esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Processing
### Spectrum Envelope  
Intuitively, the continuous waveform recorded by a seismometer with a window length of 1 h is in a basically stable form when no obvious events such as earthquakes or blasting are recorded, and the maximum value of the corresponding spectrum envelope amplitude fluctuates in a basically horizontal state [19]. Therefore, two key features are extracted when processing the continuous waveform. One is to obtain the spectrum from the 1 h continuous seismic waveform through a fast Fourier transform, and the other is to find the maximum amplitude of the spectrum envelope.  
\[\mathrm{x_{i}}=\frac{\mathrm{a_{0}}}{2}+\sum_{k=1}^{m}\left(\sqrt{\mathrm{a_{ k}+b_{k}}}\cos(\frac{2\pi ki}{N}+\arctan(-\mathrm{b_{k}}/\mathrm{a_{k}}))\right) \tag{3}\]  
\[\mathrm{a_{0}}=\frac{2}{N}\sum_{i=0}^{N-1}x_{i},\;\mathrm{a_{k}}=\frac{2}{N} \sum_{i=0}^{N-1}x_{i}\cos\frac{2\pi ki}{N} \tag{4}\]  
\[\mathrm{b_{k}}=\frac{2}{N}\sum_{i=0}^{N-1}x_{i}\sin\frac{2\pi ki}{N},\;k=1,2, 3,\ldots,m \tag{5}\]  
\(\mathrm{\{x_{i}\}}\) is the continuous waveform data sequence recorded in 1 h, which is the amplitude spectrum. The curve formed by connecting the highest lines of the different frequency amplitudes from the obtained spectrum is called a spectrum envelope, as shown in Figures 3 and 4 [20].  
Figure 3: Spectrum envelope of Wuhai (WUH) station at 5:00 on 6 April 2015.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 3 Mars to Earth Scenario
### DTN Topology and Links  
The DTN layout is shown in Figure 4. There are two regions, Mars and Earth. The former consists of one lander (Lander node) and one Mars orbiter (Orbiter). The terrestrial segment consists of four relays, three ground stations (GSs) and the European Data Relay Satellite GEO satellite gateway (EDRS), and two destinations: the ESA (European Space Agency) Mission Control Center (ESA-MCC) and the ESA Payload Data Ground Segment (PDGS). EDRS is used here more as a symbolic name than to actually denote the current EDRS system [20] and analogously for the PDGS node. In the figure, dotted or dashed lines denote intermittent space links where LTP is used; continuous lines represent continuous terrestrial links, with TCP. Note that the Orbiter is connected to the four terrestrial relay nodes, while the Lander is connected both to Orbiter and to GSs, but not directly to EDRS.  
Link settings are presented in Table 1. While the delay on both Earth and Mars links is assumed negligible, on interplanetary links is very long because the two planets have been considered at their maximum distance, namely about 23 min, it will be scaled down to 23 s in the experiments for convenience. Negligible losses are assumed on all links. Transmission rates vary considerably, as expected. The three interplanetary direct links between Lander and Ground stations have a very limited bandwidth in both directions (only 32 kbit/s); the shorter Lander to Orbiter link is much faster  
Figure 4: Layout of the Mars–Earth network considered in the paper. Dotted/dashed lines: space intermittent links, with LTP; continuous lines: terrestrial continuous links with TCP.  
(2 Mbit/s), again in both directions; the Orbiter to terrestrial relays (GSs and EDRS) links are fast in downlink (2 Mbit/s) but very slow in uplink (32 kbit/s).  
Mission Application Data Flows. For Convenience, Original Propagation Delays, in Minutes, Were Scaled-Down by a Factor of 60, i.e., Interpreted as Minues, in the Experiments  
Data flows are summarized in Figure 5. Both the Lander and the Orbiter receive Telecommands (TC) from the ESA-MCC and send back Urgent Telemetry (TM-EXP). These flows consist of small bundles with low generation rates (see Table 2) and, being urgent, they have \"expedited\" priority. Telemetry (TM) is also generated on board both space assets and sent to ESA-MCC, but has different characteristics, with larger and more frequent bundles, with \"normal\" priority. Last, we have the science flow, from Lander to PDGS. It consists of much larger bundles with \"bulk\" priority, with the highest generation rate. The focus of the experiments presented here will be on this flow, delivered either directly to destination, by means of DTNperf, or alternatively, by means of DTNbox directory synchronization. The other six flows are destined to act as background traffic, to make the scenario more realistic. Data flow characteristics are summarized in Table 2.
# PSO-Based Voltage Control Strategy for Loadability Enhancement in Smart Power Grids
## 6 Numerical Examples and Results
### IEEE 118-Bus  
To demonstrate the applicability of the proposed algorithm to large-scale power grids, the IEEE 118-bus is utilized as an example. This test model is composed of 54 generators, 64 loads, and 186 transmission lines [37].  
A series of simulations including different load bus variations, different load levels, and different branch outage contingencies has been conducted. Among those investigated tests, some selected cases listed are presented below.  
* Case 1: Several load bus variations and peak load conditions.
