esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Design**} & \multirow{2}{*}{**X (Varying HN)**} & \multirow{2}{*}{**L1**} & \multirow{2}{*}{**L2**} & \multicolumn{3}{c}{**Pearson Correlation Coefficient (R)**} \\ \cline{5-8}  & & & & & **Training** & **Validating** & **Testing** \\  & & & & & **Data** & **Data** & **Data** \\ \hline \multirow{8}{*}{A} & 3 & & & 0.67 & 0.70 & 0.70 \\  & 4 & & & 0.71 & 0.77 & 0.67 \\  & 5 & LOGSIG & PURELIN & 0.64 & 0.65 & 0.82 \\  & 6 & & & 0.71 & 0.53 & 0.74 \\  & 11 & & & 0.62 & 0.61 & 0.50 \\ \cline{2-8}  & 3 & & & 0.66 & 0.67 & 0.56 \\  & 4 & & & 0.65 & 0.65 & 0.68 \\  & 5 & TANSIG & PURELIN & 0.70 & 0.75 & 0.70 \\  & 6 & & & 0.48 & 0.79 & 0.64 \\  & 11 & & & 0.70 & 0.72 & 0.82 \\ \hline \multirow{8}{*}{B} & 3 & & & 0.73 & 0.52 & 0.86 \\  & 4 & & & 0.77 & 0.79 & 0.74 \\  & 5 & LOGSIG & PURELIN & 0.72 & 0.91 & 0.86 \\  & 6 & & & 0.76 & 0.69 & 0.74 \\  & 11 & & & 0.77 & 0.66 & 0.89 \\ \cline{1-1} \cline{2-8}  & 3 & & & 0.70 & 0.65 & 0.50 \\  & 4 & & & 0.78 & 0.76 & 0.77 \\  & 5 & TANSIG & PURELIN & 0.73 & 0.83 & 0.81 \\  & 6 & & & 0.75 & 0.70 & 0.86 \\  & 11 & & & 0.74 & 0.83 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Trained, validated, and tested ANN models for WC60 concrete. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **WC40A** & **WC40B** & **WC40C** & **WC60A** & **WC60B** & **WC60C** \\ \hline PPA & SRS & II & LRS & LRS & SRS & SRS \\ A1 & II & LRS & LRS & LRS & LRS & LRS \\ A2 & SRS & LRS & LRS & SRS & SRS & LRS \\ A3 & SRS & LRS & LRS & LRS & SRS & II \\ NA & LRS & LRS & SRS & LRS & LRS & SRS \\ \hline \hline \end{tabular} Yellow—indicates good sensitivity and can detect damage more than 20%. White—indicates good sensitivity and can only detect damage for less than 20%. Red—indicates poor sensitivity and cannot detect damage.  
\end{table} 
Table 5: Classification of sensitivity for all parameters simulated from optimum ANN models. Figure 12: Sensitivity of all parameters simulated from optimum ANN models.
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 5 Performance Evaluation  
In this section, we evaluate the performance of the proposed scheme using simulation results in MATLAB. In order to improve the clarity, the results are grouped into two subsections. Firstly, we present the simulation results for low traffic and high traffic scenarios to indicate the quick convergence of the proposed scheme towards an optimal solution. Secondly, the comparison is drawn between our proposed solution and existing schemes across (1) QoS parameters (e.g., data rate, BER and stability index) and (2) blocking probability. The impact of these evaluation parameters is investigated by varying the iterations, channels, traffic load and stability index (SoC). The Monte Carlo simulation model is considered to get average results over 500 iterations. PU traffic is modeled using on and off states with the PU arrival rate from 0.0 to 0.6. For the simplicity of SoC computations, the PU history is maintained for 30 slots, which are divided into three regions with weights \(\omega_{1}=0.6\)  
Figure 4: Particle encoding.  
\(\omega_{2}=0.25\) and \(\omega_{3}=0.15\), respectively. The rest of the simulation parameters are presented in Tables 3 and 4.
