esa-sceva/satcom-chunk-collection huggingface dataset:
# Alignment Method of an Axis Based on Camera Calibration in a Rotating Optical Measurement System
## 2 Principle
### Steps to Determine the Rotation Center of the Rotating Optical Measurement System
#### 2.3.2 Method for Coincidence of the Optical Center and the Rotation Center  
Since not enough of the optical centers can be obtained when the camera is rotating at one position, the camera is moved to different positions to collect more images, as shown in Figure 4, where c\({}_{i}\) (\(i=1,2,3\)) represent the three initial positions of the camera. In order to easily move the camera through the translations to align the rotation center, we need c\({}_{1}\)c\({}_{2}\)\(\perp\)c\({}_{1}\)c\({}_{3}\), c\({}_{1}\)c\({}_{2}\|O_{w}Z_{w}\), c\({}_{1}\)c\({}_{3}\|O_{w}Y_{w}\).  
Figure 4: The rotation diagram of the camera placed at positions c\({}_{1}\), c\({}_{2}\) and c\({}_{3}\).  
The LSC fitting [28] is performed to calculate the rotation center, which satisfies that the sum of the squares of the distances between each sample point and the fitting curve must be minimized. Let (\(y_{0}\), \(z_{0}\)) be the two-dimensional (2D) coordinates of the rotation center and (\(y_{j}\), \(z_{j}\)) (\(j\) = 1\(\sim\)\(N\), \(N\) is the number of camera rotation) be the 2D coordinates of the optical centers projected onto the \(Y_{w}\)\(O_{w}\)\(Z_{w}\) plane. In order to obtain (\(y_{0}\), \(z_{0}\)), the objective function is  
\[C=\text{min}\sum_{j=1}^{N}{[(y_{j}-y_{0})^{2}+(z_{j}-z_{0})^{2}-R^{2}]}. \tag{8}\]  
Theoretically, the radius of the fitted circle varies when the camera is moved, but the circle centers should remain unchanged. However, there are deviations among the fitted centers \(O_{ri}\) in the actual fitting. The initial rotation center \(\overline{O}_{r}\) is decided by the average of the \(O_{ri}\). We adjust the camera to \(\overline{O}_{r}\) and calibrate the camera again to get a set of new optical centers \(O^{\prime}\)\({}_{rj}\), whose average is \(O^{\prime}\)\({}_{r}\). The weighted average value of \(O_{ri}\) and \(O^{\prime}\)\({}_{r}\) is taken as the final rotation center \(O_{r}\) = (\(O_{y}\), \(O_{z}\)). The calculation formula is as follows:  
\[O_{y}=\sum_{m=1}^{n+1}{k_{ym}O_{ym}}, \tag{9}\]  
\[O_{z}=\sum_{m=1}^{n+1}{k_{zm}O_{zm}}, \tag{10}\]  
where \(O_{ym}\) and \(O_{zm}\) represent the coordinates of the fitted circle centers \(O_{ri}\) and \(O^{\prime}\)\({}_{r}\) in the \(Y_{w}\) and \(Z_{w}\) directions, respectively; \(m\) = 1, 2,... \(n\)+1, where \(n\) is the number of the fitted circles. \(k_{ym}\) and \(k_{zm}\) represent the weighted factors, respectively, which are decided by the variance \(\sigma_{m}\) = (\(\sigma_{ym}\), \(\sigma_{zm}\)) of each group of the optical centers by Equations (11) and (12),  
\[k_{ym}=\frac{\sigma_{ym}}{\sum\limits_{m=1}^{n+1}{\sigma_{ym}}}, \tag{11}\]  
\[k_{zm}=\frac{\sigma_{zm}}{\sum\limits_{m=1}^{n+1}{\sigma_{zm}}}. \tag{12}\]
## 2 Materials and Methods
### Test System Design and Test
#### 2.3.3 Detection Algorithm  
The You Only Look Once (YOLO) series of algorithms changed real-time object detection by making it a regression task, predicting bounding boxes and categories in one step, crucial for fast, accurate detections [14]. Among YOLO versions, YOLOv8 stands out due to its optimized network and training, boosting detection speed and accuracy. For our elite tea-picking robot, we chose YOLOv8 based on several strengths. Its efficient backbone enhances feature extraction, vital for detecting tea leaves of varying shapes, sizes, and colors. YOLOv8's improved loss functions and optimization speed up training and adaptation to tea leaf characteristics, boosting accuracy. Compared to YOLOv5, YOLOv8 handles challenges like complex backgrounds or object occlusion in tea gardens better.  
