esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
###### Abstract  
Achieving global coverage and performance goals for 6G requires seamless integration of satellite and terrestrial networks, yet current operational frameworks lack common standards for managing these heterogeneous infrastructures. This paper addresses the critical need for unified satellite-terrestrial network operations by proposing the CMS framework, a novel task-scheduling-based approach that bridges the operational gap between satellite operations (SatOps) and network operations (NetOps). The framework integrates satellite-specific constraints with network service requirements and QoS metrics through constraint satisfaction programming and multi-objective optimization. Three novel architectures are introduced: integrated operations (embedding NetOps within SatOps), coordinated operations (unified control with separate execution channels), and adaptive operations (mutual adaptation through intelligent interfaces). Each architecture addresses different connectivity scenarios and integration requirements for both sporadic and persistent satellite constellations. The proposed architectures are evaluated against challenges spanning infrastructure and architecture, interoperability and standardization, integrated management, operational dynamics, and technology maturation and deployment. Validation through simulation demonstrates significant performance improvements, with task completion rates improving by 17.87% to 44.02% and data throughput gains of 25.09% to 93.62% compared to traditional approaches. The CMS framework establishes a resilient operational standard for future 6G networks, offering practical solutions to bridge the current divide between satellite and terrestrial network operations.  
6G; satellite-terrestrial integration; non-terrestrial networks; operational framework; integrated network architecture; MANO 2025, 13, 312[[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). 2025, 13, 312[[https://doi.org/10.3390/technologies13080312](https://doi.org/10.3390/technologies13080312)]([https://doi.org/10.3390/technologies13080312](https://doi.org/10.3390/technologies13080312))
## 4 Dummy Generation Scheme
### Dummy Location Generation Algorithm  
To meet \(<L_{(k,s)},Q_{l}>\), we need to achieve two goals. (1) Each location evenly appears in a continuous query; (2) in each query, \(k\) dummy locations do not locate in the same organization. Therefore, we take two measures to ensure that the two goals are achieved. Firstly, the user's real location is not submitted to the LBS server but is replaced by a historical location in the same organization as the user's real location. Secondly, we add an identifier \(Ide\) for each location in \(U_{ij}\) to ensure selected dummy locations are different in each query request \(Req\). We set \(Ide\) to \(1\) each time a location is selected. When the identifiers of all locations in \(U_{ij}\) are set to \(1\), which represents that all locations in \(U_{ij}\) are traversed, all identifiers for each location in \(U_{ij}\) are set to \(0\) to start a new round of selection. While we use a historical location in the same organization to replace the user's real location to protect the real one from being identified, because of the user's real location and the dummy location that replaces the real one in the same organization, the loss of service quality is small. Based on the above goals and measures, we design an algorithm to generate dummy locations. Algorithm 3 shows the formal description of it. First, we determine the second-level grid \(U_{lo,j}^{sub}\) which the current location \(L_{0}\) belongs to and then use \(U_{lo,j}^{sub}\) as the starting point to randomly select \(s-1\) second-level grids from near and far. The \(s\) second-level grids are expressed as \(SG=\{U_{lo,j}^{sub},U_{i_{1},j+1}^{sub},U_{i_{2},j+2}^{sub},\cdots,U_{i_{s-1}, j+s-1}^{sub}\}\). Then, we randomly select one location from each grid of \(SG\) and obtain \(s\) locations \(L_{1},L_{2},\cdots,L_{s}\) which are located in \(s\) different second-level grids. We also set each identifier \(Ide\) of the selected historical locations to \(1\). Second. we randomly select the next \(s\) locations \(L_{s+1},L_{s+2},\cdots,L_{2s}\) from the historical locations of \(Ide=0\) in \(s\) grids of \(SG\), and set the identifier \(Ide\) of selected historical locations to \(1\). Then, we repeat the above procedure until the remaining \(k-2s\) locations are selected. Finally, \(k\) locations \(L_{1},L_{2},\cdots,L_{k}\) are selected and evenly distributed in \(s\) second-level grids. Among them, we use \(L_{1}\) to replace the user's real location \(L_{0}\) to prevent the server from distinguishing \(L_{0}\) from other dummy locations by comparing with historical location data it owns. For simplicity, we assume that there are enough historical locations in the historical location dataset (in fact, we can also get enough historical locations) to ensure that users can choose different historical locations each time. The specific algorithm is described as follows.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ4--Edge Intelligence Application Domains
#### 4.4.4 Intelligent Transport System (ITS)  
Smart cities are cities that incorporate information and communication technologies (ICT) to improve the quality and performance of urban services such as communication, governance, safety, energy, sustainability and transport.  
