esa-sceva/satcom-chunk-collection huggingface dataset:
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 4 Federated Optimisation (FedOpt)
### Efficiency and Privacy in FedOpt
#### 4.3.1 Initialisation Phase  
In the beginning, the global parameters \(\varpi_{o}\) and the learning rate \(\wp\) are initialised by the cloud server. Then, all the users copy the global training model to the private devices. Apart from having the security parameter \(\sigma\), a secret key \(\delta\) is assigned to each user which is comprised of two big prime numbers \(j,k(|j|=|k|=\sigma)\) where, these prime numbers are given as public parameters \(M\).
## 5 Thermal Stress Analysis of FGMs and Composite Materials
In summary, it can be concluded that the internal physical and mechanical properties in FGMs are continuous and there is no interfacial stress. Compared with composite materials, FGMs are better for overcoming temperature changes in an extreme environment.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
## 3 Difficulties in Locating Skyline Geolocation in Hilly Areas
### Proximity of Camera to Skyline  
For dense hills, the skyline is close to the camera, which leads to locating failure. In skyline geolocation, the nearest sampling point of the DEM dataset is often used as the ground truth of the image taken (as illustrated in Figure 5). Although there is an offset between the ground truth point and image taken position, the skyline features of the ground truth and image are also the most similar. But when the skyline is close to the camera, the difference between the two skylines becomes significant.  
Figure 4: The effects of vegetation on the skyline are illustrated in the areas enclosed by the red rectangles.  
Denoting the distance between two adjacent sampling points as \(D_{sample}\), and the distance between them as \(\Delta\)D,  
\[\Delta\mathrm{D}\in\left[0,\ \frac{\sqrt{2}}{2}D_{sample}\right]. \tag{1}\]  
Since \(\Delta\)D may not be 0, there will be a difference between the image skyline and the ground truth skyline. The difference in skyline caused by this offset is illustrated in the Figure 6.  
When the offset direction is orthogonal to the image viewing direction as in Figure 6a, it is easy to determine that the skyline moves by \(\Delta\)r, and this \(\Delta\)r is equal to \(\Delta\)D. Since the distance between the camera and the skyline is D, and the FoV of the camera is \(\alpha\), the length of the skyline is  
\[2\mathrm{R}=2\mathrm{D}\times\mathrm{tan}\left(\frac{\alpha}{2}\right). \tag{2}\]  
The percentage of skyline movement due to offset is  
\[\frac{\Delta\mathrm{r}}{2\mathrm{R}}=\frac{1}{2\tan\left(\frac{\alpha}{2} \right)}\cdot\frac{\Delta\mathrm{D}}{D}. \tag{3}\]  
Figure 5: Relationship between the image-taken point and Digital Elevation Model (DEM) sampling points. The red dot denotes the location of image taken point, and the grey dots denote sampling points in the DEM dataset. The sampling point in the upper left corner is the ground truth of the image taken point.  
Figure 6: Two differences between image skyline and ground truth skyline: (**a**) The offset direction is orthogonal to the image viewing direction. (**b**) The offset direction is parallel to the image viewing direction. The distance between the image taken point and the skyline is denoted as D. The field of view (FoV) of the camera is \(\alpha\). The error between skylines is \(\Delta\)D. The length of the skyline is 2R.  
When the offset direction is parallel to the image viewing direction as in Figure 6b, the change in the length of the skyline is \(2\Delta\pi\), where  
\[\Delta\mathrm{r}=\mathrm{R}\times\left(\frac{\Delta\mathrm{D}}{\mathrm{D}}\right). \tag{4}\]  
When \(\Delta\mathrm{D}<<\mathrm{D}\) (e.g., in Alps area or desert area), the influence of offset between the image taken point and its ground truth point on the skyline can be ignored. However, when the camera is close to the mountain, such as in a hilly area, the skyline location will be greatly affected.
