esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration of Artificial Intelligence in Art Preservation and Exhibition Spaces
## 2 Literature Review
### The Application Prospects and Potential Limitations of AI in Art Exhibitions  
The typical functions of general art museums include collection management, education, exhibition, on-site services, and performances. These operations may gradually incorporate artificial intelligence (AI) and other technologies in the future to develop further applications. In recent years, art museums have been seeking to improve their digital applications and enhance their value-added services. One important strategy is to integrate the digital exhibition process with AI [10,11].  
AI can create highly interactive exhibition methods that engage the audience. For example, in the \"Eye of the Machine: Frame of Choice\" exhibition at the National Taiwan Normal University (NTNU) Art Museum, part of the Ministry of Education's \"Advanced Display Technology Integration and Promotion Project\", AI-generated dynamic images interact with the audience, showcasing the contrast between war and harmony. AI can also provide personalized exhibition content based on audience preferences and behavior data. By analyzing historical browsing records and feedback, AI can recommend specific artworks or exhibition areas, enhancing audience engagement and satisfaction. The exhibition process can help analyze audience behavior data, optimize exhibition design and management, and significantly contribute to art education and creative inspiration.  
With the widespread adoption of 5G in the future, devices carried by visitors, display cabinets, playback devices, monitors, and sound sensors in the exhibition halls will likely have the capability to collect data on audience behavior and on-site conditions. AI can efficiently process large amounts of data, providing precise audience behavior analysis and exhibition effectiveness evaluation. For example, by analyzing visitor flow data and preferences, museums can optimize exhibition design and marketing strategies, thus improving audience experience and attendance and serving as a reference for future curation or on-site management of art museums [12; 13; 14].
## 4 RTK vs. PPP  
As seen previously, scintillation conditions and ionospheric disturbances can vary spatially and temporally. If the base station and the rover experience different ionospheric conditions, the differential corrections may not accurately compensate for the scintillation effects, leading to degraded RTK performance. On the other hand, PPP relies on corrections obtained from a network of globally distributed stations and does not require local base station infrastructure. While PPP is not immune to ionospheric disturbances, the use of multi-frequency measurements to directly observe and eliminate ionospheric error, along with advancements in receiver firmware, contributes to enhanced accuracy and availability during scintillation.  
RTK positions in the following example were obtained using RTCMV3 messages (a protocol used for transmitting base observations to the rover), quad-frequency and a baseline length of 6.5 km. The PPP positions were obtained using TerraStar-C PRO Correction Services from NovAtel. The position error of the RTK and PPP solutions is shown in Figure 10. During a high-scintillation event between 570,000 and 600,000, PPP shows larger errors than outside the scintillation period but still outperforms the RTK solution during this time. These results align with expectations and are consistent with other findings documented in [10]. Outside the scintillation period, RTK offers equivalent accuracy without the convergence time associated with PPP.  
Figure 9: C/N0 rapid fading vs. carrier phase double-differenced measurements.
# ResADM: A Transfer-Learning-Based Attack Detection Method for Cyber-Physical Systems
## 2 Background
### Residual Network  
The residual network, also known as ResNet, is a classic convolutional neural network that utilizes residual blocks to implement skip connections in stacked CNN networks. This approach mitigated the problem of feature loss and addressed the issues of vanishing and exploding gradients by directly passing the previous layer's features on to the subsequent layers, facilitating easier training and optimization [11]. Residual networks have found wide applications in tasks such as image recognition and text classification. The structure of ResNet is illustrated in Figure 1, where \(x\) represents the input; the weight layer refers to the convolutional layers responsible for feature extraction; \(F(x)\) denotes the result obtained after convolution; \(x\) identity represents the identity mapping, where the input data are directly passed through the residual connection without any transformation to ensure the consistency of input dimensions, enabling smooth residual connections; \(F(x)+x\) indicates the combination of the output from two convolutional layers with the previous convolutional layer; and ReLU (rectified linear unit) is the activation function.  
Some studies have utilized ResNet as a foundation and enhanced the richness and the accuracy of the feature extraction by incorporating cross-layer transfer and embedding modules [12]. This approach has successfully addressed the challenges associated with detecting train bottom parts. Furthermore, there has been research that combined auto-encoder networks with residual networks to create a novel network intrusion-detection method [13]. In this method, pre-processed data were used to extract features through an auto-encoder network by reconstructing the reconstruction error. The extracted fea  
Figure 1: Structure of the residual block.  
tures were then employed to train a specifically designed residual network, which was subsequently used for classification detection on a test set using the trained model. The experimental results demonstrated that this approach achieved good performance in terms of accuracy, a true-positive rate, and a false-positive rate.  
