esa-sceva/satcom-chunk-collection huggingface dataset:
## Category III: System Optimization
### Operational Cost Minimization  
The cost minimization involves both the individual users and the overall energy cost. For example, [PERSON] et al. [114] mainly considered the perspective of individual end users. The mixed integer linear programming is used to reduce energy waste and minimize energy cost. In [64], a proof-of-concept architecture based on secure private blockchain is proposed. The concept of atomic meta-transactions is introduced. The energy consumer will commit to pay, and, after receiving the energy, an energy receipt confirmation will be generated for confirmation and connected with the smart contract. The system has proven to be able to reduce the cost, processing time, and block size of energy transactions. In [141], similarly, the operation cost of electric vehicle charging stations is prioritized, while the energy supply is not affected. It is proposed that renewable energy can be used for the operation of charging stations during the day, while the central battery pack stores the excess energy at night. In this way, the charging cost of EV owners can be minimized.  
In terms of the total cost, in [11], the energy transaction between isolated island microgrids are studied, and a distributed convex optimization framework is developed for it. A cost minimization algorithm based on sub-gradient is proposed, which can expand the number of microgrids and maintain the privacy of local consumption. [PERSON] et al. [142] focused on solving the heterogeneitybetween microgrid devices. A crowd-sourcing storage system to design a centralized online algorithm is adopted, which can minimize the cost while achieving supply and demand balance. The hybrid approach combines the smart contracts of blockchain with the use of traditional computing platforms in [84].  
Power-ID [47], a Swiss pilot project, uses blockchain to create a small peer-to-peer energy market of 20 consumers and 20 prosumers. The cost of the network is flexible by concentrating the distributed network on solar energy and batteries. If a large amount of local energy is self-sufficient (such as a sunny day), then participants only have to pay their own network costs, which helps reduce the overall cost of the system.
## 3 Proposed Solution
### Area Division with MLC  
As discussed in Section 1, computational time is a major constraint when using ML algorithms. In this study, we utilized the classification function of ML to reduce the effect of run time on system performance. This solution is based on the heterogeneous distribution of the SNR (Figure 4). In Figure 4, the shape of the signal is uniform across the entire floor thanks to the elimination of reflected noise. This homogenous state, however, disappears when the received signal at the PD is a combination of directed and non-directed optical signals (Figure 4). To conduct area division, we divided the floor of the room into two sections: a center area and an edge area (corners and near-the-wall areas). To prepare for collecting the training dataset, the boundary between the two areas can be determined by two important factors: the identity of the received optical power and the amount of data used for the training process.  
Figure 8 shows the average power distribution according to the vertical projection of the room in three reflection level cases: 0.2, 0.5, and 0.8. These values depend directly on the surface material used for the walls, which cause reflective noises [23]. There is a significant difference in the power distribution between the central area (with red spots) and the edge area (with blue spots). It is clear that the central area has a more uniform distribution and is more stable than the edge area, which shows greater reflection intensity.  
Figure 7: Sampling data by: (**a**) LOS signals and (**b**) diffuse signals.  
To ensure similarity in the amount of training data for the two areas, we set the boundary as shown in Figure 9. By using this division, the training data in the center area and the edge area accounted for 47.93% and 52.07%, respectively, of the total 676 reference points. This balance in the amount of training data helped to reliably assess the effectiveness of the classification process.  
In this study, we used the SVM, DT, kNN, and RF ML algorithms, due to their ability to both classify and regress. After the training and prediction process, we calculated the accuracy score and conducted K-fold CV to evaluate the robustness of each method.
# Gravity Field Recovery Using High-Precision, High-Low Inter-Satellite Links
## 4 Results
### Temporal Gravity Field Retrieval
2011 [23] demonstrated the benefit of the co-parameterization of additional daily low degree and order gravity field coefficients for Bender-type satellite constellations. We investigated the potential of this methodology regarding the MOBILE concept by simulating a monthly solution while co-estimating daily gravity fields up to SH degree and order 10, including HIS signal plus instrument errors. The resulting retrieval errors are displayed in Figure 8 (magenta curve). They revealed an error reduction of about 40% compared to the nominal solution (green curve), which led to an increased spatial resolution of about SH degree 50 (\(\approx\)400 km) instead of 40 (\(\approx\)500 km). The corresponding spatial plot (Figure 9, f and g) shows a global reduced aliasing pattern, especially in higher latitudes. The comparison between the true HIS signal and the MOBILE recovered signal (nominal and extended processed) displayed in Figure 9 shows that the quality of the solutions could  
Figure 9: Global grids of EWH (cm) up to SH degree and order 50 after 1 month. The grids show the true HIS signal (**a**), the recovered signal (**b,d,f**), and the differences of the true HIS signal and the recovered signal (**c,e,g**), for the low–low pair configuration (**b,c**), for the MOBILE concept (**d,e**), and for the MOBILE concept using the extended processing method (**f,g**). be improved to such a level that de-striping and smoothing the solutions was no longer necessary when examining signals to degree and order 50. Therefore, a possible loss of signal by a posteriori filtering of the gravity field solutions recovered by MOBILE could be avoided.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 5 Networking Protocol Based on Digital Twins
### Topological Twinning
The dynamic calculation logic of the low Earth orbit constellation network topology is illustrated in Figure 5.  
