esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions
### Proposed Research Roadmap and Future Directions
#### 5.6.2 Strategic Roadmap for Integrated Cyber-Physical Systems  
This section presents the development stages of holistic systems where the mentioned technologies are used in an integrated manner. The reflections of this integration on cyber-physical systems are classified by years with a focus on functional gains. The main emphasis is on when multi-technology collaborative systems will acquire specific competencies [3; 10; 144]. _Strategic Roadmap and Future Perspective_: The integration of IoE, Blockchain, AI, and Quantum-Resilient Security Protocols is a strategic necessity in the evolution of cyber-physical systems [8; 64]. The development of these technologies can be evaluated through a multi-stage roadmap. Between 2025 and 2028, the groundwork phase focuses on early integration and awareness. Blockchain-based ABAC mechanisms are piloted in small-scale IoE environments [11; 82; 83]. Edge AI-based anomaly detection is experimentally deployed at the device level [25; 59; 145]. Hybrid architectures incorporating post-quantum cryptographic primitives begin initial testing [54; 180; 181]. The main challenges of this period include the lack of standardization, regulatory uncertainties, and protocol incompatibilities [10; 14; 38]. Additionally, FL [23; 144], lightweight consensus algorithms [73; 178; 179], and homomorphic encryption [46; 177] are explored to balance privacy and computational requirements. Between 2029 and 2032, the focus shifts to widespread adoption and interoperability, marking a period of maturity and sectoral expansion. Privacy-preserving AI systems based on FL are integrated into corporate IoE platforms [23; 144; 170]. DID and SSI solutions are implemented across various sectors [84; 85; 160]. Blockchain-AI integrations find concrete use cases in healthcare [88], smart cities [142], and energy infrastructures [8; 158]. Although post-quantum digital signatures do not become fully widespread, they begin to deliver stable test results in regulated environments [35; 52; 120]. The increasing need for interoperability directs attention toward context-aware access policies [171] and semantic AI solutions [136; 164]. From 2033 to 2036 and beyond, the focus moves to adaptation and autonomy, achieving full integration and intelligent systems. Fully PQC-enabled blockchain protocols start to be established [54; 67; 180]. Edge AI becomes the fundamental security layer for real-time autonomous systems [25; 144; 145]. Cross-domain data sharing is facilitated through privacy-preserving computation techniques [47; 176] and semantic blockchain networks [164; 167]. Quantum-assisted behavioral modeling enables advanced threat prediction systems [130; 164]. Beyond 2037, the proactive and predictive security phase begins. Cyber-physical systems evolve into self-healing architectures that are resistant to quantum attacks [144; 161]. Locally trained quantum-assisted AI models are used in defense systems at the edge [130; 136].
# Understanding and Classifying Permanent Denial-of-Service Attacks
## 4 Design of the Framework
### PDoS Severity Score Based on Combined Criteria
#### 4.9.1 Class 1 (Minor)  
Class 1 has the following characteristics:  
* Impact Effect: Slow;
* DoS Effect Duration: Temporary;
* Recovery Action: Simple actions like physical restart or software update.  
Class 1 represents the least severe category, characterized primarily by its less aggressive nature. This class encompasses attacks that induce a slow impact effect, resulting in temporary disruption that can often be rectified with basic corrective actions such as a physical restart or a standard software update. Examples within this class, such as Rovnix and Olmasco, demonstrate attacks that, while inconvenient, do not necessitate extensive recovery procedures. Class 1 PDoS attacks, therefore, while requiring vigilance, do not posesubstantial long-term risks to the target systems, enabling quicker recovery and minimal impact on operational continuity.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 4 Experiments and Evaluation
### Evaluation Using TUM RGB-D Dataset  
Our algorithm was compared with ORB-SLAM2 and DS-SLAM using the TUB RGB-D dataset. Tables 3 and 4 show the absolute trajectory error and relative pose error of the three algorithms in different scenarios. Compared with ORB-SLAM2, our algorithm has significantly improved. Similarly, comparing with DS-SLAM, which has dynamic object processing capability, our algorithm also has some improvement.  