However, network intrusion-detection work has often faced a scarcity in the training data for business systems and intelligent security devices. This limitation has presented challenges to deep-learning-based intrusion-detection models when applied to new cyber-physical-system applications, as their generalization capability has been weak. The distribution of network traffic samples in a new CPS application scenario might differ from the training dataset, leading to reduced effectiveness. Therefore, enhancing the generalization capability of network-attack-detection models is a critical issue that necessitates further research and improvements in model design. By addressing this challenge, these models can be more effective and reliable in new CPS scenarios.
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 5 KAFIN for Option Pricing
### KAFIN for Option Pricing  
For European option pricing, KAFIN leverages the fundamental principles of the Black-Scholes (BS) model while incorporating data-driven neural network techniques to enhance prediction accuracy and model robustness. This case study illustrates how KAFIN can be employed to price European options effectively as a case in point for finance applications.  
In the context of European option pricing, the KAFIN model begins by defining the financial differential operator based on the BS partial differential equation (PDE). The BS equation for a European call option is given by  
\[f_{BS}=\frac{\partial C}{\partial t}+\frac{1}{2}\sigma^{2}S^{2}\frac{\partial ^{2}C}{\partial S^{2}}+rS\frac{\partial C}{\partial S}-rC=0 \tag{16}\]  
where \(C\) represents the option price, \(S\) is the underlying asset price, \(\sigma\) is the volatility, \(r\) is the risk-free interest rate, and \(t\) denotes time. Equation (16) serves as financial governing constraints in PDE form.  
By applying (12)-(15), we build the KAFIN to learn the KAR to decompose the option price \(C(S,t)\) using KAR into a series of simpler functions:  
\[C(S,t)=\sum_{i=1}^{N}g_{i}(h_{i}(S),t), \tag{17}\]  
where \(g_{i}\) are univariate functions, and \(h_{i}(x)\) denote the mappings of these functions with respect to \(S\) and \(t\). This decomposition allows for a more granular and interpretable representation of the option price dynamics. The neural network component of KAFIN is designed to learn the parameters of these univariate functions and their interactions. The network is trained using a composite loss function that includes initial value loss, boundary loss and financial governing constraint loss.  
The financial governing constraint loss \(\mathcal{L}_{\text{financial}}(\theta)\) is built based on the (16) as a constraints to enforces the BS PDE constraints within the domain.
## 4 Materials and Methods
### EO Composition Analysis and Sample Preparation  
The EO component identification was performed using a coupled Gas Chromatography-Electron Impact Mass Spectrometry (GC-EIMS) instrument whereas, for quantification, a gas chromatography with a flame ionization detector (GC-FID) was employed. For the analyses, a sample of the EO from each distillation (10 \(\upmu\)L) was diluted with 990 uL of CH\({}_{2}\)Cl\({}_{2}\) (dichloromethane).
## 3 Technologies of Monitoring in Montado
### Precision Agriculture
#### 3.2.4 From the Establishment of Management Zones (MZ) to Variable Rate  
Technologies (VRT)  
Generally, agricultural producers apply identical numbers of production factors, such as fertilizers or correctives, throughout the plot; in a plot, the needs of the soil/crop can be very different depending on the physical-chemical characteristics of the soil, topography, and specific weather conditions [6]. According to [PERSON] et al. [131], although the soil ECa can be used to help define soil MZ, it must be considered that its correlations with soil fertility are variable and sometimes low. [PERSON] and [PERSON] [79] also state that the definition of MZ (by measuring the ECa of the soil) is only sometimes correct, especially for excessively and moderately drained soils. Furthermore, topography plays a significant role in influencing the spatial variation of ECa [132].  
Agricultural producers prefer this approach of treating the plots homogeneously, as it is quicker and easier to implement. However, uniform application leads to the economic inefficiency of these production systems and high environmental costs [6], since it does not consider the spatial variability of the soil [79]. In this regard, the concept of Management Zones (MZ) arises, which consists of managing areas of agricultural fields in a differentiated way, depending on the needs and physical-chemical characteristics of the soil. MZ are a form of PA, whose main objective is to decide on the quantities of production factors to be applied in a given situation, depending on the soil and the crop [79]. This concept is different than the traditional production method since it manages the variability of the plots to increase productivity and efficiency by using production factors, not forgetting environmental protection [133]. According to [PERSON] et al. (2004), cited by [6], the MZ brings economic efficiency and a reduction in production factors to the producer. These production factors are only applied where and when needed in each zone [6]. Therefore, according to those authors, when comparing the cost-effectiveness between variable rate technology (VRT) and uniform application, there is a clear advantage for the former, in different situations and with different fertilizers. Each MZ, according to [PERSON] et al. [91], becomes a differentiated management unit in which profitability can be increased, reducing production factors, through VRT.  