## 4 Surface Characterizations Based on Surface Susceptibilities
### Extraction of Surface Susceptibilities  
According to Equation (10) and related discussions, there are totally four unknown components of surface susceptibilities for isotropic problems. Since surface susceptibilities are characteristic parameters that do not vary with properties of applied fields, their values can be determined from \(\Gamma\) and \(T\) with different incident angles and polarizations. Derived from Equations (21) and (22), the surface susceptibilities can be extracted through [7]  
\[\chi_{ee}^{xx}=\chi_{ee}^{yy}=\frac{2j}{k_{0}}\frac{\Gamma(0^{ \circ})+T(0^{\circ})-1}{\Gamma(0^{\circ})+T(0^{\circ})+1} \tag{32a}\] \[\chi_{mm}^{xx}=\chi_{mm}^{yy}=\frac{2j}{k_{0}}\frac{\Gamma(0^{ \circ})-T(0^{\circ})+1}{\Gamma(0^{\circ})-T(0^{\circ})-1}\] (32b) \[\chi_{ee}^{zz}=-\frac{\chi_{mm}^{yy}}{\sin^{2}\theta_{0}}+\frac{2 j\cos\theta_{0}}{k_{0}\sin^{2}\theta_{0}}\frac{\Gamma^{\text{TM}}(\theta_{0})-T^{ \text{TM}}(\theta_{0})+1}{\Gamma^{\text{TM}}(\theta_{0})-T^{\text{TM}}(\theta_ {0})-1}\] (32c) \[\chi_{mm}^{zz}=-\frac{\chi_{ee}^{yy}}{\sin^{2}\theta_{0}}+\frac{2 j\cos\theta_{0}}{k_{0}\sin^{2}\theta_{0}}\frac{\Gamma^{\text{TE}}(\theta_{0})+T^{ \text{TE}}(\theta_{0})-1}{\Gamma^{\text{TE}}(\theta_{0})+T^{\text{TE}}(\theta_ {0})+1}, \tag{32d}\]  
where \(\Gamma(0^{\circ})\), \(T(0^{\circ})\), \(\Gamma(\theta_{0})\) and \(T(\theta_{0})\) stand for the reflection and transmission coefficients of normal incidence and oblique incidence with \(\theta=\theta_{0}\) respectively. It is worthwhile to point out that isotropic surfaces have same scattering coefficients \(\Gamma(0^{\circ})\) and \(T(0^{\circ})\) of normal incidence for TE and TM polarizations. The values of \(\Gamma\) and \(T\) can be obtained through either measurements of periodic surfaces or full-wave simulations of unit cells with periodic boundary conditions (PBCs), while the simulation method is utilized in this paper. According to Equation (32), three sets of simulations are required to determine the values of \(\overline{\overline{\chi}}_{ee}\) and \(\overline{\overline{\chi}}_{mm}\):1. one simulation of normal incidence with \(\theta=0^{\circ}\) to obtain \(\Gamma(0^{\circ})\) and \(T(0^{\circ})\);
2. one simulation of TE-polarized oblique incidence with \(\theta=\theta_{0}\) to obtain \(\Gamma^{\rm TE}(\theta_{0})\) and \(T^{\rm TE}(\theta_{0})\);
3. one simulation of TM-polarized oblique incidence with \(\theta=\theta_{0}\) to obtain \(\Gamma^{\rm TM}(\theta_{0})\) and \(T^{\rm TM}(\theta_{0})\).
# Resilience and Resilient Systems of Artificial Intelligence: Taxonomy, Models and Methods
## 4 Applications of AIS That Require Resiliency  
AIS are widely used in various fields of human activity. Certain application areas require special attention in terms of security, reliability, and trustworthiness. Table 1 shows examples of threats and negative consequences of insufficient AIS resilience for the most relevant application areas. Some threats are obvious, while others are less apparent and may require further explanation for the general reader. Nevertheless, all of these threats are real and must be taken into account when designing AIS. Figure 9: The ontological diagram of AIS resilience. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**AIS Application Area** & **AIS Threat Examples** & **Consequences of the Lack of AIS Resilience** \\ \hline \hline \multirow{8}{*}{Industry} & Cyber attacks on edge devices of cyber-physical systems that deploy AI for quality inspection in manufacturing may pose a threat. The resources available to edge devices may not be enough to implement reliable built-in cybersecurity [39; 50]. \\ \cline{2-3}  & Fault injection attacks may affect FPGA chips, on which a neural network is deployed for implementing motion controllers, quality control systems in manufacturing processes, emission monitoring systems, or energy management systems [39; 51]. \\ \cline{2-3}  & Adversarial poisoning attacks can be used to target to the training data of digital twins in complex industrial environments by gaining unauthorised access to the sensors or to the data repository [46]. \\ \cline{2-3}  & Adversarial evasion attacks on intelligent transportation systems, including self-driving vehicles, can take the form of visual patterns painted by attackers on road signs or other objects [47]. These attacks, along with legitimate, naturally occurring out-of-distribution observations, can lead to incorrect decision-making by the intelligent transportation system. \\ \hline \hline \multirow{8}{*}{Security and Cybersecurity} & The performance of an AI-based malware detector can degrade due to concept drift [52]. Concept drift can be caused by evolving malware, changes in user behavior, or changes in system configurations. \\ \cline{2-3}  & The performance of an AI-based Biometric Authentication System can degrade due to concept drift [53]. Concept drift can be caused by various factors such as aging, injury or illness of a person, as well as environmental or technological changes. \\ \cline{2-3}  & Adversarial evasion attacks can be performed against AI-based malware detection or Biometric Authentication systems. For instance, attackers can design unique sunglasses that trick a facial recognition network into granting access to a secure system [2; 54]. \\ \cline{2-3}  & Fault injection attacks can be performed against AI-based Biometric Authentication Systems [2; 55]. \\ \cline{2-3}  & &  
\end{table} 
Table 1: Examples of threats for AIS application areas.
