esa-sceva/satcom-chunk-collection huggingface dataset:
# An Open Source Framework Approach to Support Condition Monitoring and Maintenance
## 5 The Solution
The discussion in this section also aimed at showing that this architecture was able to provide the four benefits of our general approach: (i) the remotization of the different activities required by a CBM platform by means of inter-cloud communication, (ii) seamless security by isolating the devices into Arrowhead local clouds to protect potentially vulnerable devices from the Internet, (iii) support for dynamic use cases by means of the Service Registry/Orchestration services of the Arrowhead Framework, and (iv) democratization of CBM by means of the lower entrance barrier of open source technologies, the usage of a mainstream programming language (Python), and the high level of interoperability and easier system integration granted by SOA. The prototypes presented next are part of the ecosystem illustrated, capable of gathering data, which are later stored and continuously accessed to be processed and conduct analytics. Lastly, the processed data are presented to the clients through human-machine interfaces providing information to support maintenance decisions.
# Homomorphic Encryption Based Privacy-Preservation for IoMT
## 2 Related Works
### Key Considerations  
In order to maintain a secure and privacy-preserving edge-cloud ecosystem for IoMT, the following key considerations are essential to our proposed scheme,  
* Data Confidentiality: Communication of data from IoMT devices to the edge layer transfers the ownership and control of the data. The physical security offered by the devices is lost when transmitted to external network layers, such as the base station at the edge layer. Data confidentiality schemes are required to ensure that any unauthorized entity viewing the data does not observe it in plaintext format.  
Information is required to be encrypted, preventing any form of identification of a user or device. Devices or applications supporting healthcare systems are needed to encrypt the data to a ciphertext, allowing data sharing on untrusted edge and cloud servers.
* Data Integrity: Data transferred from IoMT devices to insecure edge servers are vulnerable to foreign intrusion attempts, resulting in data manipulation and affecting the reliability of the final patient data computation results. It is essential to maintain data integrity during transmission and computation.
* Privacy-Preservation: Privacy is a significant challenge when transmitting sensitive and private user data using untrustworthy external networks such as cloud servers and edge nodes. Third-party edge and cloud service providers can learn much sensitive information about personal user data and sell it for profitable gains. Data sold to other entities allow fraudulent, targeted marketing campaigns and scams, resulting in further stress and financial loss to users. Encryption of patient identity and medical diagnosis information is essential using pseudo-random permutation and public-key encryption techniques.
* Secure Data Computation: Data shared across edge entities are often owned by external service providers that require information to be decrypted before performing mathematical operations. Encryption techniques such as homomorphic encryption allow edge service providers to perform computation operations without requiring the user to decrypt the data.
## 4 Outlook
### Tandem Phase-Shifted Geometry for Infrared Streaking and Attosecond Resolution
From the expressions for the arbitrary streaking field at the \(\pi/2\) phase-shifted streaking measurements, it is clear that the instantaneous vector potential in the second measurement is proportional to the derivative of the instantaneous vector potential in the first and vice versa, where the proportionality constant is \(\omega_{\rm L}\) the carrier frequency of the IR streaking field. Using this crucial information, each streaked spectrum provides an independent measurement of the X-ray pulse profile. Furthermore, if the phase relationship between the streaking fields in the two measurements is \(\pi/2\), the value of the cycle-averaged amplitude at the point of overlap is simply \(a(t_{0})=\sqrt{A_{1}(t_{0})^{2}+A_{2}(t_{0})^{2}}\). Then, if the laser pulse envelope is well characterized, this can be inverted to provide the precise time of arrival of the X-ray pulse and the temporal characterization is complete. Beyond pulse characterization, the streaking technique