Figure 5 is a summary of the use of different expedient technologies for soil and pasture monitoring.  
Table 21 (Appendix A) summarizes the works mentioned in Section 3, where one can observe which technology or sensor was used, the general and specific application, the type of sensor used, and the geographic location where each experimental study occurred.  
Figure 7 shows the percentages of studies cited in Section 3, referring to different components of Montado (pasture, soil, grazing, and trees, among others), monitored and characterized with different proximal and/or remote sensors.
## 6 Security Analysis  
Here, we evaluate the security of our scheme via formal and informal analyses. The informal analysis considered attack scenarios to demonstrate the robustness of the scheme. The formal analysis verified mutual authentication using \"BAN logic\", provided a security proof for the session key based on the \"RoR model\", and validated the resistance to attacks via the \"AVISPA simulation tool\".
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Appearance Feature Extraction Module
#### 3.2.2 MGH Framework  
The Multi-Granular Hypergraph (MGH) framework, introduced by [PERSON] et al. in [7], represents an advanced solution for video-based person Re-ID. MGH addresses common issues like occlusions and misalignment in videos by leveraging spatial and temporal features at multiple levels of granularity.  
MGH constructs hypergraphs for different granularity levels, each representing a pair consisting of a set of node features at a specific granularity level and a set of hyperedges, capturing temporal relationships (correlations) among these features. These hyperedges are obtained by identifying the nearest neighbors of each node in the graph within temporal ranges based on the affinity of temporal features, e.g., neighbors that share similar temporal characteristics. These hyperedges represent short-term correlations between temporally adjacent features, as well as mid- and long-range correlations across different temporal lengths.  
MGH employs a HyperGraph Neural Network (HGNN) for feature learning and propagation. Within this network, information is gathered across hyperedges, and node features are updated iteratively.  
Within this framework, an attentive hypergraph feature aggregation mechanism is used to enhance the discriminative power of features. It combines weighted node features from all hypergraphs to create the final video representation.  
For the purpose of appearance feature extraction, the MGH framework aggregates complex multi-granular features into a unified representation denoted as \(\mathbf{z}^{i}_{\text{MGH}}\) for a person identified by ID \(i\) in a video, expressed as follows:  
\[\mathbf{z}^{i}_{\text{MGH}}=\phi^{\text{MGH}}(\mathbf{A}^{i}) \tag{5}\]  
where \(\mathbf{A}^{i}\) is the tensor that contains individual appearance data.
## 2 Design and Methods  
### Structure Design of Transparent EMI Shielding Window  
The sheet resistance of a conductive film provides a reliable measure of its EMI shielding ability and can be determined by the skin depth (\(\delta\)) and thickness (t) of the thin film [27]. The skin depth is defined as [28]:  
\[\delta=\frac{1}{\sqrt{\pi\mu\sigma\mathrm{f}}} \tag{1}\]where \(\mu\) and \(\sigma\) are the permeability and conductivity of the film, respectively. Usually, the thickness of the metal mesh film is designed to be greater than the skin depth in order to obtain higher shielding effectiveness. However, increasing the thickness only improves shielding performance at low frequencies [9]. For the mesh coating with a thickness much less than the skin depth, the SE is dominated by the reflection of the incident plane wave at the air-mesh interface. When the skin depth decreases, the shielding effectiveness also decreases accordingly [29]. However, the skin depth of a conductive film decreases as the frequency gets higher. A thicker metal mesh can reduce the sheet resistance of the mesh coating and enhance the EMI shielding performance at lower frequencies [30]. However, it contributes a little to the improvement of EMI SE at higher frequencies. Based on a comprehensive consideration of the effects of mesh aperture structure, sheet resistance, and skin depth on shielding effectiveness, it is possible to alleviate the frequency dependence of shielding effectiveness by designing the proper mesh thickness for achieving a balanced shielding effectiveness in a wide frequency band. In this study, the mesh thickness is several hundred nanometers, much thinner than the skin depth of the metallic film.  
The transparent EMI shielding window, shown schematically in Figure 1a, is composed of a sapphire substrate and two layers of metal mesh films on its surface. Sapphire is chosen for its excellent physical properties, including high infrared transparency, good thermal conductivity, high hardness, and the ability to withstand high temperatures [31]. The thickness of the Au mesh layer is designed to be between 200 and 300 nm, which is sufficient to provide good conductivity for effective microwave reflection. The Cr mesh is used as the adhering layer and is much thinner than the Au mesh. In addition to the mesh-based shielding films, a layer of Al\({}_{2}\)O\({}_{3}\) is used as a covering layer to protect the mesh film from erosion in harsh environments.  