# Navigation Scenario Permutation Model for Training of
## 4 Discussion
### Distributions of Angle and Distance Elements  
The selected elements \"course altering angle at the waypoint\" and \"distance between waypoints\" were extracted under the experimental conditions. The designed experimental condition focused on large ships, which are similar to autonomous ships that are currently under development. The distribution of navigation elements in Figure 6 showed that ships in actual navigation situations tend to use smaller angles or shorter distances frequently than larger ones. Hence, the proportion of values had comparable differences.
## 1 Introduction
### Related Works
#### 1.2.2 A2S Integrated Communications  
With the continuous development of information technology, the spatial scope of information services continues to expand, various space-based, sea-based, and ground-based network services continue to emerge, and the demand for multi-dimensional comprehensive information resources is gradually increasing [27; 28]. An A2S integrated network can provide seamless information services for land, sea, air, and space users, and meets the needs of the future network demand for the full-time all-space communication and network interconnection. A2S integrated communications can realize instant communication and DT on a global scale, connecting the rest of the world and promoting globalization [29]. Through terrestrial radio and optical fiber communication technologies, high-speed and large-capacity DT can be achieved, meeting the needs of various application scenarios [30]. For example, global positioning systems such as aviation, shipping, and vehicles, and data acquisition systems such as weather forecasting and earthquake early warning. Using the terrestrial radio technique, A2S integrated communications can realize global television broadcasting and live video broadcasting, which has greatly improved the coverage and influence of the media [31]. A2S integrated communications can promote the process of information, accelerate the development of the digital economy and intelligent manufacturing, further improve production efficiency, and reduce costs [32]. A2S integrated communication has achieved many results and has broad development prospects. A2S integrated communications play an important role in globalization, informatization, security, and sustainable development. Table 1 is a review of the A2S integrated MIoT.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Short Summary** & **Introduction** \\ \hline
[33] & A survey on the concept of A2S integrated communications. & The basic concept and technical system of the MIoT. \\ \hline
[34] & A survey to explore strengths and application areas. & In the field of communication of A2S integrated networks, it has also been widely used in the past. \\ \hline
[35] & A survey that analyzes key technologies. & The wireless communication technology, network protocol, and wireless design are important technologies. \\ \hline
[36] & A survey to introduce the technical systems of the MIoT. & A2S integrated technologies, including the sensor technology and communication technologies, will work together. \\ \hline
[37] & A survey that explores the drivers and concerns of the development of the MIoT. & The potential role of the MIoT in energy conservation, emission reduction, ship safety, and other aspects is emphasized. \\ \hline
[38] & A survey illustrating the application advantages of this communication system. & It can realize the transmission of data on a global scale, connect the rest of the world, and promote the development of globalization. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the A2S integrated MIoT.
###### Abstract  
Satellite-aerial-ground integrated networks (SAGINs) combined with hybrid free-space optical (FSO) and radio frequency (RF) transmissions have shown great potential in improving service throughput and reliability. The coverage mismatch and rate limitation of traditional hybrid FSO/RF design have restricted its development. In this paper, we investigate the performance of parallel FSO/RF transmissions in SAGIN, taking into account the effect of weather conditions and the quality of service (QoS) of ground users. A three-hop relay system is proposed, where the FSO and RF links jointly provide services to ground users in remote areas. Specifically, considering the limited transmit power of the relay node, we have studied the optimal power allocation between parallel FSO and RF links to further improve system energy efficiency. The performance of the proposed system is evaluated in terms of capacity outage probability, weighted average bit-error rate (BER), and energy efficiency. Moreover, asymptotic capacity outage probability is also derived to obtain more engineering insights. Finally, numerical results show that the energy efficiency of the proposed parallel scheme improves by 30.9% compared to only the FSO scheme at a total transmit power of 15 dBW.  