may also be used to study few-femtosecond electron dynamics at XFELs. For example, the interaction of matter with high-fluence X-ray pulses of durations of few femtoseconds triggers complex dynamics in the electronic system that are not yet fully understood. Photoelectron streaking spectroscopy provides the possibility to study these dynamics on a femtosecond time scale. As a first approach, Auger decay lifetime of simple gas-phase molecules could be measured directly in the temporal domain. Synchrotron sources have been used to measure the lifetime of core holes from the spectral width of the electrons emitted during the decay of the excited state. However, these measurements fail to provide details of the temporal evolution of the excited state, when multi-electron dynamics are involved. In contrast, at XFELs with photoelectron streaking spectroscopy, the temporal evolution of highly excited states and corresponding relaxation dynamics can also be investigated. **Acknowledgments:** [PERSON] acknowledges financial support from a Marie Curie International Outgoing Fellowship. [PERSON] and R.K. acknowledge financial support by the BaCaTe program. [PERSON], [PERSON] and M.M. acknowledge support by the Deutsche Forschungsgemeinschaft (DFG) under grant nos. SFB925/A1 and SFB 925/A3. [PERSON] acknowledges funding from the FW foundation within a Peter Paul Ewald-Fellowship. [PERSON] acknowledges funding through BMBF 05K16 GQ2. J.T.C. acknowledges support from Science Foundation Ireland under grant nos. 12/IA/1742 and 16/RI/369. Part of this research was carried out at the Linac Coherent Light Source (LCLS) at the SLAC National Accelerator Laboratory. LCLS is an Office of Science User Facility operated for the U.S. Department of Energy (DOE) Office of Science by Stanford University. **Author Contributions:** All authors contributed equally to the publication. **Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Bioactivity of SE-NPs
#### 3.2.2 Liver Function Tests  
After evaluating oxidative stress, the hepatic function parameters (plasma aspartate aminotransferase and lactate dehydrogenase, gamma-glutamyltransferase and alkaline phosphatase) in different rat groups were estimated.  
1. AST  
The AST is one of the strong biomarkers determining the liver function and structure. Here, we found a remarkably very significant (\(p\) value \(<\) 0.01) increase in the AST concentration of CCl\({}_{4}\) rats in comparison to control rats. Plasma levels of AST in CCl\({}_{4}\) treated rats were 804.40% higher than controls, and Se-NP treatment reduced this increase to 421.43% as compared to controls. Pre-administration of Se-NP treatment before CCl\({}_{4}\) challenge decreased AST activity by 27.29% compared to CCl\({}_{4}\) treatment alone (Figure 4).  
1. Lactate DH  
Lactate dehydrogenase (LDH) is expressed extensively in body tissues, in particular liver cells. It is released during hepatic damage, making it a marker of common tissue injuries and diseases involving hepatic toxicity. LDH is a bioindicator for necrotic progression in the living tissue. As expected, LDH was dramatically increased in the CCl\({}_{4}\) rats due to the toxicity compared to the control rats. LDH activity was enhanced by 68.16% in CCl\({}_{4}\) group animals compared to controls, and CCl\({}_{4}\) + Se-NP treated reduced this increase by 19.52% compared to CCl\({}_{4}\) group animals (Figure 4).  
1. Gamma-glutamyltransferase  
In the cell membranes of many tissues, gamma-glutamyl transferase (GGT) transfers amino acids and the glutamyl moiety of the glutathione across the cell membrane to various acceptor molecules including water, and peptides, leaving the cysteine product to preserve oxidative stability inside the cell. The concentration of GGT was shown to be significantly (\(p\)-value \(<\) 0.05) increased due to CCl\({}_{4}\) toxicity in comparison to the control rats. On the contrary, the GGT level was restored approximately to normal levels in the CCl\({}_{4}\) rats treated with Se-NPs in comparison to the control (Figure 4).  
1. Alkaline phosphatase  
Alkaline phosphatase (ALP) is of the critical indicators on liver health. Its concentration was found to behave like that of the GGT level in the CCl\({}_{4}\) group. Because of the CCl\({}_{4}\) toxicity, ALP level was significantly (\(p\)-value \(<\) 0.05) elevated in the CCl\({}_{4}\) rats compared to the control level. Se-NPs were found to partially but not significantly restore the ALP level in CCl\({}_{4}\) rats (Figure 4).  
