99 & 32.97 & 34.35 & 36.65 \\ \hline FBCNN & \(SSIM\) & **0.7563** & **0.8389** & **0.8703** & **0.8882** & 0.9006 & 0.9113 & 0.9237 & 0.9376 & 0.9540 \\  & \(PSNR\) & **26.19** & **28.45** & **29.76** & **30.66** & **31.39** & **32.11** & **33.06** & **34.38** & 36.45 \\ \hline Average Filter & \(SSIM\) & 0.7352 & 0.7881 & 0.8058 & 0.8142 & 0.8192 & 0.8228 & 0.8263 & 0.8294 & 0.8328 \\  & \(PSNR\) & 25.32 & 26.57 & 26.96 & 27.15 & 27.26 & 27.34 & 27.42 & 27.50 & 27.60 \\ \hline Median Filter & \(SSIM\) & 0.7364 & 0.7979 & 0.8193 & 0.8296 & 0.8360 & 0.8406 & 0.8455 & 0.8500 & 0.8552 \\  & \(PSNR\) & 25.54 & 27.09 & 27.65 & 27.94 & 28.11 & 28.25 & 28.40 & 28.55 & 28.73 \\ \hline Wiener Filter & \(SSIM\) & 0.7557 & 0.8188 & 0.8422 & 0.8545 & 0.8625 & 0.8688 & 0.8753 & 0.8816 & 0.8878 \\  & \(PSNR\) & 26.06 & 28.18 & 29.17 & 29.79 & 30.24 & 30.63 & 31.09 & 31.60 & 32.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(SSIM\) and \(PSNR\) scores for the LIVE1 dataset. The **first**, the **second**, and the third best results are highlighted.
## 1 Introduction
Despite their growing adoption, evaluating the performance of these modes across varying levels of question complexity presents significant challenges, particularly in academic contexts, where interaction demands can vary widely [2; 19; 20; 21]. This study addresses this gap by examining the interplay between interaction modes and question complexity, as well as how these factors influence UX metrics. The importance of evaluating these aspects is further underscored by their implications for student satisfaction and retention. Effective chatbot systems can respond promptly to student queries, provide uninterrupted access to academic support, and reduce administrative burdens. These capabilities are crucial for improving satisfaction and engagement, especially in educational systems that aim to enhance accessibility and inclusivity [17; 19]. Despite the growing adoption of chatbots, current evaluation methods often lack standardization and fail to provide a holistic perspective on UX. This study addresses this gap by proposing a comprehensive framework that integrates diverse metrics, including task efficiency, error rates, engagement levels, and UX [22]. To address this research gap, the present study evaluates the influence of chatbot interaction modes (text-based, menu-based, hybrid) and question complexity (simple, intermediate, complex) on evaluation focused on four distinct UX dimensions: perceived usability (via CUQ), engagement (via UES-SF), and objective performance metrics (efficiency, through response times; and effectiveness, through error rates). This study employs a PCA-based weighting method to integrate these metrics into a comprehensive framework, ensuring a statistically robust representation of their respective impacts on overall user experience. To illustrate the practical application of this framework, this study evaluates Tayseer, an Arabic educational chatbot [7]. A within-subject factorial design (3 \(\times\) 3 configuration) involving 30 students was utilized to assess Tayseer's performance across diverse interaction scenarios. Research question: How can we develop and validate a holistic evaluation framework for educational chatbots through examining the interplay among interaction modes, question complexity, and user experience dimensions? RQ1: How do interaction modes influence student experience? RQ2: How does question complexity affect performance and chatbot usability? RQ3: Are specific modes better suited to particular levels of question complexity? RQ4: What criteria should guide future comparisons among educational chatbots? By investigating the effects of interaction modes and question complexity on usability and UX, this research aims to foster the development of more efficient, engaging, and inclusive educational chatbots. The findings are expected to inform future design practices, enabling chatbots to better meet the varied requirements of students across different linguistic and educational settings.