The network topology of the LEO constellation is characterized by periodicity and predictability. Two lines of reports for each satellite in the LEO constellation are pre-entered, along with the limiting latitude values visible between neighboring heterodyne satellites in this constellation from the STK software simulations. At the onset of the simulation, the SGP4 algorithm is employed to calculate the real-time position information of each satellite at the current simulation moment, which is then represented using Cartesian coordinates. The position information is subsequently converted into latitude and longitude  
Figure 4: Schematic diagram of network topology simulation. coordinates, which determine the link status between any two neighboring LEO satellites. For example, the two lines of reports for two neighboring heterorbital satellites, L0101 and L0201, in the 60-star constellation in a LEO polar orbit on 1 August 2022 are shown in Figure 6. The maximum visible latitude between neighboring heterorbital satellites in this constellation, as determined from the STK software simulations, is 75\({}^{\circ}\). The gp4 algorithm was utilized to calculate the Cartesian coordinates of satellite L0101, resulting in the position (7558.71112301405, 0.4011442917211835, \(-0.3435727076716426\)) on 1 August 2022, at 4:00:00 h. These coordinates were then converted to longitude and latitude, resulting in (0.00304071348882\({}^{\circ}\), \(-0.002619110010^{\circ}\)). Likewise, the Cartesian coordinates for satellite L0201 are (6154.4885404322695, 3715.757929718934, 2333.5585136680743), resulting in longitude and latitude values of (31.1213807/701\({}^{\circ}\), 18.07854158\({}^{\circ}\)). Since neither satellite dimension exceeds the limiting latitude, the link between L0101 and L0201 is connected at this moment.
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 3 WAMS Operation and Dynamic Fault Detection
### Optimal PMU Placement and Fault Detection
However, when faults are dynamic and evolve rapidly, such as in the case of transient faults or evolving system instabilities, the sudden changes in \(V_{j}\) and \(V_{i}\) may not be efficiently captured and processed, particularly if the sampling rate or data synchronization is inadequate. This has been a limitation in traditional SCADA systems, which operate at much lower sampling rates (typically seconds) and lack real-time synchronization. Optimal PMU placement is therefore not just about ensuring observability but also about capturing the dynamic changes in \(V_{j}\) and \(V_{i}\) with high fidelity. This involves strategically locating PMUs at critical nodes where voltage deviations due to faults are most significant or where fault propagation is likely to impact grid stability. It is in this design that metaheuristic-based optimization algorithms are often explored to achieve overall grid stability. Furthermore, the placement must account for areas of the network prone to high fault incidence or regions where transient behavior can significantly affect operational decisions. To enhance the effectiveness of PMU placement, it is essential to consider the dynamic characteristics of the system. While PMUs provide near-instantaneous measurements, they assume a relatively stable frequency or phase angle value across the grid. During faults, the grid frequency and phase angles may deviate, but these deviations are generally slower compared to the rapid voltage changes. Thus, the PMU placement strategy should aim to maximize the ability to monitor \(V_{j}\) and \(V_{i}\) dynamics under these conditions [56]. Additionally, advanced data processing techniques, such as adaptive filtering and real-time dynamic state estimation, can complement optimal PMU placement. These methods help refine the measurement of \(V_{j}\) and \(V_{i}\) during fast-changing fault scenarios, ensuring that the system remains accurately observable even under extreme conditions. Therefore, the design of optimal PMU placement should not only focus on achieving full-network observability but also on capturing and processing the dynamic changes in voltage magnitudes at critical points in the grid. Doing so ensures that the system can efficiently respond to and mitigate the impact of rapidly evolving faults, maintaining stability and operational integrity.
## 4 Evaluation of CNN Models
### Validation Techniques  
Furthermore, the model's performance was validated in accordance with the following criteria:* Cross-validation was employed as a technique to ensure the robustness of the model and reduce the likelihood of overfitting. In order to ensure the model's robustness and to reduce the likelihood of overfitting, k-fold cross-validation was employed during the training phase. This technique involved dividing the training dataset into \"k\" subsets and iteratively training the model \"k\" times (k = 5), with each iteration involving the use of a different subset held out for validation.