Figure 3: Hepatic oxidative stress parameters (malondialdehydedehyde (**A**) and reduced glutathione (**B**)) in rat liver in different treatment groups. * (\(p\)-value \(<\) 0.05) indicates significant differences in other rat groups in comparison to control (CN) group animals. # (\(p\)-value \(<\) 0.05) indicates significant differences in different rat groups compared to the CCl\({}_{4}\) group animals. Also ** and ## indicate \(p\)\(\leq\) 0.005 while *** and ## indicate \(p\)\(\leq\) 0.001.
## 4 Implementation
### Predictive Control of Asymmetry of Roll Gap of the Heavy-Plate Mill Stand
To calculate the value \(\Delta S^{*}\) it is necessary to define the values of pressure \(F_{\mathrm{OS}}\) and \(F_{\mathrm{DS}}\) in advance. The expression \(\Delta S\)= \(\Delta F/C_{\mathrm{K}}\) is adopted for calculating control actions in the proposed method. Each of the control signals, sent to the controllers of the hydraulic SD position, is equal to a half of the calculated value \(\Delta S^{*}\). Furthermore, before the feed is reversed, one should change the position of the upper operating roll and set a preliminary misalignment of roll gap in such a way that on the side with more intensive rolling force the roll position takes the value \((S-\frac{\Delta S}{2})\), while on the opposite side \(-(S+\frac{\Delta S}{2})\). This will provide compensation of roll misalignment in the previous passage. The method effectively compensates for the mismatch of gauge on the feed sides to prevent error buildup and thus improve the precision of control. In addition, the risk of the RAC controller getting into the area of output signal limitation also reduces as shown in Figure 6. This occurs due to the decrease of pressure mismatch before reverse passage start. Patent [9] presents and rationalizes a similar predictive control method. However, that principle relies on controlling the rolls of the edge. Not every rolling mill has an edger. Moreover, synchronizing the control of electric and hydraulic drives of two stands can be challenging. This is why this solution could be impractical. This paper presents an  
Figure 7: Wedge-shaped feed exiting the rolls (**a**) and ‘reverse’ misalignment of the gap upon entering the rolls (**b**). approach that relies on controlling the hydraulic screwdowns of the horizontal stand. This is a simpler and more effective approach. Besides, it offers more options for implementation on plate and broad-strip mills.
## 6 Conclusions  
In the presence of ICM-MP fluctuations, the existing sparse model of MP clutter is disturbed, and the performance of the existing MP clutter suppression methods drops. In this paper, a MP clutter cancellation approach is presented to eliminate the influence of ICM-MP fluctuation. The proposed algorithm modifies the sparse model of MP clutter, derives a joint optimization problem, and alternately estimates the ICM-MP fluctuations and MP clutter profile with off-grid effects mitigation. The experimental results indicate the proposed joint optimization algorithm outperforms the existing STAP, LSA, and RSA in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MDR Value (dB)** & \(-\)**15** & \(-\)**19** & \(-\)**23** & \(-\)**27** & \(-\)**31** \\ \hline SRA (dB) & \(-\)5.321 & \(-\)5.032 & \(-\)4.957 & \(-\)4.915 & \(-\)4.876 \\ LSA (dB) & \(-\)5.228 & \(-\)4.867 & \(-\)4.565 & \(-\)4.348 & \(-\)4.084 \\ Proposed algorithm (dB) & \(-\)4.656 & \(-\)4.444 & \(-\)4.268 & \(-\)4.032 & \(-\)4.081 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The SINR loss value at the target Doppler frequency with different MDR values.  
Figure 10: Computational complexities of different algorithms. (a) number of multiplications; (b) number of additions.  
MP clutter cancellation when ICM-MP fluctuation exists. Moreover, the proposed approach can deal with the off-grid issue and is robust to the ICM-MP fluctuation, regardless of the MDR values.  
By ignoring the influence of range ambiguities, the proposed algorithm can suppress MP clutter with ICM-MP calibration. However, the assumption may be violated in practice, and the performance of the proposed algorithm may degrade. Consequently, extending our model by considering range ambiguities is the research direction of our future work. In addition, possible future studies include robust MP clutter suppression in nonhomogeneous and nonstationary clutter environments for airborne passive radar.  