To visualize the estimated trajectory data, the trajectory data of freiburg3_walking_halfsphere scenario and freiburg3_walking_xyz scenario are visualized as shown in Figure 11.  
Real-time response is an important factor in evaluating vSLAM systems. In this part, we experimentally evaluate the average frame rate (FPS) of different systems. Table 5 shows the average FPS of each sequence used by ORB-SLAM2 and Ours pairs for the evaluation of the TUM dataset. Ours achieves an average frame rate of 30.38 FPS, while ORB-SLAM2 achieves 25.24 FPS.  
In addition, the BiSeNet V2 network performs semantic segmentation of images much faster than the semantic segmentation network used in DS-SLAM, so Ours outperforms other vSLAM systems that use GPU acceleration, for example DS-SLAM (13.08 FPS on fr3-w-h).
# Human Activity Recognition by the Image Type Encoding Method of 3-Axial Sensor Data
## 3 Image Type Encoding Method of the 3-Axial Sensor Data
### Brightness Intensity Distribution Pattern Transformation
\(A_{n}\) and \(G_{n}\) in Figure 3 represent the 3-axial dataset of the accelerometer and gyroscope sensors with DSS size, respectively, while \(SA_{n}\) and \(SG_{n}\) represent each component of the 3-axial dataset serialized into linear form. Applying Equation (2) generates a \(SBIDP_{n}\) of size \((DSS\times 3)\times(DSS\times 3)\). The generated \(SBIDP_{n}\) exhibits greater geometric spatial patterns than Figure 2, which uses 1-axial data. This is clearly evident in the dimensional expansion using actual 3-axial data. The generated \(SBIDP_{n}\) exhibits greater geometric spatial patterns than Figure 2, which uses 1-axial data. This is clearly evident in the dimensional expansion using actual 3-axial data. Figure 3: Example of BIDP visualization by 3-axial raw data serialization: (**a**) A\({}_{n}\): acceleration dataset; G\({}_{n}\): gyro dataset; (**b**) SA\({}_{n}\): serialized acceleration data; \(\mathbf{SG}_{n}\): serialized gyro data; (**c**) \(SABDIP_{n}\): \(BIDP\) of serialized acceleration data; \(SGBDP_{n}\): \(BDIP\) of serialized gyro data; (**d**) \(SBIDP_{n}\): serialized \(BDIP\). Figure 4 shows an example of \(SBIDP\) for the 18 types of physical activities presented by the WISDM dataset using the actual 3-axial raw accelerometer and gyroscope data. All physical activities show dot patterns while some linear patterns can be seen in the inner part due to the effect of the color space caused by the different ranges of brightness. The line patterns inside the image represent information expressed from the different strength values of each axis, which can be recognized as the spatial characteristics of the physical activities. These characteristics are emphasized to a greater extent depending on the magnitude of the differences in the strength values. Figure 5 shows the magnified \(SBIDP\) results for physical activity labels 3, 7, and 17 from Figure 4. While all patterns may appear rectangular or magnified, different patterns are expressed based on brightness. Therefore, these patterns are used as classification features for physical activities. Figure 4: \(SBIDP\) example of 18 activities in the WISDM dataset. Figure 5: Example of the magnified \(SBIDP\) of some samples in Figure 4.
# Swarm-Intelligence-Centric Routing Algorithm for Wireless Sensor Networks
## 4 Performance Evaluation
Any increase in the average node speed, number of connections, or number of nodes tends to reduce the packet delivery rate, and this can be attributed to the high node mobility that causes frequent link damages, owing to which, several packets are discarded. Additionally, the increase in connection and node counts causes an increase in signal-strength traffic interference, which in turn increases the possibility of link damage.
