esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Result and Discussion
### Comparative Methods
#### 5.5.2 Comparative Analysis Based on K-Fold  
Figure 6(a)-d show the comparative analysis based on k-fold for accuracy, NPV, PDV, and threat score. The BCHO-TCN LightGBM model demonstrates superior accuracy in predicting out-of-stock events, with a 10.09% improvement over the vulture-TCN LightGBM model, achieving an accuracy of 94.81%.  
The BCHO-TCN LightGBM model shows better performance in predicting out-of-stock situations compared to the vulture-TCN LightGBM model. It surpasses the vulture-TCN model by 1.04% and achieves an NPV of 95.60% with a TP of 90.  
The BCHO-TCN LightGBM model outperforms the vulture-TCN LightGBM model in out-of-stock prediction by a margin of 0.40%. With a TP of 90, it boasts a PPV of 96.28%, surpassing existing methods.  
The BCHO-TCN LightGBM model shows better performance in predicting out-of-stock events compared to the vulture-TCN LightGBM model. It surpasses the vulture-TCN model by 5.84%, achieving a threat score of 95.86% with a TP of 90.  
Figure 6: Comparative analysis based on TP: (**a**) accuracy, (**b**) NPV, (**c**) PDV, and (**d**) threat score.
## 4 The Role of Routing Protocols to Resilience in Self-Configuring WSNs  
In WSNs, resilience capabilities can be provided by the self-configuring and adaptation mechanisms, for instance the power transmission of the nodes can be adjusted dynamically, the MAC algorithm may be aware of the wireless medium in order to optimize the performance and the routing protocol can change the topology according to the state and availability of the radio links, energy consumption, traffic balance, quality of service and other criteria. For some well-known routing protocols for WSNs, self-configuring is inherent to the design; reactive protocols, such as AODV [15] and DSR [16] update the routes when they are required; while in the MPH routing protocol [6], nodes update the routes on a periodic-time basis and when changes in the neighborhood have been detected. Despite the differences among the three aforementioned routing protocols, they use self-configuring mechanisms to build and update the topology when the conditions of the wireless environment and the available resources change. As a result, resilience to faults and attacks can be intrinsic to the above protocols. In this section, we will explain the fundamentals of the above protocols and present a brief example of their behavior under a jamming attack.
# Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM
## 4 Network Traffic Anomaly Detection Scheme Based on LNN-RLSTM Neural Network
### Initial Labeling of Exception Isolation Points  
Anomalous values have a negative impact on the accuracy of the analysis results of intelligent O&M. If the anomalous values are examined through the analysis of the trend components in the time-series model, they will be too large and complex for the operation and maintenance scenarios and the data will lose timeliness. Therefore, for a group of brand-new, unlabeled data, a traditional simple test method is needed for fast initial labeling of the data, which makes big data anomaly detection relevant. As a modification of the \"standard deviation method\", the quartile method uses the median and standard interquartile distance to estimate the dataset instead of the mean and standard deviation in the \"standard deviation method\", respectively. It has a high robustness and perfectly fits the analysis of a large amount of data in operation and maintenance scenarios. At the same time, it is also free from the constraints of normal distribution and has higher generalizability.  
Through the preliminary observation of the data, it can be found that: the user perception class network KPI data have obvious seasonality and seasonal variation by days, and although these time-series data do not conform to the positive-terrestrial distribution from the whole, the positive-terrestrial distribution can be proved for each cycle, as shown in Figure 3, and the positive-terrestrial distribution characteristics of the user perception class KPI data within the cycle can be proved through the QQ chart observation. The quartile method can have good robustness in the face of traditional anomaly detection tasks. Figure 4 illustrates the anomaly detection process for data using the quartic method, which will be subsequently extended and used to identify anomalous cycles.  
Figure 3: Regarding the QQ chart of various KPI data.  
Therefore, a sliding window is applied, the data in one cycle at a time for quadrature detection are framed and the data with a preliminary hit are labeled.
## 3 Experiments  
### Experimental Setup  
In order to verify the feasibility and validity of the proposed method, a diurnal variation observation system consisting of a tri-axial magnetometer (MAG-03 MS100, Bartington, Witney, Britain), a non-magnetic theodolite (TD)2E-NM, Boif, Beijing, China) and a differential GPS (IMU-IGM-S1, Novatel, Calgary, AB, Canada) was built in the experiment. The performance specifications of the experimental setup are shown in Table 1.  