Figure 5: The schematic diagram of the visual system.  
Figure 6: The binocular camera. (**a**) Physical image of the camera, and (**b**) depth-measuring principle.  
Its innovative three-part network structure allows efficient feature extraction, fusion, and detection. This ensures accurate tea leaf detections across various scenarios. In short, YOLOv8's optimized network, efficient feature extraction, and faster training make it perfect for our tea detection needs, as shown in Figure 7.
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 3 System Model
### Edge Computing  
The edge computing paradigm is presented in the background of the fast evolution of IoT networks due to the traditional cloud computing paradigm is unable to solve the explosive growth of information and data [28]. By placing edge computing agents between the terminals and the cloud, partial computing tasks from the original cloud center are transferred to the vicinity of the data source for execution. This new network paradigm reduces data transmission's physical path and enables timely response to terminals' task requests. In other words, the computing and storage capacity of multiple edge agents shares the pressure of traditional cloud computing [29]. As we know, the architecture of edge computing is generally composed of three layers, including a terminals layer, edge computing layer, and cloud computing layer [30].  
Edge computing, as an emerging network infrastructure, empowers new capabilities for SSA in smart grids. Figure 2 shows the scenario of applying SSA under smart grids based on the edge computing paradigm. The power terminal layer includes primary power devices equipped with smart sensors, microgrid facilities, and intelligent charging piles. At edge SSA layer consists of edge agents that acquire the terminal layer's situational elements for awareness. Then, the edge SSA layer collaborates with the power cloud master station layer to accomplish SSA tasks.  
Since the edge agents are close to the power terminals, they possess the capability to interconnect a massive number of heterogeneous terminals. Task processing latency is dramatically decreased by the fact that data acquisition and analysis are carried out directly at the edge and avoid potential congestion of situational elements in other parts of the network. Practical deployment of edge computing has naturally decentralized characteristics. Such enable distributed computing and storage, dynamic scheduling, unified management of resources, thus possessing distributed security capabilities.  
Figure 2: Edge computing paradigm for smart grids security situational awareness.
# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 3 Experimental Architecture and Results
### System Architecture  
Figure 2 depicts the system architecture. A 5G-UAV is equipped with a D-link 5G router and is connected to the 5G innovation network. This connection makes use of the 5G local breakout core, which is located quite close to the experiment site. The position of the local breakout core satisfies the delay requirements of real-time robotics applications [9].  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Value** \\ \hline Mode & TDD \\ \(J\) & 1 \\ \(v\) & 1 in UL, 4 in DL \\ \(Q\) & 6 (64 QAM) \\ \(f\) & 1 \\ \(BW\) & 100 MHz \\ \(OH\) & 0.08 (3 GPP 38.306) \\ DL/UL Ratio & \(4:1\) \\ FR1/FR2 & FR1 \\ \hline Downlink & 1402 Mbps \\ Uplink & 94 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Utilized 5G innovation network—cell throughput capacity. The values are selected according to the network’s and the UE’s hardware and configuration.  
An edge server that receives sensor data is co-located with the 5G local breakout core. This edge server is also responsible for the processing of the autonomous mission, which occurs during the second phase of the experiments and is described in Sections 3.3 and 3.5.
## 4 Training Details
### Hardware and Software  
We trained the deep neural network models on a single machine comprising an Intel Core i9 Processor with 16 cores clocked at 5.2 GHz, an NVIDIA RTX 3080 Ti GPU, and 32 GB of RAM. We used PyTorch 1.11 as our deep learning framework and Python 3.6 and CUDA 10.2 on an Ubuntu 20.04 LTS operating system. To preprocess our data, we applied normalization techniques to ensure optimal performance during training. During pretext training, we used the Adam optimizer with a learning rate of 0.004 for 50 epochs and a batch size of 256. Based on the existing literature [35; 36] on contrastive learning, it has been stated that there is a positive correlation between increasing batch size and the quality of embeddings generated by the model. Specifically, larger batch sizes result in more discriminative and robust embeddings, faster convergence, and more efficient training for contrastive learning models. However, due to limited computing power availability, we used a batch size of 256 throughout our experiments.