## 3 The Proposed Model
### Process Involved in CSO-FS Technique  
At the initial stage, the presented FSSML-EDM model incorporates the design of the CSO-FS technique for electing feature subsets. The CSO algorithm is chosen over other optimization algorithms due to its simplicity and high parallelism. The CSO simulates the chicken movement and the performance of the chicken swarm; the CSO is explained as follows: CSO has several groups, and all the groups have a dominant rooster, some hens, and chicks [15]. The rooster, hen, and chick from the group are found dependent upon their fitness value. The rooster (group head) is the chicken which is an optimum fitness value. However, the chick is the chicken which has the worse fitness value. The majority of chickens are hens and it can be selected arbitrarily to stay in that group. The dominance connection and mother-child connection from the group remain unaltered and upgrade during (G) time steps. The movement of chickens are expressed under the equation which utilizes to the rooster place upgrade provided by Equation (1):  
\[X_{ij}^{r+1}=X_{i,j}^{t}\times\Big{(}1+randn\Big{(}0,\ \sigma^{2}\Big{)}\Big{)} \tag{1}\]  
whereas:  
\[\sigma^{2}=\begin{cases}1&\text{if }f_{i}\leq f_{k}\\ \exp\Big{(}\frac{f_{k}-f_{i}}{|f_{i}+\varepsilon|}\Big{)}&\text{Otherwise}\end{cases}\]  
In which \(k\in[1,\ N_{r}]\), \(k\
eq i\), and \(N_{r}\) refers the amount of chosen roosters. \(X_{i,j}\) signifies the place of rooster number \(i\) in \(j\)th dimensional under \(t\) and \(t+1\) iteration, \(randn\ (O,\ \sigma^{2})\) is utilized for generating Gaussian arbitrary numbers with mean 0 and variance \(\sigma^{2}\); \(\varepsilon\) refers to the constant with minimum value; and \(f_{i}\) is the fitness value to the equivalent rooster \(i\). The equation which utilizes the hen place upgrade is provided by Equations (2)-(4):  
\[X_{i,j}^{t+1}=X_{i,j}^{t}+S_{1}randn\Big{(}X_{r1,j}^{t}-X_{i,j}^{t}\Big{)}+S_{ 2}randn\Big{(}X_{r2,j}^{t}-X_{i,j}^{t}\Big{)} \tag{2}\]  
In which:  
\[S_{1}=\exp\left(\frac{f_{i}-f_{r1}}{|f_{i}|+\varepsilon}\right) \tag{3}\]  
and:  
\[S_{1}=\ \exp\ (f_{r2}-f_{i}) \tag{4}\]  
where \(r1\), \(r\in[1,\ \dots,\ N]\), \(r1\
eq rr\) refers to the index of the rooster, but \(r2\) implies the chicken in the swarm which is a rooster or hen and a uniform arbitrary number is created by \(randn\). Finally, the equation that utilizes the chick place upgrade is provided by Equation (5):  
\[X_{i,j}^{t+1}=X_{i,j}^{t}+FL\Big{(}X_{m,j}^{t}-X_{i,j}^{t}\Big{)},\ FL\in[0,2] \tag{5}\]  
where \(X_{m,j}^{t}\) signifies the place of \(i\)th chick mother.
# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
###### Abstract
Many different data can be redundant or omitted according to the user's interest or device type. The main concern in a health platform is to provide a flexible and continuous service in a situation where data are collected differently according to the users' situation [8]. In a situation where various devices have been developed and distributed to people, people may feel confused due to overlapping information. Accordingly, research on a method for integrated management of information is needed. Therefore, if user data are redundant or omitted, representative values are used to remove duplicate data and estimate omitted values. A representative value can be selected, such as the mean, median, mode, quartile, percentile, or trimmed mean [9]. An omitted value is represented with a missing value or null, which is estimated using methods such as collaborative filtering, mean imputation, regression analysis, and neural networks [10]. In this study, we propose a recurrent neural network-based multimodal deep learning for estimating missing values in healthcare. This RNN-based estimation is achieved by using two proposed techniques. The first is to analyze the manner in which data duplication caused by multimodality influences the estimation of a missing value. The second technique is to evaluate the missing value estimation method that uses variable-by-variable RNN learning and multimodal deep learning. In the case of data duplication by heterogeneous devices in the user's healthcare environment, it is provided using a representative value processing method. If a missing value occurs according to the user environment, it is imputation with the predicted value from the RNN for each variable. The contributions of the proposed method are as follows:  
* It is possible to determine the direction of data integration in an environment where the types of wearable devices are diversifying and contribute to enabling continuous service to users. * This is a method for dealing with the data duplication that occurs in a heterogeneous healthcare environment. * Previous studies on the imputation of missing values were conducted on one device or one data set in healthcare. * The proposed method is more suitable for healthcare environments by the imputation of missing values in a structure where different devices complement each other. * The proposed method can flexibly integrate the data collected in the perfume healthcare environment. This study consists of the following: Section 2 describes the research trends of soft computing and the data characteristics in heterogeneous health platforms. Section 3 describes the proposed RNN-based multimodal deep learning for estimating missing values in healthcare. Section 4 describes the experimental results and the performance evaluation. Finally, Section 5 draws conclusions.
# Wireless Energy Harvesting Two-Way Relay Networks with Hardware Impairments
## 3 Ergodic Capacities Analysis
### Ergodic Capacity of DF Relaying  
In order to determine the ergodic capacity of the DF protocol for such an EH-TWR networks, we need to evaluate the ergodic capacity of the source-to-relay link \(\mathbb{C}_{ir}\), the relay-to-destination link \(\mathbb{C}_{ri}\), and the multiple-access constraint capacity \(\mathbb{C}_{\mathrm{mac}}\). Based on Lemma 2, it is non-trivial to obtain the CDFs of each SNDRs: \(F_{Y_{iR}},F_{Y_{Ri}}\), and \(F_{Y_{\mathrm{MA}}}\) first.  
Theorem 3.1: _Denote \(X=\gamma_{1}\), \(Y=\gamma_{2}\), the CDFs of \(\mathrm{Y}_{iR}\), \(\mathrm{Y}_{Ri}\), and \(\mathrm{Y}_{\mathrm{MA}}\) is given by_  
\[F_{Y_{iR}}(t)=1-e^{-\frac{tA_{i}}{(1-\rho)-t(1-\rho)k_{1}^{2}+\kappa_{2}^{2}}},\quad 0<t<\frac{1-\rho}{(1-\rho)\kappa_{1}^{2}+\kappa_{2}^{2}} \tag{21}\]  
\[F_{Y_{Ri}}(t)=1-e^{-A_{i}\sqrt{\frac{tV_{i}}{(1-\kappa^{2})\eta\rho}}}-A_{i} \zeta_{i},\quad\quad 0<t<\frac{1}{\kappa^{2}} \tag{22}\]  
_For \(F_{Y_{\mathrm{MA}}}(t)\), there are two cases based on the relative values of \(A_{1}\) and \(A_{2}\). When \(A_{1}=A_{2}\), \(0<t<\frac{1-\rho}{(1-\rho)\kappa_{1}^{2}+\kappa_{2}^{2}}\)_  
\[F_{Y_{\mathrm{MA}}}(t)=1-e^{-\frac{tA_{2}}{\bar{\rho}-t(\kappa^{2}-\rho\kappa_ {1}^{2})}}-\frac{tA_{2}}{\bar{\rho}-t(\kappa^{2}-\rho\kappa_{1}^{2})}e^{-\frac {tA_{1}}{\bar{\rho}-t(\kappa^{2}-\rho\kappa_{1}^{2})}}. \tag{23}\]  
_When \(A_{1}\
eq A_{2}\), \(0<t<\frac{1-\rho}{(1-\rho)\kappa_{1}^{2}+\kappa_{2}^{2}}\)_  
\[F_{Y_{\mathrm{MA}}}(t)=1+\frac{A_{1}}{A_{2}-A_{1}}e^{-\frac{tA_{2}}{\bar{\rho} -t(\kappa^{2}-\rho\kappa_{1}^{2})}}-\frac{A_{2}}{A_{2}-A_{1}}e^{-\frac{tA_{1} }{\bar{\rho}-t(\kappa^{2}-\rho\kappa_{1}^{2})}} \tag{24}\]  
_where_  
\[\zeta_{i}=\int_{0}^{\sqrt{\frac{tV_{i}}{(1-\kappa^{2})\eta\rho}}}e^{-\left((A _{1}-A_{2})x+\frac{tA_{i}V_{i}}{(1-\kappa^{2})\eta\rho},\frac{1}{\bar{\rho}} \right)}dx \tag{25}\]  
_and \(i,\bar{\gamma}\in\{1,2\},i\
eq\bar{\gamma},\bar{\rho}=1-\rho\)._  
Proof.: The proof is given in Appendix A.  
