esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Enhanced bandwidth issues for 5G system are fruitfully resolved by organizing free space optics (FSO) communication frameworks. The high bandwidth, the maximum number of channel transmission requirements, and high data rate have been boosted during the last years because of the COVID-19 pandemic. The online services and digital applications have increased pressure on installed optical network models. In addition, the optical networks with high capacity transmission produce nonlinear distortions, which degrade system efficiency. This paper presents a mixed FSO and fiber network to tackle the factors of nonlinearities and enrich the system capacity and range. Furthermore, the issues related to radio frequency, FSO pointing, and co-channel interference are considered in this work. The theoretical and simulation structures are validated using advanced measuring parameters, such as bit error rate (BER), peak to average power ratio (PAPR), cumulative distribution function (CDF), and outage probability. The nonlinear factors are addressed successfully, and the capacity is developed from current models. Finally, the proposed model's limitations and future direction are discussed in this paper.  
nonlinear interference; co-channel interference; free space optics; free space pointing errors 2022
## 6 Conclusions  
The initial version the Virtual Astronaut created for Santa Maria Crater [40] demonstrates that the concept can provide an effective tool for organizing and presenting data from multiple instruments that have differing resolutions in an interactive 3D virtual environment. The VA integrates data from both orbital and landed missions. The VA allows the user to explore and view the surface at different scales. The user can further interact with the environment by viewing and accessing _in-situ_ observations, making measurements of features, and controlling an animation of rover drives. Within the VA tool, the user can freely move around the scene and look in any direction desired. Thus, the user is not limited to seeing the scene only through the viewpoint of the rover. The high-resolution image mosaics built from rover-based combined with orbital mosaics for regional coverage makes the VA unique when compared with other Mars virtual reality systems. The development tools chosen to implement the VA provided an efficient environment for iteratively creating functionality and revising these functions based on feedback from users. Future work includes generating additional versions of the VA as Opportunity explores the rim of Endeavour Crater and Curiosity explores Gale Crater.  
Figure 11: A user makes a measurement of distance between the two red points on the 3D scene. Results are shown in a pop-up window attached to the last position of the mouse. The distance between the points in this example is about half a meter, and the height difference is about 20 cm. Both points are about 2.5 m away from the viewer.
# Advancing Fault Detection in Distribution Networks with a Real-Time Approach Using Robust RVFLN
## 2 Fault Detection Method
### R-Rvfln  
The regularized RVFLN (R-RVFLN) effectively avoids the overfitting problem of the model and reduces the magnitude of the output weight. This also reduces the model complexity. Using the method in [53; 55], a regular term \((\ell_{2}\,norm)\) is added to the objective function. With this added term, an R-RVFLN algorithm is generated by ridge regression. For a given N distinct samples \((s_{o},z_{o})\), the R-RVFLN with norm \(\ell_{2}\) is as given in Equation (8).  
\[min:V=\frac{1}{2}\left\|\alpha\right\|_{2}^{2}+\frac{B}{2}\sum_{o=1}^{N}\varepsilon _{o}^{2},\quad\quad s.t:\mathbf{f}(\mathbf{s_{o}})\alpha=z_{o}-\varepsilon_{o },\forall o \tag{8}\]  
B is the regularization parameter. The task of this parameter is to balance the training error and the output weight norm; \(\sum_{o=1}^{N}\varepsilon_{o}^{2}\) and \(\left\|\alpha\right\|_{2}^{2}\) denote empirical and structural losses, respectively. According to the regularized LS algorithm, the solution of \(\alpha\) is as given in Equation (9).  
\[\alpha=\begin{cases}(\mathbf{F}^{\dagger}\mathbf{F}+\frac{1}{B})^{-1}\mathbf{ F}^{\dagger}\mathbf{Z}&,\quad N\geq K\\ \mathbf{F}^{\dagger}(\mathbf{F}\mathbf{F}^{\dagger}+\frac{1}{B})^{-1}\mathbf{ Z}&,\quad N<K\end{cases} \tag{9}\]  
Equation (9) is described as the offline learning form of the R-RVFLN. As described in [53; 56], the online learning form describes a recursive solution.  