## 3 System Model
### Computational Model
#### 3.3.1 Local Computing  
If the computational requirements of the computing tasks are within the processing power of the UE, the device performs computing tasks locally. We assume that the CPU in \(UE_{i}\) operates at a frequency of \(f_{i}\), with \(c_{i}\) representing the local computational power in terms of CPU cycles. Therefore, the local computation time can be calculated as follows:  
\[t_{i}^{local}=\frac{c_{i}}{f_{i}}. \tag{5}\]  
Parameter \(k\) is based on the findings presented in reference [30]. Local energy consumption can be mathematically represented as  
\[E_{i}^{local}=kf_{i}(c_{i})^{2}. \tag{6}\]  
Incorporating the influence of chip architecture [31], the effective switching capacitance \(\kappa\) is introduced in the equation. The total energy consumption is provided by the following expression (\(\alpha,\beta\) are loss factors):  
\[C_{i}^{local}=\alpha t_{i}^{local}+\beta E_{i}^{local}. \tag{7}\]
## 2 IEEE 802.15.4-2015 Overview  
The 2015 version of the IEEE 802.15.4 standard offers 18 different physical layers, each targeting specific applications and market segments. In this section, we present and briefly describe the two physical layers analyzed in this paper, i.e., OQPSK-DSSS and SUN-OFDM.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
###### Abstract  
An automatic medical diagnosis service based on deep learning has been introduced in e-healthcare, bringing great convenience to human life. However, due to privacy regulations, insufficient data sharing among medical centers has led to many severe challenges for automated medical diagnostic services, including diagnostic accuracy. To solve such problems, swarm learning (SL), a blockchain-based federated learning (BCFL), has been proposed. Although SL avoids single-point-of-failure attacks and offers an incentive mechanism, it still faces privacy breaches and poisoning attacks. In this paper, we propose a new privacy-preserving Byzantine-resilient swarm learning (PBSL) that is resistant to poisoning attacks while protecting data privacy. Specifically, we adopt threshold fully homomorphic encryption (TFHE) to protect data privacy and provide secure aggregation. And the cosine similarity is used to judge the malicious gradient uploaded by malicious medical centers. Through security analysis, PBSL is able to defend against a variety of known security attacks. Finally, PBSL is implemented by uniting deep learning with blockchain-based smart contract platforms. Experiments based on different datasets show that the PBSL algorithm is practical and efficient.  
blockchain-based federated learning; privacy-preserving Byzantine-resilient training; automatic medical diagnosis; healthcare +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON]; [PERSON] Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare. _Appl. Sci._**2024,** _14_, 5247. [[https://doi.org/10.3390/app14125247](https://doi.org/10.3390/app14125247)]([https://doi.org/10.3390/app14125247](https://doi.org/10.3390/app14125247))
## 4 Experimental Design
### Ablation Experiments  
In this subsection, we analyze the contribution of each component of HSDW-MI for the classification results. First, only the dynamic weighting component is retained in the algorithm, which forms HSDW-MI-sc-sp-s. Then, the sample safety factor sampling component is added based on the previous step, which forms HSDW-MI-sc-sp. Second, the storage pool is added based on the previous step, which forms HSDW-MI-sc. Finally, the spectral clustering is added based on the previous step, which forms HSDW-MI. In order to highlight the effect of the ablation experiment, we use the variable, extremely imbalanced ImbVar_Extreme_SIG data stream with mixed drifts as the experimental data stream, as shown in Figures 3 and 4.  
Figure 3 displays the results of each component on macro-Recall, G-mean, macro-Precision, and macro-F1-score metrics. As illustrated in Figure 3, the complete version of the  
Figure 4: Macro-Recall of ablation experiment on the ImbVar_Extreme_SIG stream.  
Figure 3: Results of ablation experiment on the ImbVar_Extreme_SIG stream.  
algorithm, HSDW-MI, exhibits the best overall performance on these four metrics, followed by HSDW-MI-sc. In terms of macro-Precision, HSDW-MI-sc-sp-s achieves 94.29, ranking 1 st, while HSDW-MI-sc-sp scores 83.49, ranking 4 th. This reveals that when sampling is not added, the algorithm focuses more on the majority class samples and exhibits greater accuracy for these samples. However, when sampling is added, the learnability of the majority class samples is impaired. Nevertheless, with the addition of the storage pool and spectral clustering, the algorithm's macro-Precision value is enhanced, demonstrating an improved ability to classify majority-class samples.  
