esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Analysis and Discussion
### Results Analysis

Table 2: \(J_{1}\) and \(J_{2}\) values under different objective functions and electrolyzer types. Figure 4: The rated power of AWE and PEME using different objective functions and electrolyzer types. identified by the proposed method in this study. In contrast, the recommended results of methods 1 and 2 were situated near the intersection point C between the BC and CD regions, respectively. This means that the proposed method is effective and excellent. Figure 6 and Table 3 present the rated power of the hybrid electrolyzer along with the optimization objective function values derived from different optimization methods. In Figure 6 and Table 3, when using the optimization objective \(\min(J_{2})\) with method 1, the configuration result of the hybrid electrolyzer was a single PEME. This indicates that the utilization rate of hydrogen production was overly focused, and a relatively one-sided result was obtained. In contrast, when applying \(\min(J_{1})\), the planning result revealed a hybrid electrolyzer composed of AWE and PEME. However, this approach excessively reduced the cost of hydrogen production, which significantly increased the loss of hydrogen production. Consequently, the rated power of the hybrid electrolyzer tended to be relatively high. When comparing method 1 with the proposed method, the total rated power of the hybrid electrolyzer remained consistent, although the proportions of AWE and PEME differed. Notably, the proposed method further reduced the hydrogen production costs by increasing the proportion of AWE within the hybrid electrolyzer. However, this came at the expense of a higher hydrogen production loss, which was mainly manifested by the low hydrogen production efficiency of AWE. Figure 5: The pareto front of the optimization problem \((J_{1}/\min(J_{1})\,\mid,\,J_{2}/\,\mid\,\min(J_{2})\,\mid)\) using the proposed methods. Figure 6: The rated power of the electrolyzer and \(J_{1}/\min(J_{1})\) using different methods. Compared with the results of a single optimization objective in Table 3, the total rated power of the hybrid electrolyzer, when assessed using multi-objective functions and the proposed method, matched min(\(J_{2}\)) but was lower than min(\(J_{1}\)). Under the hybrid electrolyzer structure, when focusing on a single optimization objective of minimizing the economy of hydrogen production, the proposed method decreased by 1.00% and increased by 21.71% in the economy and utilization of hydrogen production, respectively. Furthermore, compared with a single optimization objective minimizing the loss of hydrogen production, the utilization and economy of hydrogen production were reduced by 5.22% and increased by 6.46%, respectively. Overall, the proposed method achieved a better balance between the optimization objectives than the results from a single optimization objective, making it more advantageous.
# A DRL-Based Satellite Service Allocation Method in LEO Satellite Networks
## 5 Results
As the number of services further increases, between 300 and 700, termed the \"saturation state\", the proposed method demonstrates significant superiority, with the ability to allocate an additional 5% of services. When the number of services exceeds 700, considered the \"overload state\" due to severe resource shortages, the three methods show similar performance. Overall, the proposed method consistently provides a higher service allocation rate relative to the comparison algorithms. Figure (b)b shows that in the stable state, the performance of the MCF algorithm is similar to the proposed algorithm, with both significantly outperforming the random algorithm. After entering the saturation state, the proposed algorithm gradually exhibits higher superiority, achieving approximately a 10% improvement compared to the MCF algorithm. From Figure (c)c, it can be observed that in both the stable and saturation states, the proposed algorithm and the MCF algorithm exhibit significant optimization in terms of server resource utilization. Upon entering the overload state, due to resource scarcity, none of the three algorithms can further increase resource utilization. Although the difference with the MCF algorithm is not substantial, the proposed algorithm consistently demonstrates performance superior to the MCF algorithm in this scenario. Figure 8: DRL model training process.
### Objectives and Contributions  
This paper proposes a new detection algorithm that yields similar performance to that of ML with low computational complexity. A data precoding scheme is proposed that efficiently exploits the channel's frequency diversity without affecting the system's spectral efficiency. The proposed detector offers two main advantages:  
* It assimilates the linear data precoding stage so that the operation is obtained with equivalent channel matrices that maintain the band structure. This makes it possible to extract the channel diversity, which significantly improves performance while demanding low complexity;
* It uses an improved search order that enables the decoder to find the optimal solution in fewer iterations. Furthermore, the detector's maximum search size can be set to an amount of operations that will not have a significant impact on performance.  
Finally, an iterative reception structure is proposed that makes it possible to eliminate ICI among data subcarriers. The performance achieved with this technique after one iteration comes close to that obtained in conditions of exact knowledge of the channel.
# Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization
## 1 Introduction
### Pareto Optimization  
The administrator has a limited amount of resources and achieving complete security is unattainable [32; 33]. Thus, a realistic approach is to find an optimal strategy that maximizes the performance of the network with a minimum cost despite known vulnerabilities [34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. Similarly, the attacker has a limited amount of resources. The knowledge of possible actions for both players allows participants to discover the potential actions and their best counter action [37]. Thus, both participants are forced to maximize their utility from multiple goal perspectives [38]. Pareto optimality is a state of resource allocation that addresses the issue of cost/benefit trade off. In a Pareto optimal state, no goal can be further improved without making the other goals worse off.  
[PERSON] et al. [32] have proposed a model for evaluating vulnerabilities by quantifying corresponding Pareto optima using the supply-demand flow model. The derived vulnerability to the attack metric reflects the cost of a failed link, as well as the attacker's willingness to attack a link.  
The Pareto front separates the feasible region from an infeasible region [39]. The optimal operating point is computed by considering a network utility function. In this model, vulnerability to the attack metric is dependent on network topology, which makes it unsuitable for fully connected networks [40].  
[PERSON] and [PERSON] proposed a bio-inspired metaheuristic hybrid model that addresses the bi-objective network design problem. It combines Improved Strength Pareto Evolutionary Algorithm--SPEA2 and the Bacterial Chemotaxis Multi-objective Optimization Algorithm (BCMOA) [41]. The Pareto-optimal points approximations are faster than SPEA2 and Binary-BCMOA, however this method is not applicable to our model since we include more than two objectives.  
In recent years, multi-objective reinforcement learning has emerged as a growing area of research. [PERSON] et al. [42] used reinforcement learning to enhance the security scheme of cognitive radio networks by helping secondary users to learn and detect malicious attacks and nodes. The nodes then adapt and dynamically reconfigure operating parameters.
# Stride-TCN for Energy Consumption Forecasting and Its Optimization
## 3 Methodology
### Time Series Forecasting Problem  
First, we highlight the nature of the time-series forecasting task. Given an input sequence \(X=\{x_{0},\ldots,x_{T}\}\), where the task is to predict the outputs \(Y=(y\hat{0},\ \ldots,y\hat{p}\ )\) each time, a key constraint is that, from an ordered number of \(T\) observed data points, \(P\) data points must be immediately predicted chronologically. Formally, a modeling network is any function \(f:X\to Y\) that produces the mapping  
\[Y=f(X) \tag{1}\]  
Building function \(f\) is the process of learning to find optimal parameters of network \(f\) from a set of time series \(\left\{x_{0:T}^{(i)}\right\}_{i=1}^{N}\) that denotes the future time series as \(\left\{x_{(T+1):(T+P)}^{(i)}\right\}_{i=1}^{N}\), where \(N\) is the number of series, \(T\) represents the length of the historical observations, and \(P\) indicates the length of the forecasting horizon. The learning process is the process of minimizing the error function \(L(Y,f(X))\) between the actual output and predictions. Time-series forecasting generally focuses on the prediction of real values, usually loss functions, such as the mean squared error (MSE), mean absolute error (MAE), or its variants (mean absolute percentage error, root mean square error, etc.). The MSE is greater for learning the outliers in the dataset, whereas MAE is good for ignoring outliers. However, in some cases, the data are less sensitive to outliers, and those points should not have high priority. Therefore, the Huber loss combines the proposed MAE and MSE and solves this problem [6]. The mathematical form of the Huber loss is:  
\[L(Y,\ f(X))=\left\{\begin{aligned} \frac{1}{2}(Y-f(X))^{2}& \quad\text{for}\ |Y-f(X)|\leq\delta\\ \delta|Y-f(X)|-\frac{1}{2}\delta^{2}&\quad\text{ otherwise}\end{aligned}\right. \tag{2}\]  
where the \(\delta\) parameter is sensitive to outliers. In this study, we used the Huber loss as the error function to calculate the difference between the current and expected output where the \(\delta\) parameter is fine-tuned by the algorithm.
## 3 Results
### Stability Test of MNDVI Estimation for \(f\)PAR  
In Section 3.2, we found that MNDVI has high stability and correlation in estimating \(f\)PAR during the entire critical growth period of winter wheat. However, whether using the same correlation equation for \(f\)PAR estimation during the critical growth period can maintain high accuracy across different varieties and irrigation schemes still needs to be verified. Therefore, in this study, the estimated values of \(f\)PAR were obtained based on the following equation:  
\[f\text{PAR}=0.0772\text{e}^{2.7526\text{MNDVI}}, \tag{4}\]  
To verify the accuracy of the estimated values, the study used actual measured values for validation and used the coefficient of determination R\({}^{2}\) and the root mean square error RMSE as accuracy evaluation indicators to assess the robustness across different varieties, irrigation schemes, and dates.