###### Abstract
the scheduled maintenance campaigns for each component and differentiate between campaigns that lead to operational unavailability and not, (2) simulate the potential failure events and associated corrective maintenance events, and utilize their real historical failure and maintenance data or data extracted from the well-known failure database, i.e., the offshore and onshore reliability data handbook (OREDA) [17], or both, (3) assign \"failure rate\" and \"mean time to repair\" (MTTR) values for each maintainable item (component level) and associated failure modes, (4) simulate maintenance events that are triggered by CBM or PdM algorithms, (5) assign the capability level of condition monitoring techniques and prediction algorithms [16], and leverage the predicted failure events into opportunistic maintenance intervals in terms of intelligent maintenance, (6) simulate deterioration process and predict failure events based on realistic (fluctuating, seasonal patterns, stand-by operations, extreme loading intervals) loading and operating profiles. Thus, to build such a model and validate its structure and behavior, a case study of a centrifugal compressor used for natural gas transportation is purposefully selected. The novel multi-method computational simulation model in this paper is decomposed into four sub-models (1) working state for operational availability and intelligent maintenance, (2) scheduled maintenance states (component level and equipment level) which also presents the opportunistic maintenance intervals, (3) failure states which represent failure modes and triggers for failure events, and (4) corrective maintenance states. Furthermore, to highlight the expected lifetime benefits of intelligent maintenance during 20 years of operation, two main use case scenarios shall be modeled: with and without intelligent maintenance. The latter use case scenario (without intelligent maintenance) has several sub-scenarios that also study the effectiveness of several possible data sources: (1) empiric case study data (experience), (2) manipulated empiric case study data, (3) the OREDA database [17], and (4) mixed data-input from both the empiric case study and the OREDA database. These four sub-scenarios along with the intelligent maintenance scenario result in a total of five simulated use case scenarios. The six-step modeling and simulation methodology, presented in the following section, is applied to build the desired novel multi-method computational simulation model that combines agent-based modeling with system dynamics to simulate the five use case scenarios. In this case, the multi-method modeling software Anylogic 8 is utilized. The rest of this section is organized as follows. First, Section 2 explains the materials and methodology of this study, which includes the entire six-step simulation modeling methodology adopted. Section 3 presents the simulated results obtained from the computational model. Section 4 discusses and validates the findings of this study. Finally, Section 5 offers some conclusions and makes recommendations for future work.
# Visual Navigation Systems for Maritime Smart Ships: A Survey
## 3 Visual Perception Navigation Technology  
Visual aid navigation is an important function of autonomous navigation in smart ships. In order to reduce the workload of smart ship pilots, a visual assistance system visualizes and processes the decisions of intelligent navigation devices for computer-aided visual navigation, which improves the situation awareness of smart ship pilots. The navigation information of the existing visual navigation system mainly comes from the traditional equipment on the ship's bridge, such as AISs, radar, infrared sensors, electronic nautical charts, etc. Through augmented reality technology, the above navigation information will be matched to the video imagery of the ship's video camera so as to realize the overlap of the virtual information and the real imagery and adjust the routes and sailing speeds according to the current situation of the ship, as well as make avoidance decisions so as to improve the safety of the ship [80]. In a navigation system, it is crucial to accurately detect the navigation information of the target ship. The environmental perception of ship navigation also means using AIS, radar, depth sounder, camera and other sensors to collect environmental data for ship navigation and constructing algorithms, such as data integration and perception enhancement algorithms, to automatically process and analyze perceived data, identify potential dangers, implement collision avoidance measures and safeguard the safety of navigation. The visual perception technology of ships cannot be separated from the environmental data perceived by the sensors; however, the use of any one of these methods for perception alone may have the problems of inconsistent information and insufficient data accuracy. At this stage, in addition to single-sensor perception, the main source of environmental data is multi-sensor data fusion. Generally, when designing a multi-sensor data fusion architecture, centralized fusion, distributed fusion and hybrid fusion are involved. We first analyze the technologies related to single perception, followed by a further study of integrated perception based on single perception.
## 2 Theoretical Models
### Surface Charging Theory Model
#### 2.1.1 Equation for Current Balance
The ion current is established by computing the number of macroscopic ion particles impacting a specified surface. This count is subsequently converted into current, taking into account the weight and charge of the macroscopic ion particles. SPIS simulates the surface charging phenomenon by first calculating the distribution of electrons and ions. To enhance computational speed and simulation accuracy, a particle mixing model is adopted, treating electrons as a fluid and ions as kinetic particles. The dynamic process of plasma electrons under the self-consistent electric field is computed through the Vlasov-Poisson equation [10], while the dynamic process of ions is calculated using the Boltzmann-Poisson equation. Moreover, due to the high plasma density in the plume and the extensive computations involved, the Particle In Cell-Monte Carlo Collisions (PIC-MCC) method is employed. This method introduces the concept of macro-particles, where the motion of macro-particles is represented by a subset of them rather than the actual motion of charged particles. In the PIC method, particles reside within grid cells, and it requires assigning weights to the volume of particles with different energies within the grid cell to distribute charges to surrounding grid points, thereby representing the total charge of the macro-particle, and the motion equation of macro-particles is obtained through the combination of [PERSON]'s equations, the force relationship of macro-particles in electromagnetic fields, and Vlasov-Poisson, which is represented as  
\[\left\{ \begin{array}{l}M_{\text{a}}\frac{\text{d}v_{\text{m}}}{\text{d}t}=Q _{\text{a}}(E+v_{n}\times B)\\ \frac{\text{d}v_{n}}{\text{d}t}=v_{n}\\ \
abla^{2}\phi=-\frac{\rho}{\epsilon_{0}}\\ E=-\
abla\phi\end{array} \right. \tag{3}\]  
Figure 1: Diagram of spacecraft surface current. where \(M_{\rm a}\) is the mass of particles composing a macro-particle, \(v_{\rm n}\) is the speed of the macro-particle, \(Q_{\rm a}\) is the total charge of a macro-particle, \(E\) is the electric field intensity, \(B\) is the magnetic field intensity, \(r_{n}\) is the travel distance of the macro-particle, \(\phi\) is the potential, \(\rho\) is the charge density, \(\varepsilon_{0}\) is the vacuum dielectric constant.