Formal analysis, Y.D. and Z.P.; methodology, Y.D. and Z.P.; validation, Y.D. and W.L.; writing--original draft, Y.D.; writing--review and editing, W.L. and D.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hunan Provincial Natural Science Foundation of China, grant No. 2021 JJ40029; Scientific Research Fund of the Hunan Provincial Education Department, grant No. 21B0710; and the 2021 Undergraduate Innovation and Entrepreneurship Training Program of Hunan Province, grant No. 3333.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 4 Model Validation and Results Analysis
### Prediction Results Analysis
#### 4.4.2 Prediction Results of WOA-Attention-BILSTM Model
Therefore, the prediction accuracy and performance of WOA-Attention-BILSTM are excellent. Compare the Haversine Distance of the first 120 steps of each model, as shown in Figure 13. From the figure, it can be seen that the Haversine Distance errors of the LSTM-based model fluctuate relatively regularly in a certain range, while the BP model has a large fluctuation of errors, and the predicted trajectories are relatively incoherent. The error of WOA-Attention-BILSTM is more stable, ranging from \(0.003\) km to \(0.4\) km. As a result of the above analysis, it can be concluded that the proposed WOA-Attention-BILSTM model has the lowest error and is stable, has a significant improvement in prediction accuracy compared with other models, and takes less time to perform the prediction task. Therefore, the WOA-Attention-BILSTM model has good accuracy and performance in the ship trajectory prediction problem and can satisfy the needs of ship collision avoidance, route planning, and maritime supervision. Figure 13: Comparison of model prediction results by Haversine Distance. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **MAE** & **RMSE** & **MAPE (\%)** & **Haversine Distance (km)** & **Execution Time (s)** \\ \hline BP & 0.0730 & 0.0410 & 0.0182 & 1.9199 & 0.00106 \\ LSTM & 0.2005 & 0.0495 & 0.0382 & 4.2073 & 5.86765 \\ GRU & 0.0531 & 0.0103 & 0.0066 & 0.7245 & 2.25732 \\ BILSTM & 0.0431 & 0.0188 & 0.0107 & 1.2218 & 2.46863 \\ Attention-BILSTM & 0.0169 & 0.0074 & 0.0052 & 0.5666 & 2.24087 \\ CNN-LSTM & 0.0484 & 0.0130 & 0.0095 & 0.9555 & 4.68542 \\ WOA-Attention-BILSTM & 0.0092 & 0.0051 & 0.0036 & 0.4042 & 2.34252 \\ \hline \end{tabular}
\end{table} 
Table 5: Metrics of WOA-Attention-BILSTM and other models.
# Distributed Fault Detection and Isolation Approach for Oil Pipelines
## 6 Conclusions  
In this paper, a distributed FDI approach based on structural analysis for OPS is proposed; likewise, the design of a distributed FDI system of an OPS located in Peru is developed. This FDI system is composed of four local diagnosers with a total of eight analytical redundancy relations obtained from a set of FMSO sets. Through the proposed algorithms, local diagnosers are efficiently built for each subsystem by progressively incorporating information from neighboring subsystems to achieve the previously defined fault detection and isolation objectives. The FDI system developed was tested by simulation validating that the eight faults of the set of interest, which include five sensor faults and three pipeline segment faults, can be detected and isolated. The main advantage of this approach is that it allows a distributed diagnosis design without the need to use the global model of the process, which makes it much more feasible to implement for complex and large processes.  
Figure 8: Analytical redundancy relations \(r_{i}\).  
