esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Dataset Description and Processing
### Data Processing
#### 2.3.4 Outlier Removal  
Being the most important layer in the proposed flowchart of data processing, the outlier removal layer is very effective in improving data quality, especially when aircraft engines are considered, as they are highly dynamic systems susceptible to outliers due to harsh operating conditions. In this case, an outlier detection list that is based on different types of distances and statistical tests (i.e., Grubbs test; Mahalanobis, Euclidean, and Minkowski distances) is used. Overall, these distances and statistical tests play a vital role in outlier detection by quantifying the dissimilarity or anomaly of data points compared with the majority of the dataset. They provide valuable insights into the presence of outliers, helping to identify potential errors, anomalies, or interesting patterns in the data [24]. A mixture of these tools is used to strengthen the outlier detection process and overcome the shortcomings of each by helping to eliminate/reduce outliers as much as possible. It should be mentioned that in such a situation, it is necessary to take into account that each class of data must be processed separately by such algorithm, because processing global data all at once would lead to a massive loss of data samples, as different classes can be seen as tools for others, and so on. A different class is supposed to contain different models, and different models are more similar to each other.
# Web-Based Toolkit for Performance Simulation and Analysis of Power Line Communication Networks
## 5 Overview of the Provided Results
### Identifying problematic nodes within a problematic time interval.  
As was just mentioned, once the DSO knows when a given network is facing problems, PRIME Analytics can be used for identifying where the problems are taking place and who are the nodes involved in such problems.  
Regarding the identification of problematic nodes, if a time interval is selected that covers such a period and the number of switches is sorted by the number of unregistrations of their \"children\" (Figure 16), it can be observed which are the two switches from which more nodes unregister. In this example, from now on, these two switches will be referred to as SW1 (with MAC address ending in FA, with 226 unregistrations) and SW2 (with MAC address ending in 70, with 205 unregistrations).  
Figure 16: Switches sorted by number of unregistrations in a given period of time.  
Figure 15: Rank of 15 min intervals with more unregistrations.  
Nodes can unregister because they lose connectivity or because some nodes in their communication path to the concentrator do so. In the graph of connections to switches for these two nodes, it can be observed that both SW1 and SW2 communicate with the concentrator through the same switch. Figure 17 shows the graph obtained when using the MAC address of SW2 as search criterion (it is the same for SW1). From now on, this SW with MAC address ending in 41 will be referred to as SW3.  
Repeating the search with SW3 MAC address, it can be checked that this switch is directly connected to the concentrator (with MAC address ending in 30), as shown in Figure 18.  
If switch SW3 is searched in the table of nodes sorted by the number of unregistrations (very similar to the one shown in Figure 16, but displaying nodes instead of switches), it can be observed that it does not unregister in the selected period (the search functionality of the web browser can be used to check that SW3 MAC does not appear in such a table),  
Figure 17: Connections to switches for node SW2.  
Figure 18: SW3 is directly connected to the concentrator (with MAC address ending in 30).  
so it can be concluded that the problem is located downstream. If switches SW1 and SW2 are searched in this table, it can be also observed that they do not unregister in such an interval, so the problem is in the nodes connected to them. Analyzing the nodes with the highest number of unregistrations in that period, it can be observed that both are connected to switches SW1 and SW2, so the communication problem is located in the section that connects these nodes with their \"parents\".  
DSOs (Distribution System Operators) can accurately locate this part of the network using the MAC addresses of such nodes and the MAC addresses of switches SW1 and SW2.
Cross-validation involves dividing the full dataset in a number of folds and using different combinations of folds to compose the training, validation, and testing subsets. By repeating the training+validation+testing processes over different fold combinations, the robustness of the training process is increased (as more data can be used for training), and the classification accuracy will be more statistically significant (as this will be calculated as the average performance for all the combinations). As an example, in [23; 24], the experiments were carried out using a leave-one-out CV strategy, on a location basis: The data were recorded in 6 different locations (the CV comprises 6 folds), where the data recorded in all the locations except one were used for training, and the evaluation was done on data of the unused location (thus ensuring full independence between the training and testing subsets).