## 5 Research and Development Challenges
### Blockchain for Security in Drone-Based Delivery  
To ensure successful package delivery, numerous crucial data need to be processed and stored, such as time, delivery location, date of delivery, and user information. This information needs to be stored in a secure system to prevent others from exploiting these data for heinous purposes [82]. Blockchain can be an effective solution to provide a secure and robust framework for storing such information in a secure platform. Thus, more attention should be given to the design of secure delivery systems. Additionally, a secure transaction network is required between drone and commercially deployed CSs to trade energy [46]. To ensure a secure transaction network between drones and CSs, researchers use different blockchain consensus mechanisms. However, such consensus mechanism is required to exchange distributed trusted ledgers with each other in order to validate each transaction. Since drones have a limited battery lifetime, it requires additional energy consumption to share such consensus messages. As a result, the implementation of energy-efficient secure blockchain mechanism requires further study. In particular, the IOTA consensus mechanism can help implement energy-efficient and secure energy trading between drone and CSs [46; 83].
## 4 Comparison Analysis of Prediction Models
### Prediction Accuracy Comparison
In addition to these conventional performance metrics, a cost-sensitive analysis was conducted to assess the impact of misclassifications in a traffic safety context. By assigning a cost of one unit for a false positive (CFP = 1) and a cost of five units for a false negative (CFN = 5) [39], reflecting the higher risk of missing a hazardous event--the overall misclassification cost was calculated for each model. Based on the estimated distribution of misclassifications in the test set, the BP neural network incurred a total cost of 54 units (9 false positives and 9 false negatives), GA-BP resulted in 46 units (6 false positives and 8 false negatives), and XGBoost accumulated 55 units (10 false positives and 9 false negatives). This cost analysis underscores that despite XGBoost's lower algorithmic error, its higher misclassification cost indicates a greater likelihood of high-risk errors, particularly false negatives, than the GA-BP model. Overall, the cost-sensitive evaluation further supports the superiority of the GA-BP neural network in minimizing overall misclassification errors and the potential high-impact errors critical in traffic safety applications. The evaluation results for all three models are summarized in Table 6.  
Overall, the comparative analysis indicates that while XGBoost performed well on the training set, its generalization ability on the test set was weaker than the BP and GA-BP neural networks. The GA-BP neural network consistently outperformed the other models across multiple evaluation metrics, demonstrating higher classification accuracy and robustness. Importantly, none of the models exhibited overfitting, confirming their stability in handling the given classification task. These findings suggest that incorporating genetic algorithms into BP neural networks enhances predictive capability, making GA-BP the most suitable model for this application.
## 1 Introduction
### System Control with Energy Storage Options  
Several control strategies have been implemented in the RE system that are equipped with energy storage units. SCs have been used in combination with batteries to extend the battery lifetime by buffering the peak current pulses and reducing the charge/discharge cycles in the battery. [PERSON] et al. [53] designed a hybrid SC-battery energy storage system for a PV system, where the SCs supplied the high peak power while the battery supplied the low power in terms of operating conditions. It was concluded that the addition of SCs increased the battery SOC by 12% under peak load, hence reducing the size of the battery and avoiding a deep discharge of the batteries. [PERSON] et al. [54] proposed a hybrid battery and SCs for large-scale grid-connected wind turbine systems in order to smooth fluctuations. The LA batteries smoothed the power output for ~10 min, while the SCs absorbed the short transients in energy (1-10 s) that prevented the current peaks from reaching the battery. These applications highlight the feasibility of coupling SCs in parallel with batteries to RE systems to improve the performance with the inherent variability of the resource and the ability of SCs to reduce the size of the battery so that the cost can be reduced. [PERSON] et al. [55] proposed a current control scheme for Li-ion battery energy storage systems (energy storage capacity of 0.55 kWh) that was designed based on the SOC of the batteries for load leveling and peak shaving of the on-grid system. A bidirectional converter was designed to transfer power in both directions by including a current control loop. Provided the SOC is limited to the upper and lower thresholds, the simulated results indicated that the batteries absorbed 35-W power from the grid while injecting 200 W of power to the grid in a time period of 25 ms (corresponding to the energy capacity of 10\({}^{-3}\) Wh). The energy transfer of the bidirectional alternating current (AC)/direct current (DC) converter was confirmed.