With the derived CDFs: \(F_{Y_{iR}}\), \(F_{Y_{Ri}}\), and \(F_{Y_{\mathrm{MA}}}\), we can further derive their ergodic capacities as shown in the following Theorem 2 based on Lemma 2.  
_and_  
\[\hat{\xi}_{i}=\int_{0}^{\sqrt{\frac{zV_{i}}{x^{2}qp}}}e^{-\left((A_{1} -A_{2})x+\frac{zA_{i}V_{i}}{\kappa^{2}qp},\frac{1}{z}\right)}dx \tag{31a}\] \[E_{1}(z)=\int_{z}^{\infty}\frac{e^{-z}}{z}dz\] (31b) \[ci(z)=-\int_{z}^{\infty}\!\!\frac{\cos z}{z}dz,\,si(z)=-\!\!\int_ {z}^{\infty}\!\!\!\frac{\sin z}{z}dz\] (31c) \[\Lambda_{i}=\frac{A_{i}^{2}V_{i}}{\eta\rho}. \tag{31d}\]  
Proof.: The proof is given in Appendix B.  
Using the obtained ergodic capacities of each link, the total ergodic capacity can be obtained by  
\[\mathbb{C}_{e}^{\rm DF}=\min\left(\mathbb{E}\{I_{1}\}+\mathbb{E}\{I_{2}\}, \mathbb{E}\{R_{\rm MA}\}\right), \tag{32}\]  
where \(\mathbb{E}\{I_{1}\}=\min(\mathbb{E}\{C(Y_{1R})\},\mathbb{E}\{C(Y_{R2})\})\), \(\mathbb{E}\{I_{2}\}=\min(\mathbb{E}\{C(Y_{2R})\},\mathbb{E}\{C(Y_{R1})\})\).
## 1 Introduction
This document aims to provide a review of the state-of-the-art in the domain of satellites land products validation, with a focus on current and future European Space Agency (ESA) optical imaging sensors and to identify the current challenges, the needs for improvements and the data gaps. Furthermore, the on-going efforts for tackling such gaps are reported and discussed. The status and needs were gathered in a series of international meetings with the science community, in particular in a recent land validation workshop [3]. The overarching objective of this document is to set the stage for building a long-term strategy for ESA optical satellite land products validation, which will help to define and prioritise the upcoming Cal/Val activities in coordination with the other Space Agencies and in close liaison with the Committee on Earth Observation Satellite Land Product Validation (CEOS-LPV) sub-group. The document starts in Section 2 by introducing the background and the international context in which the ESA operates to coordinate and steer the effort in land Cal/Val activities. Section 3 provides the underlying concepts and terminology, which will be referred to throughout the paper. Section 4 illustrates the current status and challenges in Cal/Val, while Section 5 presents the readiness level in terms of infrastructure and methods and the overall strategy to fill the identified gaps. Concluding remarks are presented in Section 6.