satellite-aerial-ground integrated network (SAGIN); high-altitude platform (HAP); parallel FSO/RF transmissions; relay link; power allocation; energy efficiency
# Remote-Sensing Image Encryption Algorithm Using the Advanced Encryption Standard
## Theoretical Principle  
### Aes  
The National Institute of Standards and Technology (NIST) recruited a new data encryption standard for symmetric cryptosystem to be instead of the old version, i.e., Data Encryption Standard (DES) on 2 January 1997. Rijndael algorithm has been chosen by NIST and became the new advanced encryption standard, i.e., AES, on 2 October 2000 [16]. This algorithm is designed by [PERSON] and [PERSON]. AES is a block cipher, whose block size is 128 bits. AES core parameters are listed in Table 1. Each round is comprised of the four functions, i.e., SubBytes(\(\cdot\)), ShiftRows(\(\cdot\)), MixColumns(\(\cdot\)), and AddRoundKey(\(\cdot\)). Their detailed explanations can be found in Reference [16].
## 3 Methods  
The approach proposed in this study for large-scale aquaculture mapping with multi-source remotely sensed images and the GEE platform includes seven main steps (Figure 2): (1) data preprocessing, (2) feature extraction, (3) water surface extraction (including aquaculture land), (4) sample selection, (5) Random Forest classification in the GEE platform, (6) morphological post processing, and (7) accuracy assessment. These steps were repeated several times to find the optimal parameters.
# A Deep Reinforcement Learning Floorplanning Algorithm Based on Sequence Pairs +
## 4 Reinforcement Learning  
Reinforcement learning is a machine learning approach aimed at learning how to make decisions to achieve specific goals through interaction with the environment. In reinforcement learning, an agent observes the state of the environment, selects appropriate actions, and continuously optimizes its strategy based on feedback from the environment regarding its actions. This feedback is typically provided in the form of rewards [42; 43] or penalties, and the agent's objective is to learn the optimal strategy by maximizing the long-term cumulative reward.  
Almost all reinforcement learning satisfies the framework of Markov Decision Processes (MDPs). A typical MDP, as shown in Figure 3, consists of four key elements:  
1. States \(S\): a finite set of environmental states.
2. Actions \(A\): a finite set of actions taken by the reinforcement learning agent.
3. State transition model \(P\) (\(s\), \(a\), \(s^{\prime}\)): representing the probability of transitioning from state \(s\in S\) to the next state, \(s^{\prime}\in S\), when action \(a\in A\) is taken.
4. Reward function \(R\) (\(s\), \(a\)): representing the numerical reward for taking action \(a\in A\) in state \(s\in S\). This reward can be positive, negative, or zero.  
The goal of an MDP is to find a policy \(\pi\) that maximizes the total accumulated numerical reward. The expression for the total cumulative reward is as follows:  
\[R_{t}=\sum_{t}^{\infty}\gamma^{t}r_{t} \tag{4}\]  
Figure 2: (a) represents the horizontal constraint graph and (b) represents the vertical constraint graph.  
where \(\gamma\) represents the reward discount factor, \(t\) denotes the time step, and \(r\) represents the reward value at time step \(t\). The state value function \(V_{\pi}(s)\) in an MDP is defined as the expected reward value of state \(s\) under policy \(\pi\), as defined in Equation (5).  
\[V_{\pi}(s)=E_{\pi}[R_{t}|s_{t}=s]=E_{\pi}\left[\sum_{t}^{\infty}\gamma^{t}r_{t} |s_{t}=s\right] \tag{5}\]  
In this context, \(E_{\pi}\) represents the expected value of the reward function under policy \(\pi\). Similarly, the state-action value function \(Q_{\pi}\) (\(s\), \(a\)) is the expected reward value when action \(a\) is taken in state \(s\) under policy \(\pi\), defined as follows:  
\[Q_{\pi}(s,a)=E_{\pi}[R_{t}|s_{t}=s,a_{t}=a]=E_{\pi}\left[\sum_{t}^{\infty} \gamma^{t}r_{t}|s_{t}=s,a_{t}=a\right] \tag{6}\]
# Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach
## 4 Experimental Setup
### Amplitude's Correlation Coefficient  
Correlation coefficient is a subunit parameter, which can be exploited to understand the temporal evolution of the structures present in the SAR images. Pearson correlation coefficient \(\rho\) can be computed between the amplitudes of each two consecutively-acquired images from the dataset:  
\[\rho_{i}=\frac{E[(A_{i}-\mu_{i})(A_{i+1}-\mu_{i+1})]}{\sigma_{i}\sigma_{i+1}} \tag{11}\]  
where \(i\) represents the position of the images in the dataset (sorted according to acquisition dates), \(E\) denotes the statistical expectation, \(A\) is the amplitude, \(\mu\)--its mean, and \(\sigma\)--its standard deviation. This formula will be computed across the whole test scene, using \(5\times 5\) sliding windows. For a global characterization of the dataset, the pondered mean of the \(\rho_{i}\) absolute values has been computed. The weights can be selected according to the temporal distribution of the images:  
\[\rho=\sum_{i=1}^{N-1}\frac{t_{i}}{t_{t}}\rho_{i} \tag{12}\]  
where \(N\) is the number of dataset images, \(t_{i}\) represents the temporal interval between acquisitions \(i\) and \(i+1\), and \(t_{t}\) is the dataset acquisition timespan.