The design of the mesh pattern can significantly impact the optical properties of transparent shielding materials. Conventional meshes, such as square metal grids, tend to produce poor imaging quality due to centralized diffraction distribution [32]. To reduce the concentration of high-order diffraction and minimize the impact of imaging caused by the mesh shielding layer, the metal mesh is designed with irregular patterns, as shown in Figure 1b. The irregular mesh patterns are designed with an equivalent period of 110 \(\upmu\)m and a linewidth of 2.5 \(\upmu\)m, which allows for high IR transmittance due to the reduced obscuration ratio.
## 2 Outdoor Positioning in the IoT Ecosystem
### Common LoRaWAN-Based Localization Architectures
#### 2.4.1 Central Architecture (Cloud-Based)  
Cloud computing emerges as the key platform for localization system data storage, processing, and analytics due to its simplicity, availability, and scalability. In cloud-based localization systems, when gateways receive data from end nodes, they send data to a network server. Network servers aggregate and forward data to the cloud layer through the Internet. Cloud layer, at first, discovers the adopted method by system and user agent for end-node localization. Then, the end-node localization and user interfaces are provided. Cloud-based method decreases the localization latency by providing storage, computational, and processing power. Therefore, received data will be accessible for real-time positioning and monitoring. Cloud-based architecture is shown in Figure 2.  
Figure 1: Performance comparison of LPWAN technologies. Adapted from [34].
## 4 Results and Analysis
### Results and Analysis of Experiments
#### 4.2.3 The Effects on the Intensity of User Emotions in Real Environments  
The emotional intensity of user emotions after they encountered interaction errors in real environments was investigated. The emotional categories of happiness, surprise, anger, and disgust were considered. The data for each category show normal distributions and pass homogeneity of variance. One-way analysis of variance (ANOVA), followed by a Least Significant Difference (LSD) test, was used to compare the general intensity of emotions, respectively, obtained after three interaction errors. The results show that there are no significant differences among the general intensity of happiness, surprise, and disgust but significant differences in anger (\(p<0.05\)) when users encounter repeated interaction errors. In addition, all the \(p\)-values are larger than 0.05 when considering the effects of feedback and contexts. This reveals that the types of feedback and contexts do not affect the general intensity of each emotion.
## Appendix A Formal Definitions of TV-HARM  
### A.1. Mathematical Representation of TV-HARM  
This appendix provides the formal definitions of TV-HARM, detailing its hierarchical structure and the extension for incorporating protocol vulnerabilities. The original definitions of TV-HARM were based on the hierarchical integration of the attack graph (AG) and attack tree (AT). To enhance its applicability to satellite networks, we extend the model by incorporating protocol vulnerabilities (\(pv_{1}\), \(pv_{2}\),...), addressing its previous limitations in analyzing multisegment security interactions.
# A Deep Learning Framework for Enhanced Detection of Polymorphic Ransomware
## 1 Introduction
### Research Questions and Objectives
#### 1.2.2 Objectives  
This research aims to significantly enhance early ransomware detection by addressing key limitations in existing approaches. Our specific objectives are as follows:  
1. Introduce an enhanced GAN-based data augmentation module with a novel Bi-Gradual Minimax loss function. Existing GAN models struggle to accurately bridge the gap between real and synthetic data distributions, especially with scarce pre-encryption data. Our new loss function, employing a gradual up-weighting coefficient, minimizes this divergence, enabling the generation of highly realistic artificial ransomware attack patterns. This directly tackles data scarcity, a major obstacle to early detection, and is expected to significantly improve accuracy.
2. Propose an Incremental Mutual Information Selection (IMIS) technique to enhance model adaptability. Traditional feature selection methods often become outdated due to the dynamic nature of ransomware. IMIS dynamically reassesses feature relevance as new data integrates into our custom DBN-based model. This on-the-fly adaptation allows continuous learning and improved detection accuracy against evolving threats, ensuring long-term effectiveness.
3. Present an Uncertainty-Aware Dynamic Early Stopping (UA-DES) criterion and method to optimize DBN training. Overfitting is a common challenge in DBN training, particularly in determining optimal epochs. Existing dynamic approaches often fall short due to the inherent variability of ransomware behavior. By leveraging Bayesian approximation to accurately estimate uncertainty, UA-DES aims to determine the optimal number of training epochs, leading to improved model generalization, best-in-class prediction performance, and effective overfitting prevention.