\[\begin{cases}\mathbf{V}_{m}=\mathbf{V}_{m+1}+\mathbf{F}_{m}^{\dagger}\mathbf{ F}_{m}\\ \alpha_{m}=\alpha_{m-1}+\mathbf{V}_{m}^{-1}\mathbf{F}_{m}^{\dagger}(\mathbf{Z}_{ m}-\mathbf{F}_{m}^{\dagger}\alpha_{m-1})\end{cases} \tag{10}\]  
**F\({}_{m}\)** is called the hidden layer output for the \(m\)-th instance. The initial values of the model are expressed as in Equation (11).  
\[\begin{cases}\mathbf{V}_{0}=\mathbf{F}_{0}^{\dagger}\mathbf{F}_{0}+\frac{1}{ B}\\ \alpha_{0}=\mathbf{V}_{0}^{-1}\mathbf{F}_{0}^{\dagger}\mathbf{Z}_{0}\end{cases} \tag{11}\]  
One advantage is that the online R-RVFLN model can be initialized using a small number of samples. Another advantage is that the output weight norm is relatively small. It also has disadvantages. The greatest disadvantage is that, when there are data with outliers, the quality of the online R-RVFLN may deteriorate. In this case, the regularization parameter B may not run correctly, leading to algorithm misfitting.
## 5 Energy-Harvesting Applications
### PLS in Energy-Harvesting Networks  
PLS is a security strategy that uses the physical qualities of the communication channel to improve data security, rather than depending exclusively on cryptographic approaches [96; 38; 97]. This section delves into the principles of PLS, its use in EH-WSNs, a comparison with existing encryption methods, and the obstacles connected with PLS implementation. PLS uses the inherent properties of the communication medium to ensure secure transmission. In contrast to classical encryption systems, which operate with complex algorithms and key management on the basis of conventional security technologies, PLS is based on the physical properties of the channel. The basic concepts of PLS are the following:Secrecy Capacity: The maximum amount of information that can be safely transmitted over a communication channel.
* The Signal-to-Noise Ratio (SNR): This ratio is a comparison of the level of the desired signal to the level of background noise.
* Channel State Information (CSI): It states that knowledge of the channel conditions is crucial for optimal scheduling and allocation of radio resources.  
PLS can be included into EH-WSNs to further enhance the data security when data are shifted across energy-harvesting networks [9, 98]. Data protection and energy efficiency are the objectives of PLS integration in EH-WSNs. PLS techniques that are applied in EH-WSNs include the following:  
* Energy-Harvesting-Based Security: Employing the energy-harvesting abilities of the network, in order to assist secure communication.
* Secure Data Transmission: Making sure that data are transmitted securely using PLS methods, even with less energy resources.  
PLS provides a number of advantages over conventional encryption methods that include the following:  
* Reduced Computational Overhead: PLS does not need complex cryptographic algorithms, bringing down the computational burden.
* Enhanced Security in Adverse Conditions: PLS can preserve security even in demanding environments with excessive levels of interference.  
Table 1 portrays a difference between PLS and traditional encryption methods.
## 3 Results  
### Experimental Results  
The racetrack-based and ring-based filters were experimentally characterized by taking into consideration chips from different parts of the wafer, using the above experimental set-up, with particular attention on evaluating the filter insertion losses and the transfer function spectrum. In Table 3, we report the results of the spectral characterization of the filter exhibiting, for each configuration, the lowest IL.  
It is evident from these values that the IL of the filters is still higher than that needed to meet the requirements for practical NG-PONs. In particular, for the rings-based filter, the high losses also result in a significant broadening of the resonance peak, leading to a low suppression of adjacent channels at 100 GHz spacing. In order to improve the performance of the second iterations of these filter designs, we analyze the measured filter performance, to identify possible solutions.
# Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
## 3 Materials and Methods
### Data Preprocessing  
In the analysis of industrial multivariate time series, due to significant variations in numerical values across different variables, this study utilizes the Min-Max normalization method to alleviate the adverse effects stemming from large numerical differences among variables in time series data. Normalization is applied to each dimension of the time series data using the following formula:  
\[x_{i}=\frac{x_{t}-min(x_{train,i})}{max(x_{train,i})-min(x_{train,i})+\in^{ \prime}}, \tag{1}\]  
where \(max(x_{train,i})\) and \(min(x_{train,i})\) represent the maximum and minimum values, respectively, on the \(i\)-th dimension in the training set. The symbol \(\in^{\prime}\) denotes a small constant vector used to prevent division by zero. Since time series exhibit temporal correlations, i.e., there is temporal dependence between different time points, this study takes into account the dependency relationship between the current time point and historical time points. A fixed-length input is created using a sliding window of length \(n\), denoted as \(S_{t}=\{x_{t-n+1},\cdots,x_{t-1},x_{t}\}\). For model training, instead of directly using \(X\) as the model input, the multivariate time series \(X\) is segmented into sliding windows \(S\) to serve as the model input. This approach enables the model to assess the anomaly of the time observation \(x_{t}\) by considering not only \(x_{t}\) itself but also the historical temporal dependencies associated with \(x_{t}\).
## 7 Conclusions  
The monitoring data are fundamental for establishing landslide displacement prediction models, and the results of prediction are significant for landslide early warning. In this study, the Shengjbao landslide in the TRGA was taken as an example. Based on the monitoring data, the response relationship between landslide displacement and factors is analyzed, an ensemble model of the coupled LSTM and SVR algorithm with the weight coefficients based on linear combination theory was established. The dynamic model based on the deep learning algorithm of LSTM and the static SVR model were also used to predict landslide displacement for comparison. The main conclusions are as follows: (1) Under the same fluctuation conditions of reservoir water level, a longer duration of rainfall has a more important influence on landslide deformation; (2) Overall, the LSTM models perform better than the SVR models, but the results of LSTM models are not closer to the original value than the results of SVR models at every time step in predicting dataset; (3) The proposed Ensemble model integrated both the advantages of LSTM and SVR algorithms, where the Ensemble model has higher prediction performance than both of LSTM and SVR models. In general, by combing the advantages of different ML algorithms, the ensemble model proposed in this paper can effectively construct the response relationship between landslide displacement and factors.  
Overall, the proposed method, which applies time series analysis, LSTM and SVR, can achieve accurate predictions in case of slow and the step-like deformation periods. This method of establishing an Ensemble model has the potential for application to predict landslide displacement in the TGRA and other landslide-prone regions.  
Writing--original draft preparation, H.J.; writing--review and editing, H.J., Y.L., C.Z., H.H., T.G., and K.Y.; supervision, K.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (No.292 2018 YFC0809400).  
**Acknowledgments:** We greatly appreciate the careful reviews and thoughtful suggestions by reviewers. The authors wish to thank [PERSON] and [PERSON] for their assistance in language improvement, and [PERSON] for her technical support. The first author would like to thank the China Scholarship Council for funding his research at the University of Vienna, Austria.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data Collection  
This section describes the data collection related to the main study and, therefore, only considers the main specimen while the additional specimens are left for future work. For maximum precision, the specimens were inspected with a Zeiss DuraMax CMM with a measurement accuracy of 2.9 \(\upmu\)m + L/200 at 18-30 \({}^{\circ}\)C where L is the measurement length in mm. A fixture was designed and 3D-printed to automate the inspection, ensure specimens were measured under the same conditions, and make the changeover from one specimen to the next as simple as possible. The fixture was printed with PLA using a Prusa MK2.5 fused-filament fabrication (FFF) printer with a layer thickness of 0.1 mm. The fixture makes use of a small bolt to secure the specimen against the opposite corner, and the force is carefully exerted on the specimen to avoid deflection, which could influence the results. The fixture was tested with a prototype of the specimen 3D-printed with PLA filament prior to the fabrication of the actual test specimens. This preliminary test displayed negligible deflection when the bolt was tightened by hand and was, therefore, considered appropriate for the current experiment. This decision is further substantiated by the material properties as PLA is significantly more flexible than PA12 and, therefore, more prone to deflection. Figure 14 shows the CAD-design of the fixture (a) and the fixture with the prototype in place (b). The two ears allow the fixture to be bolted to the measurement table with M10 bolts.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Machine manufacturer & EOS GmbH—Electro Optical Systems \\ Machine model & EOSINT P395 \\ Parameter profile & Balanced \\ Layer height & 120 \(\upmu\)m \\ Hatch distance & 0.3 mm \\ Build chamber temperature & 180 \({}^{\circ}\)C \\ Material & PA2200 (PA12) \\ Bulk density & \textgreater{}0.430 \({}^{\ast}\) \\  & d50 = 58 \({}^{\ast}\) \\ Mean grain size & d10 = 40 \({}^{\ast}\) \\  & d90 = 90 \({}^{\ast}\) \\ Material blend & 50\% recycled \\ \hline \end{tabular} \({}^{\ast}\) from the material datasheet.  