The Internet of Vehicles (IoV) is an emerging paradigm that is driven by recent advancements in vehicular communications, networking and processing power [148]. This new conjecture enables a plethora of new exciting applications [26; 40; 43; 45; 51; 52; 56; 58; 75; 148; 149; 150; 149; 151] that go beyond the concept already well disseminated of autonomous vehicles as smart parking systems, unmanned aerial vehicles (UAVs), traffic monitoring, intelligent roadways, etc.  
However, such as in the other domain fields analyzed in this paper, centralized processing also presents important drawbacks in the ITS subject. Autonomous systems, in general, require real-time response since any delay or latency in transmitting information could be extremely dangerous. Furthermore, monitoring services are based on sound, image and/or video analysis which represents tons of megabytes of data per second that are estimated to increase [159].  
[PERSON] et al. [51] use EI to achieve higher results in QoS and Quality of Experience. It is based on the principle that the infrastructure must provide mechanisms of collaborative sensing and cognitive services to Cognitive Internet of Things applications. As a proof of concept, the authors implemented four applications and services. One of the services is Content Sharing of Vehicular Networks that uses the strategies Heterogeneous Information Network (HIN), Latent Semantic Indexing (LSI) and Collaborative Filtering (CF) to determine the popular content for storage in caches of Roadside Units.
## 3 Related Studies and Motivation
### Motivation  
Figure 5 shows when a node and switch fail in an existing network routing path and the network routing transmission path is disconnected. In the event of a failure, it is difficult to find an efficient path to which links should be used to transmit data to the destination node. In other words, an optimized routing path is needed to measure the network bandwidth that changes in real time to avoid the link with the best link bandwidth and when nodes fail due to natural geological and other factors. In addition, when selecting links, reducing the link transfer cost as much as possible should also be considered, as mentioned in the erasure coding-based improved study.  
Figure 5: Example of a failure in a network routing path.  
Therefore, in this study, we apply DQN-based deep reinforcement learning to a network environment configured with an erasure-coding-based distributed file system. In addition, we propose a methodology that adds not only the dataset related to network routing as input values to the neural network model applied, but also the input parameter values required for decoding in erasure coding, and modifies the layers used in the neural network model.
# Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems
## 5 Experimental Setup  
All optimization algorithms were assessed using the benchmark functions provided in the Congress on Evolutionary Computation (CEC) 2022 suite. For each function, every algorithm was executed across **30 independent runs.** Each run employed a population size of \(N\), where \(N=\) (_specify value here_). The initial population for all algorithms was randomly initialized within the specified boundaries of each function, ensuring consistency by using the same random seed across all experiments.  
The performance metrics reported include the mean, standard deviation (Std), and standard error of the mean (SEM), which are defined as follows:  
* **Mean:** The arithmetic mean of the best fitness values obtained over the 30 runs.
* **Standard deviation (Std)**: A measure of the variability in the fitness values across the 30 runs.