Figure 4 presents the macro-Recall variation curves of each component of the HSDW-MI algorithm on the ImbVar_Extreme_SIG data stream. As shown in Figure 4, the worst performance comes from HSDW-MI-sc-sp-s, which only includes dynamic weighting, as it can only handle concept drift in the data stream and lacks mechanisms to address imbalance and variable data streams. The performance of HSDW-MI-sc-sp improves with the addition of sample safety factor sampling, which better handles imbalance problems. Adding the storage pool further enhances HSDW-MI-sc performance, particularly in phases without concept drift. This is because the selected experimental data streams are extremely imbalanced, and extracting stored instances from the storage pool during the steady stage enables the algorithm to classify minority classes more accurately and effectively cope with variations in class ratio. Finally, the addition of spectral clustering marginally improves the overall performance of the algorithm. However, this addition allows the algorithm's performance to recover more quickly after concept drift compared to the previous component, as spectral clustering clusters each class, and oversampling or undersampling based on clusters maintains the original class distribution.
## 4 Statistical Analyses of Numerical Simulation Results
### Short-Term Response Analyses  
The short-term statistical values of motion and load responses in the typical numerical simulation Conditions 3\(\sim\)9 (\(H_{1/3}=12\) m, \(T_{z}=13.5\) s, \(U=24\) knots, \(\beta=0\), 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 150\({}^{\circ}\) and 180\({}^{\circ}\)) are analyzed in this section. It is noted that the wave characteristic period is determined according to the principle that the ratio of the average wave length to the ship length is close to 1. The simulations were made by using both linear and nonlinear hydroelastic theory and same incident wave time series were provided for the two schemes for the sake of consistency. Figure 18 shows the comparison of mean value of crest and trough peaks of heave, pitch and total VBM amidships that counted based on the numerical time series. As can be seen from the results, the largest heave and pitch motions occurred at bow oblique waves 30\({}^{\circ}\) or 60\({}^{\circ}\), which can be explained by the relationship between encountered frequency and resonant frequency. The heave and pitch peaks that obtained by nonlinear hydroelastic theory are generally smaller than those by linear hydroelastic theory since the local slamming force (or moment) are always opposite the ship heave (or pitch) direction it could buffer the motion amplitude. The VBM amidships for a ship sailing in head or bow oblique waves that predicted by nonlinear hydroelastic theory are much larger than those by linear theory due to the occurrence of whipping loads. The smallest VBM values occurred when a ship sails in beam waves.  
In addition, the time series of VBM amidships in the wave condition (\(H_{1/3}=12\) m, \(T_{z}=13.5\) s, \(U=24\) knots) while sailing at different wave headings are summarized in Figure 19. The total VBM were separated into LF wave loads and HF whipping loads with a cut-off frequency of 0.3 Hz. As is seen, frequent and severe slamming events occurred when the ship sailing in head waves. The magnitude of whipping loads is even larger than the wave loads, which results in the occurrence of extreme large total sagging loads. Significant whipping loads are also observed for a ship sailing in bow oblique waves 30\({}^{\circ}\) and 60\({}^{\circ}\) even though they are not as severe as those for head waves. The whipping loads are very weak when ship sailing in beam waves and the whipping loads are almost disappeared when ship sailing in stern oblique waves 120\({}^{\circ}\) mainly due to the relatively low encountered frequency. However, moderate whipping loads are found again for a ship sailing in stern oblique waves 150\({}^{\circ}\) and following waves.  
Figure 18: Comparison of short-term extreme values between linear and nonlinear schemes. (**a**) Heave; (**b**) pitch; (**c**) VBM.  
Figure 19: VBM load components in different wave headings.
## 3 Calibration of ATMD with Viscous Damping-Stiffness Control Force  
The considered ATMD is illustrated in Figure 1a. It comprises a mass \(m_{2}\) attached to the primary structure through a passive spring \(k_{2 dh}\) of the classical [PERSON]'s TMD, coupled with an ideal active actuator that applies the active control force \(f_{a}\). Using the same spring \(k_{2 dh}\) as in the passive TMD allows for the replacement of the active actuator in the ATMD with a viscous damper to form a TMD, without the need to tune its frequency. Conversely, the viscous damper in an existing TMD can be replaced with an active actuator to upgrade it into an ATMD of the same mass but with enhanced efficiency.  