\end{table}
Table 4: Build process specifications.
# Pose-Based Gait Analysis for Diagnosis of Parkinson's Disease
## 1 Introduction
Although markerless motion capture has been studied in comparison with gold-standard motion capture techniques [21], it is yet to be used in the prediction of neurological conditions such as PD. Hence, in this study, we extract PD-discriminative gait features from raw videos of subjects and demonstrate the potential of markerless motion capture for PD prediction. The main contributions of this study are as follows. First, we propose eight features for the diagnosis of [PERSON]'s disease from videos. We explore pose estimation for markerless motion capture and perform feature extraction from the human pose estimation time series. Then, through systematic analysis and several experiments, we propose features that can aid in PD diagnosis from arbitrary views. We note that vision-based approaches can be based only on the detection of perceivable gait impairments, most of which can also be found due to ageing or other conditions. Hence, the proposed technique could serve the purpose of screening. If gait impairment is detected, further clinical investigations may be required to determine if it is due to PD or other reasons.
# A Solution Procedure to Improve 3D Solid Finite Element Analysis with an Enrichment Scheme
## 3 Improving 3D Solid FE Solutions
### Connecting Rod Problem  
The next example is a connecting rod problem as shown in Figure 16. The structure is fixed at the surfaces marked in blue and subjected to \(x\)- and \(y\)-directional forces with \(F_{x}=-100\) N and \(F_{y}=100\) N, respectively, at the surface marked in red. The given values for Young's modulus and [PERSON]'s ratio are \(E=2.0\times 10^{11}\) N/m\({}^{2}\) and \(\
u=0.3\), respectively. For the connecting rod problem, coarse and fine meshes are considered as shown in Figure 17. The coarse mesh is constructed using a total of 1558 elements (428 HEX8, 86 PRI6, and 1044 TET4) and the fine mesh is constructed using a total of 6463 elements (2540 HEX8, 8 PRI6, 246 PYR5, and 3369 TET4). The reference solution was obtained using 10-node tetrahedral elements with fine mesh.  
Table 6 provides the relative error in the maximum von Mises stress. Table 7 gives detailed information on the adaptive solution procedure including DOFs, order of cover functions, and \(\alpha\). Figure 18 shows the von Mises stress distributions obtained from the standard solution procedure and adaptive solution procedure, respectively, including the automatically determined cover functions at each node. The adaptive cover functions are applied selectively to each node where stress concentrations or jumps occur. Comparing the standard solution obtained from the fine mesh with 13,854 DOFs and the adaptive solution obtained from the coarse mesh with 7179 DOFs, the proposed adaptive enrichment scheme can effectively improve the accuracy of the solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multicolumn{3}{c}{**von Mises Stress**} \\ \cline{2-3}  & **Standard Solution** & **Adaptive Solution** \\ \hline Coarse mesh & 39.46\% & 14.73\% \\ Fine mesh & 28.83\% & 5.60\% \\ \hline Reference solution & \multicolumn{2}{c}{\(\tau_{ref}=8.324\times 10^{-4}\) N/m\({}^{2}\)} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative errors in the maximum von Mises stress (\(e_{r}\)) for the tool jig problem using coarse and fine meshes.  