* **Standard error of the mean (SEM)**: Computed as \[\text{SEM}=\frac{\text{Std}}{\sqrt{30}}\]  
_IEEE CEC2022 Benchmark Functions_  
The CEC2022 benchmark suite (see Table 1) comprises 12 standard optimization functions that are widely recognized for evaluating the performance of optimization algorithms. These functions present a diverse range of mathematical challenges, including unimodal and multimodal landscapes, as well as hybrid and composition functions. Each function is defined within a 10-dimensional search space with boundaries \([-100,100]\). The suite includes simpler functions such as Zakharov (F1) and Rosenbrock (F2), alongside more complex problems like Rastrigin's (F4), Levy (F5), and a series of hybrid (F6-F8) and composition functions (F9-F12). The minimum known objective values (\(F_{\text{min}}\)) for these functions vary, offering a spectrum of difficulties designed to assess the robustness and efficiency of optimization algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Problem No.** & **Problem Name** & **Dim** & **Range** & \(F_{\text{min}}\) \\ \hline F1 & Zakharov Function & 10 & \([-100,100]\) & 300 \\ F2 & Rosenbrock’s Function & 10 & \([-100,100]\) & 400 \\ F3 & Schaffer’s F7 & 10 & \([-100,100]\) & 600 \\ F4 & Rastrigin’s Function & 10 & \([-100,100]\) & 800 \\ F5 & Levy Function & 10 & \([-100,100]\) & 900 \\ F6 & Hybrid Function 1 & 10 & \([-100,100]\) & 1800 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CEC2022 benchmark functions.
## 3 Multi-Beam Fast Tracking and Satellite Capture Method
### The Multi-Beam Scheme for Antenna Loss of Satellite  
For the second stage of the SOTM system, when the SOTM antenna loses satellite connection, it is likely that the midpoint of the directional pattern is not within the range formed by connecting points A, B, C, and D in Figure 10. In this case, calculations need to be performed. Here, we take the azimuth angle as an example to illustrate. First, the azimuth directional pattern of the main lobe is given, as shown in Figure 11.  
First, take the two points in the directional pattern of the four beams with the largest difference in azimuth angle. Assume that these two points are points C and D, with coordinates \((A_{c},E_{c})\) and  
Although the equation of the curve is known, it contains many nonlinear components, including logarithmic functions, exponential functions, trigonometric functions, and inverse trigonometric functions. From a mathematical point of view, this processing process is relatively simple and does not require much discussion. However, from the perspective of engineering practice, the processing of these functions poses a great challenge to microprocessors. The processing process will consume much time and require high computational and storage performance of microprocessors. Therefore, a piecewise linearization method is adopted here to segmentally linearize the above functions, which greatly reduces the difficulty of microprocessor processing.  
Figure 13 shows a schematic diagram of the piecewise linearization method for the azimuth directional curve of the main lobe. The black dots in the figure represent several points taken along the curve. These points are then connected by straight lines to form the red line segments denoted as \(a\), \(b\), \(\ldots\), \(i\) in Figure 13. The equation of each straight line is calculated as follows:  
\[\left\{\begin{aligned} & f(a)=\rho_{a}x+\sigma_{a}\\ & f(b)=\rho_{b}x+\sigma_{b}\\ &\vdots\\ & f(i)=\rho_{i}x+\sigma_{i}\\ \end{aligned}\right. \tag{4}\]  
Figure 12: Calculation of the main lobe azimuth.  
Figure 13: Piecewise linearization of the antenna pattern of the main lobe azimuth.  
By storing the parameters of each straight line and the coordinate values at the endpoints on the local storage of the microprocessor, real-time data processing can be performed for this solution. It should be noted that the closer to the center point O, the more accurate the pointing needs to be, and the higher the requirement for the accuracy of the data. Hence, as we approach point O, the line segments become shorter, and the endpoints become more densely distributed.
## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WLAN-Based IoT
#### 4.2.3 Solutions for IEEE 802.11/802.11 ah Multi-Hop Communication Collision  
While using IEEE 802.11 ah in IoT networks, [PERSON] _et al._[72] and [PERSON] _et al._[73] introduced a collision avoidance mechanism called virtual grouping for Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). Based on a random Arbitration Inter-Frame Space Number (AIFSN) scheme, they support two types of stations (STAs), _i.e._, contending STAs and non-contending STAs. After data transmission, an STA obtains a new AIFSN value. If the obtained value is lower than a predefined threshold, then the STA is placed in contending STAs. Otherwise, it is placed in the block of non-contending STAs. Power saving is achieved by moving non-contending STAs to a sleep mode. However, the overhead of dynamic grouping based on data availability should consume additional energy but it is not considered. [PERSON] _et al._[74] proposed a packet buffering strategy to store packets at an AP if devices are in a sleep mode. The AP sends a message to such sleeping devices during specificintervals based on their category, either Traffic Indication Map (TIM)-based or non-TIM-based. Packets are then transmitted only to TIM-based devices while all non-TIM devices are kept in a sleep mode to further save power. Power saving mechanisms can also be deployed on Android smart phones. [PERSON] _et al._[69] presented an end user power saving control in Android devices in which they extended Android framework for power saving control and moved that control to end users. The methodology provides end users with the application control of the IEEE 802.11 interface power management so that different energy saving schemes can be implemented. [PERSON] _et al._[75] proposed a collision avoidance mechanism called Restricted Access Window (RAW) mechanism. The mechanism allows an AP to allocate a medium access period, which is divided into one or more time slots. RAW information is carried in beacons transmitted by AP. Time slots are assigned to a group of STAs and they are allowed to access the medium accordingly, during the allocated slots. As a result, chances of collision get reduced and power saving for devices can be achieved.
# Interval Extended Kalman Filter--Application to Underwater Localization and Control
## 4 Test-Cases
The three localization methods have been implemented for this problem. Figure 3 shows the block diagram of the closed loop system. The IEKF provides the estimates \(\mathbf{\hat{p}}\) and \(\hat{v}_{r}\). The three localization methods are then compared with a closed loop simulation where the system is simulated with the same Euler discretization (the Runge-Kutta methods have shown similar results). Two scenarios are simulated with two different initial positions for the robot:  
* In the first one, the initial estimation of the position of the robot has an error of 1 m.
* In the second one, the initial position is chosen to make the Extended Kalman Filter diverge. The initial error is large, approximately 23 m (see Figure 7).
## 2 Generation of Precision Timing Information
### Determination of Clock Weights  
In the _AT1_ algorithm, a crucial step involves determining the weights (\(w\)) assigned to each clock. These weights are generally inversely proportional to their errors i.e. clocks with higher errors receive lower weights. Different algorithms use different approaches for determining weights. Some rely on error variances [9]. Typically, an administrative weight limit is also put on the weights (like defining a maximum value of the weight that a clock in the ensemble can get) such that the ensemble is not biased to only one type of clock in the ensemble [20].  
For this paper, the weights are determined based on the estimation error of each clock. Let \(A\), \(B\), and \(C\) represent the estimation errors and \(W_{HM}\), \(W_{C1}\), and \(W_{C2}\) represent the weights for the H-maser, cesium 1, and cesium 2, respectively. The weights and the errors are inversely proportional to each other: \(W_{HM}\propto 1/A\), \(W_{C1}\propto 1/B\), \(W_{C2}\propto 1/C\). The sum of the weights equals 1, as shown by Equation (2). An administrative limit of 0.5 is imposed to ensure that the weight of any clock does not exceed 0.5. As an example, Equations (2) and (3) show how the weights for any given clock, e.g., hydrogen maser, are calculated.  
Figure 3: Flowchart of the system setup at the Time and Frequency Lab at FP2.  
\[W=K(W_{HM}+W_{\text{C1}}+W_{\text{C2}})=1 \tag{2}\] \[K=ABC/(AB+BC+CA)\] (3) \[W_{HM}=K/A=BC/(AB+BC+CA) \tag{4}\]  
The weights for the rest of the clocks can be estimated in a similar manner. The implementation of the algorithm results in the estimates of all clocks in the system with respect to the ensemble time i.e. \(x_{1e}\), \(x_{2e}\), and \(x_{re}\) for cesium clock 1, cesium clock 2, and H-maser, respectively.