* Real-time testing: To simulate real-world usage, the model was also tested in a simulated clinical environment, where it processed live video feeds to detect and interpret emotional expressions in real time. This testing was of great importance in assessing the model's performance in dynamic situations that were similar to actual patient interactions. The experiments, including both training and testing phases, were conducted on an MSI Summit B15 A11 MT laptop with an Intel Core i7 processor and 64 GB of RAM, running on a Linux operating system. The training duration for each session of 100 epochs was approximately 100 h.  
In order to train the CNN model to recognize emotional cues linked to psychosomatic illnesses effectively, several strategies were implemented including the following:  
* Enhanced data augmentation: To reflect the variability observed in real-world clinical settings, extensive data augmentation techniques were employed, including rotation, scaling, and lighting changes. This approach facilitated the training of the model to recognize emotional cues accurately across diverse conditions.
* Layer Optimization: A variety of architectural configurations were tested, including alterations to the number and size of convolutional layers and filters, with the objective of optimizing the model for the accurate detection of subtle emotional cues that are frequently indicative of psychosomatic illnesses.  
The integration of the emotion recognition model into clinical workflows was informed by several best practices including the following:  
* User-centric design: The interface for the emotion recognition tool was designed with input from healthcare professionals to ensure that it is intuitive and enhances rather than disrupts the clinical workflow.
* Feedback loops: Ongoing feedback was sought from end-users to facilitate iterative improvements to the system. Such feedback was of the utmost importance for the refinement of the interface design, the improvement of model accuracy, and the assurance of the tool's relevance in clinical settings [30].
* Ethical and privacy considerations: Given the sensitivity of psychological assessments, the model's integration was conducted with a strong emphasis on ethical considerations and patient privacy. This included secure data handling and clear patient consent processes [31].
## 3 Framework of Forecasting AOG Models in Aviation
### Evolution of Forecasting Models  
Over the years, forecasting models for managing aircraft maintenance and spare parts logistics have evolved significantly due to advancements in the aviation industry [25]. Initially, airlines followed a break-fix maintenance approach, repairing or replacing components only after failure.  
This reactive method often led to costly AOG situations and disrupted operations. Early maintenance schedules relied on time or usage intervals rather than real-time conditions, which limited efficiency and affordability [26].  
As the industry progressed, system-based maintenance schedules emerged, targeting maintenance at specific intervals based on flight hours or cycles. While this reduced unexpected failures, it introduced inefficiencies by replacing parts prematurely or missing failures between checks. The paradigm shifted with the introduction of predictive maintenance models, enabled by advancements in data analytics, sensor technology, and machine learning [27].  
Predictive maintenance uses real-time sensor data to monitor aircraft components, allowing airlines to assess part conditions continuously and predict potential failures. This proactive approach minimizes AOG incidents by addressing issues precisely when needed, avoiding unnecessary repairs or replacements [16]. Regression analysis, econometric modeling, and bootstrap methods enhance predictions and help airlines to optimize spare parts inventory, plan maintenance, and improve overall system functionality [28]. Transitioning from reactive to predictive maintenance has significantly improved operational efficiency and aircraft availability across the industry.
## 5 Results and Discussion
### Optimized Parameters  
Figure 10 shows the environmental temperature, user set temperature, and the optimized temperatures according to the algorithms. The optimized values oscillate within the bounded limit of [68 \({}^{\circ}\)F, 78 \({}^{\circ}\)F] and all are in between the environmental temperature value (dark blue) and approaching the user set value (red). The oscillation is influenced by the change of the environmental temperature. The optimizers aim to minimize the error difference between the optimized temperature and user's preference so that the comfort index is maximized, while at the same time reducing the difference of the optimized temperature with the environment so that the energy usage is reduced. The energy consumption in optimizing the temperature is shown in Figure 11. The energy required for the system using optimized temperatures are lower than the non-optimized system in most of the settings. The exponential IW-ABC (in green) is observed to contribute to lower energy usage.  
Figure 8: Energy Consumption vs. Time Instance.  
Figure 9: Convergence Curves of Comfort Index and Energy Consumption.  
was also in the predefined limit. Figure 13 shows the required energy of illumination. The optimized energy consumption is lower than the non-optimized system for all time instances. Thus, the required energy consumption of the lighting system is successfully reduced using the optimization system. The IW-ABC with linear decreasing inertia weight (in purple) found the best illumination level with the lowest energy usage.  
Figure 12: Optimized Illumination vs. Time Instance.  
Figure 13: Energy Consumption for Illumination Optimization.
## 3 Implementation Strategy of Dynamic Routing Protocol for Awareness Combat Networks
### Link Delay Model of the Awareness Combat Network  
Real-time capacity is one of the most concerning aspects of the awareness combat network. Therefore, it is necessary to study the time delay problem of links in the awareness combat network. In a traditional network routing protocol, time delay can be expressed by the equivalent number of hops; in the awareness combat network, time delay is a comprehensive parameter. It directly reflects the residence time of data packets in the network and also indirectly reflects the topology connectivity, congestion, and other conditions of the network. In this paper, a link delay model is established by considering the source of delay in the awareness combat network.  