## 3 Results and Discussion
### The Spatial and Temporal Distributions of the Reflections Obtained by the Optimal Constellations
The mean value of the mean revisit time over the globe for the 8-, 24-, 60-, and 120-satellite LEO constellation was 4.376 h, 1.405 h, 0.545 h, and 0.257 h, respectively. This mean value corresponds to the time to finish the visit of each cell over the globe. The smaller the value, the more efficient for the GNSS-R mission to monitor the hurricanes and other natural disasters. It can also be seen from this mean value of each optimal constellation that the optimal 24-satellite constellation improved significantly when compared with the optimal 8-satellite one, while the improvement of the optimal 120-satellite constellation compared with the 60- satellite one was not so significant. Figure 4: The spatial distribution of the mean revisit time for the \(1^{\circ}\times 1^{\circ}\) grid cells of the optimal (**a**) 8-, (**b**) 24-, (**c**) 60-, and (**d**) 120-satellite 2D-LFCs, respectively, during one day. What needs to be mentioned is that as the BDS GEO and IGSO satellites were taken into consideration in our simulation, it can be expected that the reflections over the Asia-Pacific region would be denser when compared with other regions if all the reflections available can be measured with plenty of channels. Although Figure 3b shows that due to the selection criteria of the observable reflections applied in this simulation study, there was no distinct improvement in the number of observable reflections over the Asia-Pacific region. Figure 4a-d reveal that minor improvement in the time resolution of the reflections over this region when compared with other regions does exist.
## 2 Materials and Methods
### Experimental Approach  
Participants were evaluated in two separate testing sessions between 7 and 9 days apart. In the first session, subjects' body composition analysis was assessed (bioelectrical impedance, TANITA BC-418 MA, Amsterdam, The Netherlands). Moreover, participants' maximum oxygen consumption (VO\({}_{\text{2 max}}\)) and maximum heart rate (HR\({}_{\text{max}}\)) were assessed using an incremental running test on a treadmill (Pulsar HP Cosmos, Nussdorf, Germany) equipped with a gas analyzer (Oxycon Pro. Jaegger, Germany) and heart rate monitor (Cosmos, Nussdorf, Germany). After a warm-up period of 5 min of brisk walking (6 km/h), the initial speed was set at 8 km/h, increasing by 1 km/h every minute until volitional exhaustion. The treadmill slope was kept at 1%. VO\({}_{\text{2 max}}\) was defined following the ACSM criteria [11], whereas HR\({}_{\text{max}}\) was determined as the highest HR value reached during the running test.  
The second session consisted of participating in a simulated padel competition following the International Padel Federation rules. The participants were paired based on sex and performance level. All matches were played to the best of three sets; in case of six equal games, a tie-break was played. Before each match, players performed a standardized 15 min warm-up. Total playing time (TPT, full time of the match from the beginning to the end, considering the periods of game and rest), total resting time (TRT, sum of periods in which the ball was not in play), and real playing time (RPT, total playing time minus total resting time) were measured for each match. Moreover, players' HR was continuously recorded during the competition as average values over 5 s (Polar Team System, Kempele, Finland). This competitive event was held on outdoor courts with a relative environmental humidity and temperature of 45.7 \(\pm\) 7.3% and 24.1 \(\pm\) 7.1 degrees Celsius, respectively.  
Both sessions were conducted between 9:00 a.m. and 12:00 a.m., and participants were instructed to avoid strenuous physical activity during the previous 24 h and to abstain from food (overnight fasting), caffeine, and alcohol 12 h before testing. Nevertheless, players were allowed to hydrate ad libitum during the competition (bottled mineral water).