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 3 Methodology  
Figure S25 (Supplementary Materials) depicts a flowchart diagram of the methodology used in this study. To begin, a total of 9017 earthquakes with magnitudes of 4.8 or higher were extracted from seismic catalogues considering those that occurred all over the world for the study. Thirteen datasets were built by combining different magnitudes of earthquakes and features. Each dataset was divided into two parts: training data and test data. After that, we used the \"sliding window\" technique for data preprocessing, and lastly, we generated time series-based features.  
In our work, we explored the effect of different model input and spatial window sizes, earthquake magnitudes, and whether the earthquake occurred during the day or at night using different datasets. We also performed a comparison of five state-of-the-art approaches. Considering these approaches are highly sensitive to parameter selection, we preferred to choose those configurations that would allow us to achieve the highest performance in the tests. The performance of each approach was then evaluated after the parameter selection. Finally, we evaluated the performance of each technique using ROC curves and other performance metrics.
## 4 Results and Discussion
### Design Phase Results
#### 4.1.1 Geometry Optimization: 2D FE Model Results
The results from the pre-optimized configuration (Table 3) are compared to the results of a random configuration. The vertical shadow is the rotation range that the foot must have (values defined and specified in Section 2.4, “Biomechanical Requirements”, i.e., between \(-5\) degrees and \(-8\) degrees in the plantarflexion and between \(14\) degrees and \(18\) degrees in the dorsiflexion). The horizontal shadow is the range of the ground reaction force (between \(95\%\) and \(130\%\) in plantarflexion and between \(95\%\) and \(108\%\) in dorsiflexion of the body weight of the user). The intersection area between the two shadows is the optimal area in which the curve of stiffness of the foot must fall, so that the foot rotates up to the degrees desired when subjected to ground reaction forces. Figure 22: The stiffness curves presented as _foot rotation–reaction force_ in Figure 21 can be also presented as _platform displacement–reaction force_.
## 1 Introduction
### Related Works
A leader-follower method with an asymmetric limitation in the range and tolerance of the LOS angle is presented to track error between leader and follower for sub-active surface vessels is presented in [36]. It is assumed that the information speed of the leader is not available for tracking purposes. For this purpose, a reconstruction module is designed to accurately inject this information into a controller as a reference for follow-up by the vehicle follower. The control of formation for Nonholonomic mobile robots with heterogeneous uncertainties is studied in [37]. The formation mechanism is based on the leader-follower plan. The integral sliding mode control (ISMC) method is used to avoid uncertainties incompatible when robots are formed. The theoretical analysis, using the Lyapunov method, proves that the sliding surface vector of the coordinated formation control system with anomalous uncertainty is locally asymptotic stable. Conditions that are accessible by SMC are guaranteed using the ISMC methodology. The control method provided for the maneuvers of forming a multi-robot system consisting of three robots in two predicted paths in the presence of asymmetric uncertainty. Simulation results show the effectiveness, feasibility, and robustness of the ISMC method. Other robust methods have also been used for uncertainty removal [38; 39; 40]. Energy replenish method for robots is provided in [41; 42]. The authors proposed a supervised learning method to do the classification that is very interesting and can be applied to our method [43; 44]. The authors in [45; 46; 47; 48; 49; 50; 51; 52; 53] gave a novel mobile sink-based method to optimize system performance, which can be referred to. This paper is organized as follows: Kinematic and dynamic model of autonomous surface vehicles are given in Section 2. Moreover, the leader-follower formation model of ASVs obtains in this section. Adaptive robust formation control that is the main result of this paper is designed for the considered leader-follower model in Section 3. Simulation results are given in Section 4 to validate the proposed methodology. Finally, the paper is concluded in Section 5.