As described previously, the magnetometer error, misalignment error, and alignment to the north are calibrated successively. The relevant experiment was carried out in an area within 100 m far from Changchun Geomagnetic station. There is no magnetic interference around the selected area. In addition, the six-channel spectrum analyzer Spectramag-6 supporting the use of Mag-03 MS100 magnetometer is adopted as the data acquisition unit.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Device** & **Quantity** & **Value** \\ \hline \multirow{4}{*}{Magnetometer} & Measuring range & \(\pm\)100 nT \\ \cline{2-3}  & Measurement noise floor & 6\(\sim\)10 pT/\(\sqrt{\text{Hz}}\) at 1 Hz \\ \cline{2-3}  & Non-orthogonality error & \(<\)0.1\({}^{\circ}\) \\ \cline{2-3}  & Scaling error & \(<\)\(\pm\)0.5\% \\ \cline{2-3}  & Offset error & \(\leq\)\(\pm\)5 nT \\ \hline \multirow{4}{*}{Non-magnetic theodolite} & Magnetic contamination & \(\leq\)1 nT \\ \cline{2-3}  & Plate level & 20\({}^{\prime\prime}\)/2 mm \\ \cline{2-3}  & Mean squared error of a horizontal & 2\({}^{\prime\prime}\) \\ \cline{2-3}  & measured face left/right & \\ \cline{2-3}  & Mean squared error of a vertical & \\ \cline{2-3}  & measured face up/down & 6\({}^{\prime\prime}\) \\ \cline{2-3}  & Initial horizontal misalignment & \(<\)20\({}^{\prime\prime}\) \\ \cline{2-3}  & Initial vertical misalignment & \(<\)25\({}^{\prime\prime}\) \\ \hline \multirow{2}{*}{Differential GPS} & Position accuracy (RMS) & horizontal 0.02 m \\ \cline{2-3}  & & vertical 0.03 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance specifications of experimental devices  
Figure 4: Relative position of site A and B.
## 4 Application of Methodology for Satellite Images
### Subjective Image and Segmentation Method Assessment
From the overall results in Table 6, the classical segmentation method FCM obtained one first place and the GMM two first places. Similarly, SFFCM obtained one first place and FRFCM two first places. Other special situations are presented in the discussion section. Thus, we can note that the quality of the results of segmentation algorithms depends on the image content's characteristics and the algorithm's sensitivity to the corresponding visual characteristics of the image. Figure 12: Satellite image “port_19” after segmentation: (**a**) RSSFCA; (**b**) SFFCM; (**c**) FRFCM; (**d**) FCM; (**e**) GMM; (**f**) original color image from AID. Figure 13: Satellite image “farmland_159” after segmentation: (**a**) RSSFCA; (**b**) SFFCM; (**c**) FRFCM; (**d**) FCM; (**e**) GMM; (**f**) original color image from AID.
## 2 Materials and Methods
### Data Analysis  
The pollutant data were not uniformly distributed, making the standard deviation unsuitable for estimating data errors. As a result, the standard error of the mean (SEM) was used instead. SEM is calculated using random samples from the original population and provides an estimate of the variability of potential sample means, much like how the standard deviation (SD) estimates the variability of individual observations. However, SEM is sensitive to extreme values and to the sample size. Typically, there is less variability in the sample mean values compared with the original population because mean values are used in the calculation of SEM. This indicates that SEM measures the precision with which the sample mean (M) estimates the population mean (\(\upmu\)). Therefore, SEM serves as an indicator of the degree of uncertainty in the mean estimate and is commonly used to calculate the confidence interval (Equations (3) and (4)):  
\[\text{SEM}=\frac{\text{SD}}{\sqrt{\upmu}}, \tag{3}\]  
\[95\%\text{CI}=\text{M}\pm 1.96\times\text{SEM}, \tag{4}\]  
where SD is the sample standard deviation, n is the sample size, and CI represents the confidence interval [38].  
To assess whether the levels of PM\({}_{10}\) pollution attributed to traffic were influenced by the national lockdown declared by the government on 15 March 2020, we employed non-parametric statistical methods. These methods, including the Kruskal-Wallis test, are appropriate for non-normally distributed data sets and do not require the assumption of normality [39]. The primary objective of the Kruskal-Wallis test is to determine whether independent samples, each containing observations from populations with inherent uncertainty, have similar medians. This would indicate that the populations under study, despite their variability, are statistically similar.  