## 4 Discussion  
The proposed framework has been verified on both an interactive simulation of the Stanford Bunny model and a training simulation of vaginal examination during labour. The number of vectors required to render a virtual scene was reduced from 1048 to 48 in the Stanford Bunny simulation and from 9160 to 104 in the VE training simulation. The number of control points is higher in the VE training simulation, but the maximum latency is higher in the Stanford Bunny simulation. This is because the latency varies depending on the network state. In the Stanford Bunny and VE training simulation, the average latency is reduced to 6 and 7 ms, respectively, which is less than 15 ms, the maximum allowable latency to avoid motion sickness in VR simulation [22]. In AR simulations, the user can still see the real environment through the HMD, unlike in VR simulations. Therefore, the latency only causes a visual discrepancy between real-world and overlaid virtual content. For this reason, the latency condition for avoiding motion sickness can be further alleviated in AR simulations. The maximum relative error was 29% and 2.8% in the Stanford Bunny and VE simulation, respectively. In the Stanford Bunny simulation, the model was deformed by a user interaction without a predefined scenario; therefore, the error increases proportionally to the magnitude of deformation. If the global deformation cannot be predefined and the deformation induced by the interaction is large, the error could increase in the proposed framework. In the VE training simulation, however, the relative error was less than 3% because deformed shapes were predefined as part of the training scenario, using the closed and dilated cervix shapes as the target shapes, and the global deformation was transmitted by a weighted value. The number of control points can be further reduced whilst maintaining or improving the accuracy of the transmitted mesh by using advanced free-form deformation techniques [23; 24] in the proposed framework.  
Figure 12: Root mean square position error of the cervix model over simulation time steps: (**a**) the cervix model is closed; (**b**) a user is pulling the cervix model to the right; (**c**) the cervix model is dilated; (**d**) a user is pulling the dilated cervix model to the left.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 9 Discussion
### Infrastructure  
The water station infrastructure significantly impacted network performance. In the Wi-Fi network, the distribution of the wells was advantageous, enabling the division of the main tank network into three sectors. This division allowed for the use of higher gain, more directional sector antennas rather than standard omnidirectional ones, also preventing a total network failure if one sector malfunctioned, as the others could continue operating.  
Conversely, for the Trusted Wireless Cluster Tree network, the infrastructure posed challenges to this topology. As seen in the proposed design, CHs T/1/677/0/96, T/1/257/0/96, and T/1/235/0/96 are closely positioned, and their remote nodes have multiple paths to the Base Station (BS). However, CH ww2 acts as a bottleneck, as its remote nodes lack alternative routes to the BS, meaning a loss of link to this CH could result in the loss of an entire section.  
Additionally, the arrangement of the wells in relation to the secondary tanks precludes the use of a mesh approach, restricting the network to a star topology, which limits the functionality of Trusted Wireless. Despite these limitations, Trusted Wireless's mesh capability proved advantageous in linking the two networks, unlike Wi-Fi, which required a dedicated point-to-point link.
# The Blockchain Oracle Problem in Decentralized Finance--A Multivocal Approach
## 2 Literature Background
### Related Works  
There is still a paucity of works on decentralized finance and even more scarcity on the role of oracles in this field. However, other authors offered perspectives that were useful for the development of this paper. Werner et al. [49], Schar [50], Harvey et al. [51], and Amler et al. [21] provided systematization of knowledge in a broad sense of the concept of decentralized Finance. They discussed many DeFi applications, as well as related opportunities and challenges, and they also provided a small overview of the role of oracles. Liu et al. [17] and Kumar et al. [20] instead focused precisely on blockchain oracles implemented in decentralized Finance. While Kumar et al. [20] offered a theoretical proposal for a DeFi oracle, Liu et al. [17], using primary data, discussed the reliability of oracles in DeFi considering the deviation rate of Ampleforth, MakerDAO, Synthetix, and Compound. Gu et al. [32] and Angeris and Chitra [52] finally produced a piece of research on blockchain oracles in DeFi for a specific application. The first discussed the role of oracles in the governance of non-custodial stablecoins, particularly analyzing the passage of MakerDAO from version V1 to version V2. The second instead focused on the role of oracles implemented for Automated Market Makers. An interesting study, while not directly related to this one by Kaleem and Shi [53], showed the increasing rate of oracle-related queries on the Ethereum platform. As expected, most are initiated by DeFi platforms.
# Mobility-Multihoming Duality
## 6 Results of Evaluation
### TCP Misordering  
Figure 9a on page 26 shows misordered packet counts for the TCP scenario. Across the different delay configurations and in both directions, TCP over ILNP performed consistently, with negligible misordering observed.