* Case 2: All load bus variations, peak load conditions, and line outage contingencies.  
The simulation results are shown in Figures 8 and 9. From the shown figures, one can see that the load bus voltage magnitudes after the proposed control strategy are all at the acceptable level.
## 2 Materials and Methods
### Scenarios Investigated  
In our previous study [25], we assumed that the occurrence of a traffic accident in the north tube would lead to different potential scenarios involving its partial or complete closure for 1 h, 2 h, or 3 h (however, in this investigation, we considered the worst-case scenario, which was a closure of 3 h). Specifically, the functionality of the investigated twin-tube motorway tunnel, after the occurrence of a traffic accident in the north tube, was assumed to be restored by utilizing the remaining available lane of the north tube (i.e., only one lane is closed) or rearranging the traffic flow using the south tube for two-way traffic (i.e., both lanes of the north tube are closed). The effects of the presence of an alternative route, along which to divert heavy goods vehicles (HGVs), were also investigated.  
In this study, we investigated the same scenarios assuming that, during the partial or complete closure of the north tube due to the occurrence of a traffic accident in it, a fire had simultaneously occurred on the undisrupted remaining lane of the north tube, or in the parallel tube used for two-way traffic, in the case of the partial or complete closure of the north tube.  
Figure 1 shows a cross-section of the twin-tube motorway tunnel examined.  
Figure 2 shows the scenarios investigated: (i) Scenario 0 represents the case in which the tunnel system is used under its ordinary conditions of functionality (i.e., without any traffic accident and with both lanes of each tube used for one driving direction) when a fire occurs in the north tube; (ii) Scenario Ia corresponds to the case in which, assuming that the north tube is partially closed due to a traffic accident on the right lane, a fire occurs on the undisrupted remaining lane of the north tube (i.e., the left lane); (iii) Scenario Ib is similar to Scenario Ia, but also includes the use of an alternative route along which, by using variable message signs (VMSs), only HGVs traveling towards the north are diverted; (iv) Scenario IIa corresponds to the case of the complete closure of the north tube and the use of the parallel tube for two-way traffic; here, the fire is assumed to occur on the lane used by the traffic flow traveling towards the north; (v) Scenario IIb is similar to Scenario IIa, but it also includes the use of an alternative route along which, using VMSs, only HGVs traveling towards the north are redirected. With reference to Scenarios IIa and IIb, the two traffic by-passes located at the tunnel portals, which allow for a change of carriageway, are assumed to be opened by the emergency service team in a very short time (i.e., 10 min) from the start of the traffic accident. The nodes E and I represent the two motorway junctions which are before the portals of the twin-tube tunnel that was investigated. Specifically, node E is used by HGVs when the alternative itinerary is recommended for these vehicles only.
# A Low-Complexity Volterra Filtered-Error LMS Algorithm with a Kronecker Product Decomposition
## 5 Performance Evaluation
### Simulation with Measured Primary Path and Secondary Path  
In this experiment, a real measured primary and secondary path is used to justify the proposed DVMFELMS algorithm's effectiveness in a real application. The amplitude and phase response of both primary and secondary paths is shown in Figure 8. The reference signal is chosen to be three sinusoidal waves at normalized frequencies of 0.01, 0.02, and 0.08, which is normalized to unity power [29]. A white Gaussian noise with zero mean and unit variance with a signal-to-noise ratio of 30 dB is also added. Furthermore, the reference signal is assumed to have strong saturation generated by clipping the reference at 50% of the maximum signal value. The memory length of the primary and secondary path is chosen to be 80. Figure 9 shows the NMSE achieved by all five algorithms versus the number of iterations. The step size for the FELMS algorithm is chosen to be \(\mu_{1}=1\times 10^{-4}\), step sizes for the second-order Volterra filter are set to \(\mu_{1}=1\times 10^{-4}\), \(\mu_{2}=1\times 10^{-4}\), step sizes for the 2 OFEGFLNN algorithm are chosen to be \(\mu_{1}=1\times 10^{-4},\mu_{2}=5\times 10^{-6}\), step sizes for the 2 OFEEMFNL algorithm are set to \(\mu_{1}=1\times 10^{-4},\mu_{2}=4\times 10^{-5}\), and step sizes for the 2 ODVMFELMS algorithm are set to \(\mu_{1}=1\times 10^{-4},\mu_{1}=1\times 10^{-3},\lambda_{2}=0.02\). Figure 9 shows that the reduction of primary noise achieved by the FELMS algorithm, the 2 OVFELMS algorithm, the FEGFLNN algorithm, the 2 OEMFNL algorithm, and the 2 ODVMFELMS algorithm in the real measured primary and secondary path cases is about 27.5 dB, 29.7 dB, 29.6 dB, 29.9 dB, and 29.6 dB, respectively. From Figure 9, it can be seen that the 2 OEMFNL algorithm outperforms all algorithms in NMSE, while having the lowest convergence speed. The proposed DVMFELMS algorithm can achieve almost the same NMSE compared with the VFELMS algorithm in this case. The number of multiplications in this experiment for all four nonlinear algorithms are 278960, 39210, 10289, and 958, respectively, which shows the potential in the real application of the proposed DVMFELMS algorithm.  