This method relies on rank order, where all data points across groups are ranked together, and the test evaluates whether the ranks differ significantly between groups. By using ranks, the Kruskal-Wallis test is less sensitive to outliers and the specific distribution of data within groups, making it a robust alternative to ANOVA in situations where the data do not meet the assumptions of normality. The null hypothesis tested was that there are no significant differences between the median PM\({}_{10}\) values in 2020 and the corresponding medians for the control period, which includes data from 2017-2019 and 2021-2022 for each time interval determined by the governmental restrictions.
## 4 Environmental Parameters Measurement and Discussion of Results  
### Impact of Turbulence Factor on UWOC  
UWOC systems usually mean river or sea where there is flowing water. Therefore, the submerged motor is used to simulate the water flow (turbulence). The maximum water output is set up to 1200 L per hour. The output water flow can be stabilized to simulate water flow, as shown in Figure 2.  
The medium used in this experiment is clear tap water, but the tap water is not pure. When the signal light is transmitted in the water tank, it can be seen that there are some suspended particles in the light path. When the motor is turned on, these impurities will quickly float in the water in the whole tank, and more particles can be seen on the signal light path while the floating speed is faster. The optical power of the signal light before entering the water tank is 3.516 dBm. After three reflections of blue light in the water tank, the total transmission distance is 6 m, and the received optical power is \(-\)4.604 dBm. The water pump is turned on with 1200 L per hour. In 6-m transmission, the BER values varied to some extend due to water flow, as is shown in Figure 3a.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(P_{in}\)**(dBm)** & **Input Optical Power** & 16.99 \\ \hline \(P_{min}\)**(dBm)** & **Minimum received optical power (BER =10\({}^{-3}\))** & \(-\)15.96 \\ \hline \(L_{glass}\)**(dB)** & **Optical loss through the glass** & 0.35 \\ \hline \(L_{mirror}\)**(dB)** & **Reflection loss of mirrors** & 0.65 \\ \hline \(L_{water}\)**(dB/m)** & **Optical power loss in water** & 0.65 \\ \hline \(\mathbf{R}\) & **Fresnel lens reflection** & 4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental design parameters.  
Figure 2: Water-flow-induced experiment architecture.  
Therefore, water-flow-induced turbulence has little effect on light power for the clear tap water. As shown in Figure 3b, the result distribution range of the bit error rate is larger when the motor is turned on than when it is turned off. Nevertheless, it can be seen that there is a negative relationship between the disturbance of particles in water and the BER. It may be due to the fact that there is no significant amount of bubbles to block/reflect the modulated light source.
# Robust Model-Free Control for MMC Inverters in Cold Ironing Systems
## 5 Conclusions  
This paper presents a control approach for an MMC inverter equipped with an output LC filter. The key element of this research is the proposal of a control strategy that does not depend on the system model, uses only the system input and output, and does not include any inverter parameters, making it robust. The latter is based on an intelligent  
Figure 16: Real-time result for bus voltage to nonlinear charge MMC converter using, respectively, three control methods: PI conventional controller, iPI controller, and the proposed control in RT-LAB.  
proportional-integral ((i)PI) controller with adaptive gain, which enhances its ability to eliminate disturbances.  
This paper also presents a detailed description of the MMC system and a comprehensive analysis of the control technique, including a stability study according to [PERSON]'s criterion. To evaluate the effectiveness of this method, a comparative analysis with a conventional PI controller is performed under different operating conditions. Simulation results obtained using the MATLAB/Simulink environment are presented for nonlinear and variable loads. They show a significant improvement in voltage waveform quality and enhanced noise rejection capabilities.  
* Regulating the voltage at the PCC during a variable load.
* Reducing voltage harmonics for a nonlinear load with THD equal to 1.18%.  
Comparisons with the association of the proposed method with predictive control (MPC) are important for understanding the performance of the developed control strategy. This reduces dependence on precise models in predictive strategies. Consequently, in-depth comparisons between these methods are important for characterizing and optimizing the performance of the developed controls.  
Conceptualization, A.H.; Methodology, C.A.K.; Software, C.A.K.; Validation, A.H.; Writing--original draft, C.A.K., G.Y. and M.E.-B.; Writing--review & editing, N.A.-A., A.H. and M.A.-A.; Visualization, N.A.-A. and M.A.-A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Related Work
### Per-Pixel Classification  
Per-pixel classification emerged as the primary method for semantic segmentation using deep neural networks following the influential work of Fully Convolutional Networks (FCNs) [1], which established the fundamental approach to tackling semantic segmentation tasks. The U-Net [13] architecture employs a symmetric structure that down-samples feature maps and then restores them, demonstrating good performance in medical image segmentation. The DeepLab [14; 15; 16] series of networks effectively expands the receptive field through dilated convolution, enabling the extraction of rich feature information at different scales. In contrast to DeepLab, PSPNet [3] introduces a pyramid pooling module, replacing dilated convolution layers with multi-scale pooling layers to aggregate information from multiple scales. The HRNet [4] architecture connects multiple convolutional streams of varying resolutions in parallel and continuously exchanges information between these parallel streams to achieve resolution fusion.  