## 5 WST Applications
### Greenhouses  
The automation and efficiency on greenhouse environment monitoring and control are crucial. In order to control and monitor the environmental factors, sensors and actuators are essential. Greenhouse crops can benefit a lot using WST, because inside the greenhouse the crop conditions such as climate and soil do not depend on natural agents. Thus, the implementations are easier than in outdoor applications.  
The first application of WSN in a greenhouse was reported in the year 2003, it was a monitoring and control system developed by means of Bluetooth [81]. Since that year, several applications has been developed, most of them makes use of IEEE 802.15.4/ZigBee:  
[PERSON] and [PERSON] presented a proposal of a distributed greenhouse control and monitoring system using ZigBee [82]. [PERSON] describes the results of real deployment of a WSN IEEE 802.15.4 compliant to monitor and control the environment in greenhouses with melon and cabbage [11].  
[PERSON] developed a WSN in a greenhouse, that integrates a variety of sensors which can measure substrate water, temperature, electrical conductivity, daily photosynthetic radiation and leaf wetness in real-time. Benefits came from an improved plant growth, more efficient water and fertilizer applications, together with a reduction in disease problems related to over-watering [13].  
[PERSON] reported a WSN prototype with two-part framework for greenhouses. In the first part, several sensor nodes were used to measure temperature, light and soil moisture. The other part consists of GSM module and the management software based on database running on the remote PC [15].  
[PERSON] designed a monitoring system based on ZigBee, using an star network topology inside the greenhouse and a mesh topology for the connection between the greenhouses and the management system [19].  
[PERSON] reported a multi-functional remote sensing system that integrates RFID technology with spectral imaging and environmental sensing in a greenhouse. The multi-spectral imaging system was used for remote sensing of the canopy of cabbage seedlings. Greenhouse temperature, relative humidity, and lighting conditions were measured above the crop [83].  
[PERSON] develop an specialized wireless sensor node for monitoring temperature, relative humidity and light inside greenhouses [84].
# Development and Application of a Novel Tsunami Monitoring System Based on Submerged Mooring
## 3 Key Technologies  
The tsunami monitoring system proposed in this article uses its anchoring system to deploy pressure sensors near the seabed to record real-time seabed pressure data. When a tsunami wave passes through the sea surface where the pressure sensor is located, it will cause a change in wave height, which, in turn, will cause a change in seabed pressure. According to the recorded historical pressure, the sea surface height can be inferred as the height of the tsunami wave. When confirming the tsunami wave, the original pressure data are sent to the remote data center on the ground through satellite communication buoys for real-time tsunami warning analysis [22].
## 3 Engineering Case Study
### Establishment of the Landslide Seepage Field Model
#### 3.3.2. Landslide Seepage Model  
The main profile of the Majiagou landslide was selected, and a two-dimensional seepage model of the landslide was established based on the finite element program SEEP/W (Figure 4b). The horizontal width of the model was 650.0 m, and the vertical height was 292.0 m. The model was divided into triangular and quadrilateral meshes with 5329 mesh elements and 5217 nodes in total. The landslide model contained three different geological materials. Because rock fissures cannot be considered in the model calculation, the seepage of rock fissures was simplified into a seepage problem that was equivalent to a continuous medium. The left side was set as a variable head according to the generalized RWL model as Figure 4a shows, the bottom was set as an impermeable boundary, and the  
Figure 4: Boundary conditions and seepage model for Simulation: (**a**) dynamic water head boundary; (**b**) seepage model for simulation.  
Figure 3: Time series of GWL, RWL and rainfall.  
right-side boundary was set as a fixed head of 240 m. Three water level monitoring holes, S1, S3, and S8, were set on the landslide model, which corresponded to on-site monitoring boreholes JC1, JC3, and JC8.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 4 Detection and Verification Method
### Verification Method  
As shown in Figure 12, the optical polygon with 23 faces is used as the reference angle artefact. The rotating shaft and regular optical polygon are connected with a clamp. The small eccentric installation of the optical polygon will not cause the measurement error of the autocollimator (TRIOPTICS Company, Wedel, Germany, model TriAngle US 300-57; resolution: 0.005''; accuracy: 0.25''). The mating surface of the optical polygon and rotating component are parallel to each other and orthogonal to the axis average line of the rotating component. The autocollimator is adjusted to aim at the optical polygon. The actual angle can be obtained by the use of the autocollimator and optical polygon. The actual angle subtracted from the angle obtained by the rotary encoder is the angle measurement error. Put the angles obtained by rotary encoder \(\theta\) into the angle measurement error model \(\Delta\theta\), and we get the residual error after comparison.