Figure 16: Description of the connecting rod problem (\(E=2.0\times 10^{11}\) N/m\({}^{2}\), \(\
u=0.3\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh**} & \multicolumn{3}{c}{**DOFs**} & \multicolumn{3}{c}{**Number of Nodes**} \\ \cline{2-7}  & **Standard Solution** & **Adaptive Solution** & **\(\alpha\)** & **None** & **Linear** & **Quadratic Cover** \\ \hline Coarse mesh & 1944 & 5040 & 0.25 & 506 & 86 & 86 \\ Fine mesh & 10,575 & 21,762 & 0.17 & 2999 & 310 & 311 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Supplementary information on the adaptive enrichment scheme for the tool jig problem.  
Figure 17: The numbers of elements and nodes for (**a**) coarse and (**b**) fine meshes in the connecting rod problem.  
Figure 18: von Mises stress (\(\tau_{0M}\)) distributions for the connecting rod problem. Solutions are obtained by using coarse and fine meshes.
###### Abstract
Examples of these techniques include artificial neural networks (ANNs) [12; 13], fuzzy logic (FL) [14; 15; 16; 17], adaptive-network-based fuzzy inference systems (ANFISs) [18; 19], artificial intelligent techniques [20; 21; 22; 23; 24], and expert systems [25; 26]. This research provides a detailed review of AI applications in power systems, particularly in stability, control, and protection, identifying key challenges and research gaps based on recent publications from 2017 to 2022. The main advantages of this review article are as follows. 1. The paper analyses AI and metaheuristic optimization applications in modern power system stability and protection, highlighting their role in enhancing predictive maintenance and fault detection. 2. It identifies critical research gaps and future directions in integrating intelligent techniques with smart grids and traditional power systems, offering a roadmap for advancing this crucial field. 3. This paper also discusses how AI integration improves smart grid operation and energy efficiency. AI algorithms analyze sensor, meters, and grid infrastructure data to find energy conservation and grid optimization opportunities. AI improves grid efficiency, energy savings, and environmental impact by adjusting power distribution, voltage, and routing in real time. The article is organized as follows. Section 2 explains the need for AI in power systems. Section 3 defines four critical categories of AI and lists the types commonly used in power systems. Section 4 provides an overview of the AI techniques commonly applied to different aspects of power system problems. Section 5 compares the modulation approaches and intelligent techniques for power system stability, control, and protection. Section 6 discusses the various control methods employed by intelligent techniques. Section 7 covers recent intelligent strategies developed in the power system over the last two decades. Section 9 discusses challenges and offers recommendations for future work. Finally, Section 10 summarizes and concludes the main points of the article.
## 7 Conclusions and Future Research  
This paper introduces a new type of cyber attack, false image injection attack (**FIIA**), specific to the healthcare domain and more importantly related to sensitive medical images. This type of attack is not being handled well in real time by the existing image tampering detection techniques. To ensure security and privacy of such images, a blockchain based medical image storage solution, _i_**Chain** is proposed. The proposed framework is designed to ensure that the medical images have not been tampered with and can provide support to the correct diagnosis for patients. Although, this framework is expected to be ideal, there is still scope to improve blockchain based systems, particularly the scalability issue which will be addressed in future works. The specific research questions that future research will try to answer are the following:  
* How ethical hacking tools can be utilised to identify vulnerabilities of blockchain based frameworks?
* How to inject software vulnerabilities or malware into blockchains?
* How to address the scalability of the blockchain based security framework used in implanted medical devices such as pacemakers, CRT-D (Cardiac resynchronization therapy devices) etc.?  
**Funding:** This work was supported in part by the Edith Cowan University Early Career Research Scheme for project: Securing Internet of Medical Things against False Data Injection Attacks using Blockchain, Chief Investigator: [PERSON]. The article processing charge is funded by Edith Cowan University Open Access Funding Support Scheme.  
**Acknowledgments:** The author would like to thank the reviewers for the valuable feedback to enhance the quality of the manuscript.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline
**Type** & **User** & **Cost** & **Security** & **Architecture** & **Privacy** \\ \hline Public & Crypto Traders & High & High & Decentralized & Medium \\ \hline Private & Government & Medium & Medium & Partially Decentralized & High \\ \hline Hybrid & Private Sector & Low & Medium & Partially Decentralized & High \\ \hline \end{tabular}
\end{table}
Table 2: Comparison among different blockchains [23].  
Figure 8: Computational time comparison for different hash functions..