**Author Contributions:** All the authors contributed to the development of the experiments, the result analysis, and the writing and review of the paper. Specifically, [PERSON] and [PERSON] were in charge of preparing the introduction, [PERSON] of the modeling and design of the FDI system, [PERSON] and [PERSON] of the simulation of the FDI system, [PERSON] of the model of the process and [PERSON], [PERSON] and [PERSON] of the overall ideas of the exposed research and the general conception of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by _Proyecto de Mejoramiento y Amplicacion de los Servicios del Sistema Nacional de Ciencia Tecnologia e Innovacion Tecnologia 8682-PE, Banco Mundial_, CONCYTEC and PROCIENCIA through grant E041-01[N48-2018-FONDECYT-BM-IADT-MU].  
Not applicable.  
**Data Availability Statement:** The data presented in this study is available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Evolution of Wireless Communication Technology (1G-6G)
### Fourth Generation (4G) Technology  
To transmit data, voice, multimedia, and internet services at a higher rate, quality, and security at a low cost, the Fourth Generation (4G) was initiated in the late 2000s as an all-IP-based network system. Fourth Generation technology was first used commercially in Norway in 2009 after its successful field trial in Japan in 2005. It aimed to provide peak data rates of up to 1 Gbps at low mobility and 100 Mbps at high mobility and is based on Long-Term Evolution (LTE) and Wireless Interoperability for Microwave Access (WiMAX) technologies. The LTE standard was further enhanced to LE-Advanced Pro (referred to as 4.5G) to increase the mobile broadband and connectivity performances [22]. However, 4G was not capable of operating applications that require image processing, such as machine vision, smart connected cars, and augmented reality, giving rise to the 5G network generation.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 2 Materials and Methods
### Methodology
#### 2.2.2 Holt-Winters Method  
During the validation stage, ARIMA/SARIMA models can be compared with alternative methodologies capable of making predictions from seasonal data [38]. One such alternative is the Holt-Winters method [39]. This approach typically assumes that the series  
Figure 2: ARIMA Modeling Process Flowchart.  
can be decomposed into some or all of the following components: (a) trend, (b) cyclical, (c) seasonality, and (d) irregular [40]. Furthermore, the model structure can be either additive or multiplicative, depending on the nature of the seasonality [41].  
The application of this method is based on a theoretical model used for forecasting, expressed as the following:  
\[Y_{t}=(b_{0}+b_{1})\ E_{t}+\ \mu_{t}\]  
where \(b_{o}\) is the level component, \(b_{1}\) is the slope of the trend line, and \(E_{t}\) represents the multiplicative seasonal factor.  
The process begins with the initialization of these components based on historical data. Then, at each period, the level, trend, and seasonality are iteratively updated using smoothing formulas that incorporate the parameters \(\alpha\), \(\beta\), and \(\gamma\). Finally, the model combines these components to generate forecasts that reflect both the overall evolution and the seasonal patterns of the time series. The following flowchart (Figure 3) details these steps, showing how the sequential updating of each component is performed to optimize forecast accuracy.
## 3 Data Sources and Treatments
### Wind Farm Actual Power Generation Data  
Wind generation systems have a Supervisory Control and Data Acquisition (SCADA) center that archives the generated power and the status of the wind turbines in a fixed time interval (usually 1 min, 10 min, 15 min, or 1 h resolutions). The SCADA is computer software that can be run on a single computer if the wind farm is a smaller size and contains a smaller number of wind turbines located in the same or nearby locations. However, it can be run in a distributed framework if the wind farm has a large capacity and contains a huge number of turbines dispersed in various locations. The SCADA system enables wind farm operators to supervise and regulate remote operations and turbines online. In this paper, the SCADA system of an operational microgrid wind generation system in Beijing, China is examined. The generation system is rated at 2500 kW. In this work, the wind generation data are a vector consisting of the wind generation historical archives for three years (2014-2016) with 10-min resolution. The two-year (2014-2015) data are employed for the forecast model training and validation, while the remaining one-year (2016) data are utilized for the mode testing or forecast. The wind generation data are achieved based on the local time zone, which is China standard time (CST) [CST = Coordinated Universal Time (UTC) + 08:00].  
Figure 1: Dual-step wind power prediction approach based on hybrid wavelet transform (WT)-ant colony optimization algorithm (ACO)-feedfoward artificial neural network (FFANN).