## 4 Experiment and Data Preprocessing of Artificial Lateral-Line System
### Experimental Results and Data Preprocessing  
In the section above, three sets of experiments are conducted to obtain the data of the artificial lateral-line system and the data from sensors of force and torque. Although temperature compensation and other anti-interference measures are taken, the noise from sensors is still unavoidable. The data of the pressure sensor, torque sensor and force sensor are constantly fluctuating and not stable. Moreover, abnormal data points may occasionally occur. Experimental data need to be filtered before data fitting.  
Useful signals in this experiment are concentrated at low frequencies, which can be drawn by theoretical analysis and observation of collected data. Therefore, a low-pass filter is designed for information preprocessing. The Butterworth filter is adopted, and an analog filter is converted to the digital filter by the bilinear transformation method [35].  
As the most important sensors of the artificial lateral-line system, the pressure-sensor data-sampling frequency is \(f_{s}=15\) Hz. The passband cut-off frequency is set to \(f_{c}=1.5\) Hz. The stopband start frequency is set to \(f_{z}=6.75\) Hz. Thus, the digital angular frequency can be calculated as  
\[\left\{\begin{array}{l}\omega_{c}=\frac{2\pi f_{c}}{f_{s}}=0.2\pi\\ \omega_{z}=\frac{2\pi f_{c}}{f_{s}}=0.9\pi\end{array}\right. \tag{7}\]  
Distortion occurs on the digital frequency axis of the frequency response curve of the filter, when the s-plane imaginary axis is converted to the Z-plane unit circle using a nonlinear transformation. In order to solve this problem, the simulated angular frequency is predistorted, and the predistorted simulated angular frequency is as follows:  
\[\left\{\begin{array}{l}\Omega_{c}=\frac{2}{f_{c}}\tan\frac{\omega_{c}}{2}\\ \Omega_{z}=\frac{2}{f_{s}}\tan\frac{\omega_{c}}{2}\end{array}\right. \tag{8}\]  
Figure 7: Three sets of experiments. The red arrow in the figure is the movement direction of the underwater robot and the white line is the axis of the force-and-torque-measuring device. (**a**–**c**) show the experiments in directions of surge, sway and yaw respectively.  
Passband signal attenuation is 3 dB, stopband attenuation is 15 dB. Then, the final digital Butterworth filter is obtained:  
\[\frac{Y(z)}{X(z)}=H(z)=\frac{0.5329z+0.5329}{z+0.0658} \tag{9}\]  
Convert to difference-equation form:  
\[y(n)=k_{1}y(n-1)+k_{2}x(n)+k_{3}x(n-1) \tag{10}\]  
where \(k_{1}=-0.0658\), \(k_{2}=0.5329\) and \(k_{3}=0.5329\).  
As shown in Figure 8, the amplitude remains unchanged from 0 to 2.25 HZ, and then decreases. Under this condition, high-frequency noise will be filtered out when the frequency of load change is not large and the phase-frequency curve is close to linear.  
The filter that is designed above meets the requirements of low-distortion transmission, the amplitude frequency of the signal is basically unchanged and the phase frequency is linear.
## An Autonomous Self-Aware and Adaptive Fault Tolerant  
Routing Technique for Wireless Sensor Networks  
**[PERSON] and [PERSON] ***  
Computer Systems Laboratory, Department of Computer Engineering, Chosun University,  
Gwangju Dongku SeoSuk Dong 375, Gwangju City 501-759, Korea;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-10-6851-2207; Fax: +82-62-230-7711.  
Academic Editor: [PERSON]  
_Received: 16 June 2015 / Accepted: 10 August 2015 / Published: 18 August 2015_  
We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.  