## 4 Results and Discussion
### pH Sensitivity  
The pH sensitivity of the PEC film is shown in Figure 5. When the buffer solution pH is low, the functional \(-\)NH\({}_{2}\) groups of chitosan are protonated (\(-\)NH\({}_{3}^{+}\)), and most of the carboxyl groups in alginate are in the form of \(-\)COOH. Therefore, in a solution with pH of 4 (Acidic medium), charged chitosan chains repulse each other, leading to the expansion of the films and increased swelling ratios. As the pH increased, the degree of ionization of chitosan decreased and the carboxylic acid groups  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **PEC 0.4** & **TM-PEC \%0.4** & **PEC 1** & **TM-PEC \%1** \\ \hline WVTR (g/m\({}^{2}\)/day) & 2189.2 \(\pm\) 35 & 2016.99 \(\pm\) 27 & 1929.7 \(\pm\) 68 & 1922.6 \(\pm\) 45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Water vapor transmission rate.  
Figure 4: Swelling ratio of PEC films.  
in alginate became ionized. Electrostatic interactions between \(-\)NH3\({}^{+}\) and \(-\)COO' became stronger step by step, and more compressed films with lower swelling ratios formed [32]. By increasing the pH value from 4 to 10, the swelling ratio decreased (\(p>\) 0.05). Because of the higher amount of chitosan in TM-PEC %1 film, its swelling ratio was much higher (about 800%) at pH = 4. However, by increasing the pH, it became denser and more compact leading to a low swelling value (37% for pH = 10). This decrease was less (\(p>\) 0.05) for the TM-PEC %0.4 film with a lower chitosan content (from 170% for pH = 4 to 24% for pH = 10).
## 1 Introduction
### Bibliometric Analysis and Research Clusters  
To gain a comprehensive understanding of the current research landscape in wind turbine blade fault detection, a bibliometric analysis was conducted using Scopus and VOSviewer 1.6.20 software. The analysis focused on identifying clusters of related terms and keywords that frequently co-occur in the literature, which reveals the interconnectedness of various research areas. Figure 1 shows the results of this bibliometric analysis, identifying eight distinct research clusters. These clusters highlight the key areas of focus in the field and their interrelationships:  
Advancements in Electrical System Fault Diagnosis (Red): This cluster focuses on topics such as fault diagnosis, vibration analysis, and electric fault currents. Figure 1: Research clusters related to major failure presented in wind turbine blades. Innovations in Detection and Fault Tolerance in Mechanical Structures (Green): This cluster includes research on cracks, sensors, and fault tolerance in mechanical systems. Innovations in Wind Turbine Blade Inspection (Blue): Emphasizes the role of inspection, image analysis, and fracture mechanics in improving blade integrity. Structural Health Monitoring (Yellow): Discusses non-destructive testing methods, including ultrasonic applications for monitoring blade health. Advancements in Composite Materials Design (Purple): Focuses on the development of composite materials and acoustic emission testing for enhanced blade durability. Exploring Mechanical Failures in Composite Structures (Cyan): Examines mechanical failures, fatigue testing, and fracture mechanics in composite structures. Adhesive Joints (Orange): Investigates adhesive technologies and their role in turbine component assembly and maintenance. Advances in Lightning Protection Systems (Brown): Covers research on lightning protection methods and systems, crucial for turbine safety. Table 1 presents the primary faults in wind turbine blades, categorized into the eight clusters identified in the bibliometric analysis. This categorization facilitates the observation of relationships between the various research areas, providing a structured overview of the current state of research and highlighting areas where further exploration is needed. This manuscript aims to contribute to the existing knowledge and understanding of wind turbine blade failure detection by addressing gaps identified in the current literature. The integration of sensor data, advanced data analysis techniques, and visual inspection using unmanned aerial vehicles (UAVs), together with a comparative study of different data analysis models, decision trees, naive Bayes, neural networks, logistic regression, and hierarchical clustering, improves the accuracy, efficiency, and sustainability of wind turbine failure detection. The aim of this holistic approach is to optimize the efficiency and durability of wind energy systems, thus facilitating the transition to a cleaner and more sustainable energy source.