## 4 Discussion
### Response of SOS/EOS to Climate Change among Different Grassland Types  
We observed that SOS had a similar response to pre-SOS precipitation but different response to pre-SOS temperature among meadow steppe, typical steppe, and desert steppe. Our results suggest that enhanced pre-SOS rainfall could advance vegetation green up in all the three grassland types. A greater pre-SOS temperature may advance SOS in meadow steppe given sufficient water supply from thawed frozen soil which may store substantial amounts of water from the previous winter [70; 71]. In contrast, a rapid temperature increase in spring would delay SOS in typical steppe and desert steppe due to the concurrent rapid increase in evaporation and sharp decrease in soil moisture supply [68]. The better relationship between SOS with pre-SOS precipitation than temperature illustrates that SOSis primarily controlled by water regime in all the three grassland types. This agrees with previous results reported by [PERSON] and [PERSON] (2018).  
Similar to SOS, the effects of pre-EOS precipitation on EOS in meadow steppe, typical steppe, and desert steppe were concordant. Based on these results, increased pre-EOS precipitation would delay the end of vegetation growing season in all the three grasslands types. However, EOS in desert steppe was more sensitive to moisture dynamics than that in meadow steppe and typical steppe. Similarly, like SOS, the response of EOS to pre-EOS temperature was discrepant among the three grassland types. Our results suggest that higher pre-EOS temperature may delay the end of plant growth in meadow steppe and typical steppe, while terminating the vegetation growing season earlier in desert steppe. Based on our findings, we conclude that temperature mainly determines vegetation autumn phenology in meadow steppe and typical steppe, whereas precipitation is the main driver of vegetation autumn phenology in desert steppe.  
Furthermore, the sensitivity of SOS/EOS to pre-SOS/pre-EOS temperature and precipitation changes varied among meadow steppe, typical steppe, and desert steppe. SOS in desert steppe showed the highest sensitivity to both pre-SOS temperature and precipitation than meadow steppe and typical steppe, which is in accordance with the results reported in the same region by [PERSON] et al. (2018). Meanwhile EOS in meadow steppe and typical steppe exhibit a stronger sensitivity to pre-EOS temperature, but weaker sensitivity to pre-EOS precipitation than desert steppe, as previously suggested by [PERSON] et al. (2018). The greater SD of SOS/EOS found in desert steppe than that in meadow steppe and typical steppe observed in our study was also found in the study by [PERSON] et al. (2018), suggesting a more complex and sensitive vegetation growth pattern in desert ecosystems.
## 5 Distinguishing AI Agents in Autonomous Maritime Systems from Conventional AI Systems  
Artificial intelligence (AI) has permeated numerous sectors, revolutionizing operations through automation, data analysis, and intelligent decision-making. However, the application of AI within autonomous maritime transportation systems entails distinct chal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project/System** & **Country/Organization** & **Application Type** & **AI Capabilities** & **Key Technical Features** \\ \hline \multirow{2}{*}{Yara Birkeland} & Oslo, Norway/ & Fully autonomous & Autonomous navigation, obstacle avoidance & Electric propulsion, integrated sensor fusion system \\  & Yara International & Remote and autonomous & AI-based path following, autonomy via vision & Edge processing, LiDAR, radar, thermal + visual cams \\ \multirow{2}{*}{Sea Machines SM300} & Boston, MA, USA/ & \multirow{2}{*}{Predictive navigation} & \multirow{2}{*}{Real-time anomaly detection, collision prediction} & Reinforcement learning, historical data models \\  & Sea Machines Robotics & & & \\  & & & & \\ \multirow{2}{*}{NYK Line x Fujitsu AI} & Tokyo, Japan/ & \multirow{2}{*}{Pediatric navigation and safety} & \multirow{2}{*}{Real-time anomaly detection, collision prediction} & \multirow{2}{*}{Resnforcement learning, historical data models} \\  & NYK Line and Fujitsu & & & \\ \multirow{2}{*}{Rolls-Royce Intelligent Awareness} & London, UK/ & \multirow{2}{*}{Situational awareness} & Object recognition, decision support for crew & Sensor fusion (visual + IR + radar), ML vision models \\  & Rolls-Royce & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of real-world implementations of maritime AI systems.  
lenges and requirements that set it apart from conventional AI systems deployed in other domains. This chapter elucidates the fundamental differences between AI agents tailored for autonomous maritime environments and their counterparts in general applications, highlighting the specialized functionalities, operational constraints, safety imperatives, and contextual factors that define maritime AI agents.