**Keywords:** wireless sensor networks; autonomous self-awareness and adaptive; routing technique; fault tolerant; route repair; self-healing; ASAART
## 3 Some Relevant Simulation Technologies in DDA
### Pre-Stressed Cable Numerical Realization in DDA  
As shown in Figure 9, a pre-stressed cable connects block \(i\) (\(x_{i}\),\(y_{i}\)) and \(j\) (\(x_{j}\),\(y_{j}\)). The increase in cable length is as follows:  
\[\begin{array}{rl}dl&=\frac{1}{l}[(x_{1}-x_{2})(u_{1}-u_{2})+(y_{1}-y_{2})(v_ {1}-v_{2})]+\frac{f_{0}}{s}\\ &=\left(\begin{array}{cc}u_{1}&v_{1}\end{array}\right)\left(\begin{array}{ c}l_{x}\\ l_{y}\end{array}\right)-\left(\begin{array}{cc}u_{2}&v_{2}\end{array}\right) \left(\begin{array}{c}l_{x}\\ l_{y}\end{array}\right)+\frac{f_{0}}{s}\\ &=D_{i}^{T}\left(\begin{array}{c}l_{x}\\ l_{y}\end{array}\right)-D_{j}^{T}\left(\begin{array}{c}l_{x}\\ l_{y}\end{array}\right)+\frac{f_{0}}{s}\end{array} \tag{5}\]  
where \(\mathbf{D}\) is the displacement vector in DDA; \(f_{0}\) and \(s\) are the anchorage pre-stress and stiffness, respectively. The strain energy of cable is as follows:  
\[\prod=\frac{e_{0}}{2}\left(dl+\frac{f_{0}}{s}\right)^{2}=\mathbf{D}_{i}^{T}\mathbf{E} _{i}\mathbf{E}_{i}^{T}D_{i}-2\mathbf{D}_{i}^{T}\mathbf{E}_{i}\mathbf{G}_{i}^{T}\mathbf{G}_{j}+\mathbf{ D}_{j}^{T}\mathbf{E}_{j}\mathbf{D}_{j} \tag{6}\]  
where \(\mathbf{E}_{i}=\mathbf{T}_{i}^{T}\left(l_{x},l_{y}\right)^{T}\) and \(\mathbf{G}_{i}=\mathbf{T}_{j}^{T}\left(l_{x},l_{y}\right)^{T}\). By taking the extreme value of Equation (6), six linear equations can be obtained; these equations were added into the global stiffness matrix of DDA, i.e.,  
\[\begin{array}{l}s\mathbf{E}_{i}^{T}\mathbf{E}_{i}\rightarrow\mathbf{K}_{ii}\\ s\mathbf{G}_{i}^{T}\mathbf{G}_{i}\rightarrow\mathbf{K}_{jj}\\ -s\mathbf{E}_{i}^{T}\mathbf{G}_{j}\rightarrow\mathbf{K}_{ij}\\ -s\mathbf{G}_{j}^{T}\mathbf{E}_{i}\rightarrow\mathbf{K}_{ii}\\ -f_{0}\mathbf{E}_{i}\rightarrow\mathbf{F}_{i}\\ f_{0}\mathbf{G}_{j}\rightarrow\mathbf{F}_{j}\end{array} \tag{7}\]  
where \(\mathbf{K}\) is the global stiffness matrix and \(\mathbf{F}\) is the loading vector.  
Figure 8: Viscous boundary in DDA.  
In the DDA method used for this article, the Coulomb Mohr criterion and open-close iteration were used to achieve the opening and sliding of the block boundary interface without considering the cracking and failure of the rock block. When two blocks come into contact, the anti-slip force of the block is obtained based on the normal force and friction coefficient and compared with the tangential force. When the tangential force is greater than the anti-slip force, the two blocks slide. In addition, whether the blocks re in contact can be determined based on the distance between them, achieving open-close iteration discrimination.