The data packet on the link first waits for the link to be connected; the waiting time is denoted as \(t_{d}\). After the link is connected, it starts to send data packets. According to the first-in-first-out (FIFO) queuing principle, it is necessary to wait for the first-in packets to be sent; this queuing time is denoted as \(t_{p}\). Finally, the transmission time of the packet itself is denoted as \(t_{m}\). Among them, \(t_{d}\) is the new time delay introduced by links in the awareness combat network. In the \"flat\" state of the awareness combat network, the burden for the link is relatively light and the amount of data transmitted is not large, so \(t_{p}\) and \(t_{m}\) are small and even negligible, but \(t_{d}\) is a main component. In the \"peak\" state of the awareness combat network, the link is heavily burdened and the amount of data transmitted is large, so the data packet may need n times to be completely transmitted rather than just one time during the period when the link is connected. The data packet undergoes a queuing delay and link establishment delay. The total time delay \(t_{z}\) is as follows:  
\[t_{z}=\sum_{n}\bigl{(}t_{d}[n]+t_{p}[n]\bigr{)}+t_{m} \tag{7}\]
# DNN-Based FES Control for Gait Rehabilitation of Hemiplegic Patients
## 3 Experiments  
While the developed system was designed for the rehabilitation of hemiplegic patients, healthy human subjects were used for testing as a preliminary evaluation of the system. The developed DNN-based FES envelope prediction system was compared with a conventional system using a trapezoidal FES envelope, which is widely used in commercial FES devices. Performance of the two systems (DNN-based envelope (DBE) and TE (trapezoidal envelope)) was compared considering the following factors: (1) the range of motion of the ankle joint during treadmill gait at various walking speeds, and (2) muscle fatigue after the repetitive isometric contraction of the ankle joint.  
In this study, 11 healthy male subjects aged between 20 and 30 years participated in the experimental tests. From six subjects, EMG data during treadmill gait were collected, and the data were used to train the DNN model, as described in Section 2.2. Five subjects participated in the comparison tests of the DBE and TE. Each subject was fully informed about the features of the developed system and procedures of the experiments before signing a consent form. The experiments involving human subjects were approved by the Institutional Review Board of Korea University in Seoul, Korea (KUIRB-2019-0061-01).
## 2 System Characteristics
### Absorption and Scattering
\begin{array}{l}\widetilde{\beta}_{FF}(\varphi)=\frac{1}{4\pi(1-\delta)^{2} \delta^{v}}\big{[}v(1-\delta)-(1-\delta^{v})+\left[\delta(1-\delta^{v})-v(1- \delta)\right]\sin^{-2}\big{(}\frac{\varphi}{2}\big{)}\big{]}\\ +\frac{1-\delta_{180}^{v}}{16\pi(\delta_{180}-1)\delta_{180}^{v}}\big{(}3\cos^ {2}\psi-1\big{)}\end{array}  \tag{4}\]  
where  
\[v=\frac{3-\mu}{2} \tag{5}\]  
\[\delta=\frac{4}{3(n-1)^{2}}\sin^{2}\Big{(}\frac{\varphi}{2}\Big{)} \tag{6}\]  
Here, \(n\) is the real index of refraction of the particles, \(\mu\) is the slope parameter of the hyperbolic distribution, and \(\delta_{180}\) is \(\delta\) evaluated at \(\varphi=180\) deg. \[B_{p}=1-\frac{1-\delta_{90}^{v+1}-0.5\big{(}1-\delta_{90}^{v}\big{)}}{(1- \delta_{90})\delta_{90}^{v}} \tag{7}\]  
where \(\delta_{90}\) is \(\delta\) evaluated at \(\varphi=90\) deg. The backscatter fraction, \(\beta_{p}\), which represents the polar angle of a photon scattered greater than \(90^{\circ}\), is very helpful for confirming the parameters \(n\) and \(\mu\) in the actual water.
# Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System
## 2 Materials and Methods
### WebSocket  
WebSocket is a communication protocol pertaining to the application layer in the Transmission Control Protocol/Internet Protocol model (TCP/IP) [50]. Due to the popularity and prevalence of HTTP, WebSocket uses HTTP constructs for the initial connection between a client and a server [51] and provides persistent communication so that both the client and the server can send messages at any time. Compared to traditional real-time web communication, the WebSocket protocol saves a lot of network bandwidth and server resources, and the real-time performance is significantly improved [52]. It is helpful for real-time applications such as online games, financial trading platforms, eye tracking, and Internet of Things (IoT)-based applications that support server push technology [53; 54].