## 3 Results and Discussion
### ABTS Radical Cation Scavenger  
The results of antioxidant activity are shown in Table 1. The ABTS assay showed significant differences between the UPG and the CG. For the CG (539 uM TE), the less intense treatments UPG-42 and UPG-52 showed significant decreases in activity (301 and 209 uM TE, respectively), while the higher intensity treatments UPG-71 and UPG-84 showed significant increases (698 and 1001 uM TE, respectively; \(p<0.05\)). Therefore, the conformational changes that were generated by the treatment with 100% amplitude increased the capacity of the gelatin for scavenging free radicals, while the conformations that were adopted as a result of the treatments with 50% amplitude caused the free radical scavenger activity to decrease. These results could be attributed to the fact that, according to the results of the circular dichroism, the CD ratio of the random coil was the lowest in UPG-42 and UPG-52; however, more studies are needed to confirm this.  
According to this assay, the gelatins in this study showed antioxidant activity in the range of 200-1000 uM TE/mg of gelatin, and these results are comparable to hydrolysates of green beans (200-350 uM TE/mg sample) [28] and rainbow trout hydrolysates (400-1200 uM TE/g sample) [14].
## 3 Results and Discussion
### Gain and Radiation Efficiency  
Figure 11 shows the predicted and measured gain of the proposed reconfigurable antenna. It can be seen from Figure 11a, when both diodes are in ON-state the antenna exhibits a gain of <2.5 dBi in the operational band along with radiation efficiency of <90%. However, the antenna in multiband mode shows the gain of <2.4 dBi in passband regions along with radiation efficiency of <89% (Figure 11b-d), while for the band stop region the gain and radiation efficiency of the proposed antenna decreases up to \(-8\) dBi and 22%, respectively. A strong agreement between simulated and measured results state the performance stability of the antenna. Table 2 shows the comparison between measured and simulated results for different states of PIN diodes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Switching** & **Diode** & \begin{tabular}{c} **Simulated** \\ **Bandwidth** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Measured** \\ **Bandwidth** \\ **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Smalled** \\ **(GHz)** \\ \end{tabular} & \
\begin{tabular}{c} **Measured** \\ **(dBi)** \\ \end{tabular} &
\begin{tabular}{c} **Peak Gain** \\ **(dBi)** \\ \end{tabular} \\ \hline \multirow{2}{*}{Case-11} & D1-On and & & & & \\  & D2-On & 2.29–4.47 & 2.25–4.53 & 3.86 & 3.8 \\ \hline \multirow{2}{*}{Case-01} & D1-On and & & & & \\  & D2-Off & 2.3–2.65 & 2.1–2.78 & 2.44 & 2.39 \\  & D2-Off & 3–4.5 & 3.09–4.57 & 3.91 & 3.82 \\ \hline \multirow{2}{*}{Case-10} & D1-Off and & & & & \\  & D2-On & 2.1–2.4 & 1.86–2.5 & 2.21 & 1.89 \\  & D2-On & 2.92–4.37 & 3.03–4.47 & 3.89 & 3.82 \\ \hline \multirow{2}{*}{Case-00} & D1-Off and & & & & \\  & D1-Off and & 2.64–2.85 & 2.62–2.93 & 2.01 & 1.96 \\  & D2-Off & 3.19–4.34 & 3.19–4.65 & 3.9 & 3.79 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison among simulated and measured values for various switching states.  
Figure 11: Simulated and measured gain along with radiation efficiency: (**a**) case-\(11\) (**b**) case-\(01\) (**c**) case-\(10\) (**d**) case-\(00\).