Certainly, the Transformer architecture has also demonstrated promising performance in semantic segmentation. ViT [7] first introduces the Transformer into semantic segmentation and achieves excellent results. Following that, SETR [17] replaced the traditional encoder-decoder architecture of FCNs with Transformer, efficiently capturing contextual information through global self-attention. Swin-Transformer [18] restricts attention computation to small windows, thereby enhancing computational efficiency. Metaformer [5] and SegNeXt [6] both demonstrate the effectiveness of the Transformer architecture. These two methods modify self-attention [12] into other CNN structures, yet the network still performs well. SCTNet [19] uses a complex Transformer to assist CNN training during the  
Figure 1: The mIoU versus parameters on Cityscapes. Seg50, Seg75, and Seg100, respectively, represent input image resolutions of 1536 \(\times\) 768, 1024 \(\times\) 512, and 2048 \(\times\) 1024, respectively. The orange triangle represents our model, while the blue circles represent others.  
training phase, and during inference, it relies solely on a single CNN branch, thus reducing the network's computational overhead while maintaining performance.  
Although the networks mentioned above have achieved certain results, we believe that traditional semantic segmentation heads still limit the network's performance. Therefore, this paper chooses a mask classification approach for semantic segmentation. It computes the features extracted by the backbone through multiple branches, applying different loss functions to constrain each corresponding branch, thereby enabling the network to achieve better performance.
# Mission-Oriented Real-Time Health Assessments of Microsatellite Swarm Orbits
###### Abstract
Such methods incorporate intelligent algorithmic techniques, and although they are effective for health assessment, they are difficult to model and represent the impact of components on the swarm. In contrast, research on effectiveness assessment dominates for swarm systems. [PERSON] [11] summarized the current research on the satellite communication system performance evaluation and proposed the prospect of applying neural networks in the satellite communication system performance evaluation. [PERSON] et al. [12] studied the risk of space debris caused by large constellation programs, which may collide with the constellation itself, and proposed a scheme to assess the vulnerability of the constellation system, so that the lifetime can be maximized. Although the above-mentioned literature assesses the communication status and collision risk of the constellation system, it is not enough to reflect the overall health of the constellation. To address the above issues, this paper proposes a real-time health assessment algorithm that integrates single satellite, intersatellite communication links, and swarm mission effectiveness based on the time-varying characteristics of mission-based swarm topology. Compared with the spacecraft assessment algorithm, this method fully considers the influence of intersatellite communication and swarm effectiveness on the health state. Firstly, the swarm health status is defined and an evaluation model is developed. Secondly, we calculate the health state based on the single satellite, the intersatellite communication and the swarm effectiveness. Then, the principle of variable power is introduced to deal with the case of unexpected failure. Finally, the method is applied to a swarm of modular microsatellites performing autonomous stitching missions. The health degree calculation and health state assessment of the time-varying swarm are realized by simulation.
## 7 Discussion  
A number of case studies were conducted to demonstrate and validate GloNet project results. The adopted validation methodology included three phases. In phase 1, the GloNet system was discussed with and tested by \(11\) lead users based on relevant business processes identified by these users. In phase 2, the best practice results achieved in phase 1 were presented to approximately 50 potential users within the same or similar business sectors as the lead users. The users group selected for this validation phase represented different application domains and diverse geographical regions in Europe. In phase 3, an in-depth validation of selected GloNet components was made by 40 potential users using real data from the Charanka power plant in India. In this case, a validation analysis was performed, where the \"required functionality\" by the end users was compared with the \"delivered functionality\" by the GloNet system. For each of the main GloNet software subsystems, specific questionnaires were filled in by the evaluators.  
The results obtained from these surveys regarding the Glonet VBE Management subsystem and the VO formation and operation support subsystem are presented in Figures 9 and 10. As it can be seen, GloNet solutions were considered positive in all adopted evaluation criteria. The value systems alignment analysis functionalities were not evaluated in isolation. However, as these mentioned subsystems embed the value system alignment analysis, as explained in Section 5.2, it can be inferred that the V-Align features satisfy the needs and expectations of the end-users.