The active control force \(f_{a}\) under consideration takes the form of a sum of two components: the viscous damping force and the stiffness force, as depicted in Figure 1 by symbols indicating a controllable spring and a controllable viscous damper.
## 5 Operational Descriptions of the Future Distribution Networks with the Sundom Smart Grid
### Energy Management Use Cases
#### 5.1.1 Energy Management Use Cases of the Self-Sufficient Phase
The demand aggregator is a retailer-aggregator, which is an existing market participant, the energy supplier [82; 83; 84]. The retailer-aggregator collects the demand flexibility from the passive type of loads and offers the aggregated loads for the day-ahead market Elspot (0.1 MW/12 h) [85] or intraday market Elbas (0.1 MW/1 h) [86]. An option is to participate in the TSO's organized power system reserves in balancing energy markets with either up-or down-regulating bids [86], in FCR-N [85] (0.1 MW, \(\leq\)3 min), but likely also in FCR-D (1 MW, \(\leq\)30 s).
## 4 The Efficiency of Hybrid Power Stage under Light-Load Conditions
### 125 kW Si IGBT Converter vs. 10 kW SiC Power Stage under Light-Load Conditions
#### 4.1.2 Switching Losses of IGBT- and SiC-Based Light-Load Setup
Switching losses of IGBTs and MOSFETs and dedicated relevant parameters from linearized loss energy curves, which were extracted from the respective datasheets, are listed in Table 5.  
As can be observed, the switching losses of the IGBT module under no-load operation and a switching frequency of 10 kHz results in 72.6 W per IGBT, whereas the equipped SiC-MOSFET only comes with 1.84 W.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
###### Abstract  
This paper presents a Fault-Tolerant Model Predictive Control (FTMPC) algorithm applied to a simulation model for sewer networks. The aim of this work is to preserve the operation of the predictive controller as much as possible, in accordance with its operational objectives, when there may be anomalies affecting the elements of the control system, mainly sensors and actuators. For this purpose, a fault detection and diagnosis system (FDD) based on a moving window principal component analysis technique (MWPCA) will be developed to provide an online fault monitoring solution for large-scale complex processes (e.g., sewer systems) with dynamically changing characteristics, and a reconfiguration algorithm for the MPC controller taking advantage of its own features such as constraint handling. Comparing the results obtained considering various types of faults, with situations of normal controlled operation and with the behavior of the sewer network when no control is applied, will allow some conclusions to be drawn at the end.  
fault detection and diagnosis; principal component analysis; fault-tolerant control; model predictive control; sewer systems +
Footnote †: journal: Applied Sciences
## 6 Conclusions  
The presented study demonstrates that three sets with a total ICA duration of 27 s, with 3 min rest intervals between sets, have a significant effect on CMJ height and relative peak power output in highly trained male volleyball players compared to CTRL. However, contrary to the hypothesis, a single set with a total ICA duration of 9 s did not have a significant impact on CMJ performance. Furthermore, no significant differences were observed between different set volumes (one set vs. three sets) with varying total ICA durations--9 s (one set with three contractions of 3 s) vs. 27 s (three sets with three contractions of 3 s)--on CMJ height, suggesting that three sets with a total ICA duration of 27 s may have practical implications for improving both CMJ height and peak power output in highly trained male volleyball players. Based on these findings, the authors suggest the inclusion of three sets of ICA with 3 min rest intervals in warm-up protocols, especially before competitive events or intense training sessions, in order to optimize performance outcomes.  
Conceptualization, J.J. and M.K.; methodology, J.J. and D.G.; software, I.S.; validation, D.G. and P.E.; formal analysis, I.S. and P.E.; investigation, J.J. and D.G.; writing--original draft preparation, J.J.; writing--review and editing, J.J., M.W., D.L., P.E. and M.K.; supervision, M.W. and M.K.; project administration, M.W. and M.K.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The experiment received formal approval from the Bioethics Committee for Scientific Research (03/2021) at the Academy of Physical Education in Katowice, in accordance with the ethical principles outlined in the Declaration of Helsinki of 1983.  
Written informed consent was obtained from all participants.  
Data Availability Statement: The datasets analyzed during the current study are available from the corresponding author upon reasonable request.