## 2 Theoretical Background  
[PERSON] [4] formulated the theory, which is graphically illustrated in Figures 2 and 3 and mathematically summarised in Equation (1). to describe the phenomena observed during silo discharge. Figures 2 and 3 and Equation (1) illustrate a state of equilibrium between the granular material inside the silo, the silo structure and surrounding air. AlthoughEquation (1). illustrates the equilibrium of the entire problem in a single degree of freedom, it must be understood that Equation (1). is an extension of the traditional equation of motion, which can be expanded to many degrees of freedom to capture and incorporate relevant physics. The theory incorporated a number of established scientific principles to illustrate equilibrium between the granules inside the silo, the silo wall and supporting structure, and the surrounding air for the entire duration of the discharge cycle--the commonly accepted silo theories lack such equilibrium and physics. Additionally, to adequately incorporate the relevant physics and simulate the phenomena discussed earlier, Equation (1). needs to be solved for the entire duration of the discharge cycle, taking into consideration the initial elastic deformations, forces applied, material properties, discharge rate, change in the total mass of the structure and properties of the silo structure as a whole to maintain equilibriums between the granules, the structure and surrounding air.  
\[(m(t)-dm)\ddot{y}(t)+(2\delta(t)\sqrt{k(t)(m(t)-dm)}\dot{y}(t)+k(t)y(t)=\sum f (t) \tag{1}\]  
where \(\ddot{y}(t)\) is the acceleration at time \(t\), \(\dot{y}(t)\) is the velocity at time \(t\), \(y(t)\) is the displacement at time \(t\), \(t\) is the time, \(m(t)\) is the total mass of the silo system including the materials inside the silo at time \(t\), \(\delta(t)\) is the critical damping ratio at time \(t\), \(k(t)\) is the stiffness of the system at time \(t\), \(dm\) is the mass loss at time \(t\), and \(\sum f(t)\) is the resultant dynamic force generated by the flowing granules and mass loss at time \(t\). The variables \(dm\) and \(\sum f(t)\) need to be determined from the material properties and net accelerations of the silo structure as a whole.  
Figure 2: Graphical illustration of Equation (1) for a parallel wall container.
###### Abstract  
The biomedical field entered the era of \"big data\" years ago, and a lot of software is being developed to tackle the analysis problems brought on by big data. However, very few programs focus on providing a solid foundation for file systems of biomedical big data. Since file systems are a key prerequisite for efficient big data utilization, the absence of specialized biomedical big data file systems makes it difficult to optimize storage, accelerate analysis, and enrich functionality, resulting in inefficiency. Here we present F3 BFS, a functional, fundamental, and future-oriented distributed file system, specially designed for various kinds of biomedical data. F3 BFS makes it possible to boost existing software's performance without modifying its main algorithms by transmitting raw datasets from generic file systems. Further, F3 BFS has various built-in features to help researchers manage biology datasets more efficiently and productively, including metadata management, fuzzy search, automatic backup, transparent compression, etc.  
distributed file system; biomedical big data; big data storage +
Footnote †: journal:  
disks installed in multiple server nodes together, so the capacity and scalability is no longer a problem, however the network communication overhead decreases the speed and latency of read/write operations.  
Moreover, besides the disadvantages above, since most existing file systems only provide general purpose storage platforms for generic datasets and are not specially designed for biomedical big data, they inevitably behave poorly in the following main aspects. Firstly, the generic file systems have not been optimized for the unique characteristics of biology big data, such as high repeatability, limited representation forms, and a relatively fixed file format, etc. Secondly, due to the complicated architecture of generic software, it is very hard to integrate more specialized features into the existing file systems [8]. Last but not least, they lack the built-in functionality to perform basic routine tasks related to biological data maintenance, such as sequence search, metadata management, and transparent compression. Some notable attempts have been made, such as openBIS [6], OMERO [5], relzfs [9], and FASTAFS [10], but the problems above are still unresolved: openBIS tends to provide a smart WEB GUI interface to collect and analysis data from lab experiment; OMERO is mainly focused on image data; relzfs is only a prototype of auto compression filesystem; FASTAFS is only suitable for data in FASTA format.  
Here, we present F3 BFS, an integrated file system dedicated to biology big data, featuring functional built-in utilities, fundamental infrastructure, and future-oriented architecture. F3 BFS is suggested to be able to greatly reduce the tedious routine workload of the data repository maintainer and increase the efficiency of the researchers.