Figure 7: Performance comparison using the nonlinear primary path and secondary path with band-limited white Gaussian reference signal.
# Weibull-Open-World (WOW) Multi-Type Novelty Detection in CartPole3D
## 2 Problem Formalization
### CartPole3D with Environmental Agents  
As stated in the introduction, the novel CartPole3D environment has been constructed to utilize BulletPhysics [5], with a mixed C++/Python code base. The Eval-Team and ControlToTeam developed a slightly modified version of pyBullet that uses a modified planar constrained joint for the cart and to support setting state of the position and joints used for the cart. The latter was a critical function for the look-ahead controller used by the Controller-team. This was incorporated into a python environment use by both teams--in particular, this paper uses v0.7.9 of the system from [[https://github.com/holderlb/WSU-SAILON-NG](https://github.com/holderlb/WSU-SAILON-NG)]([https://github.com/holderlb/WSU-SAILON-NG](https://github.com/holderlb/WSU-SAILON-NG)) (accessed on 10 December 2021). The resulting CartPole3D model is a simulated environment with roughly 200 total changeable parameters/properties/attributes, some of which have easily changeable python interfaces, some of which are in model \"urdf\" files, and some of which are only in the C++ code.  
Figure 2 shows a graphic rendering of the basic CartPole3D environment, the cart, the pole, the walls, and an example with four environmental agents. While the simulator can generate images, in this project, we do not receive images, just low-dimensional observation vectors, which is all the information the controlling agents receive about the environment.  
For the cart controllers, we consider three different models,  
* DQN-agent: Deep-Q-learning Network (DQN)-based control algorithm, which is the baseline
* GOWN-agent: Gaussian-based Open-World Novelty agent
* WOW-agent: our Weibull-based Open-World controller agent  
The Eval-Team-team developed trainned a DQN-based agent for CartPole3D to serve as a baseline. The baseline DQN-agent was a standard deep neural network with two layers of 512 fully connected nodes with rectified linear unit activation. The agent used DQN training, similar to that in [3; 6; 7]. The agent was trained on non-novel CartPole3D episodes and converged after 500k steps. The final average performance reached was 0.8 out of 1.0, which represents keeping the pole balanced for 160-time steps on average out of the possible 200-time steps.  
The Control-Team-team implemented the WOW-agent and GOWN-agent; see the MDPI branch of [EMAIL_ADDRESS]:Vastlab/SAILON-CartPole3D.git (accessed on 10 December 2021). Both used a lookahead-based control algorithm, where it received the current observational vector and compared that with its projected state. We noted that to support the Control-Team state setting functions needed for the WOW-agent lookahead type controller, Control-Team developed a modified pybullet. The state setting was imperfect as the test system only provided truncated position and velocity of the cart, pole, and active agents--which produced insufficient information to exactly recreate the state. Thus the lookahead state prediction was inherently noisy, and the WOW-agent must detect novelty in the presence of a noisy world model.
## 2 Induction Heating and Numerical Simulations  
### Induction Heating-Basic Principles  
The basic principle of induction heating lies in affecting the heated workpiece by a rapidly alternating magnetic field, which is typically generated by an inductor coil (depicted in red color in Figure 1a) surrounding the heated workpiece. The alternating electric current \(I_{1}\) (typically generated by a frequency converter) of the required amplitude and frequency, depicted in the black color in Figure 1a, is supplied through the coil. The heating process is then realized by the induction principle: the rapidly alternating magnetic field in the workpiece generates the induced voltage within the workpiece [42; 43]. In other words, an induction field, variable in time, induces a local electromagnetic force resulting in the generation of voltage. This induced voltage, enclosed by the workpiece cutting plane, generates a short circuit immediately generating eddy current \(I_{2}\). The eddy current is shown in Figure 1a,b, demonstrated as IZeddy (A) (the direction of which is depicted in blue color in Figure 1a), and eventually results in the heating of the workpiece via the Joule heating principle, i.e., the eddy currents are intensified by the developed short circuit.  
Generally, the distribution of the induced eddy current is not uniform throughout the cross-section of the heated workpiece; its intensity is the highest in the surface region of the workpiece and decreases rapidly towards its axial region (see Figure 1b). This phenomenon is characterized as the skin effect, which is defined by the penetration depth \(\delta\) (m) expressed via Equation (1),  
\[\delta=\sqrt{\frac{1}{\pi.f.\sigma.\mu}} \tag{1}\]  
where \(f\) is the working frequency of the electromagnetic field (Hz), \(\sigma\) is electric conductivity (\(\Omega^{-1}\).m\({}^{-1}\)), and \(\mu\) is magnetic permeability (H.m\({}^{-1}\)).  
In other words, Equation (1) determines the current density beneath the workpiece surface, which is equal to 36.79% of the current density value on the surface. This value is the boundary value of the conductive layer's thickness; below this boundary (i.e., further towards the workpiece axis) the effect of the current density decreases significantly. From Equation (1) it is also evident that the mutual relations of the individual affecting parameters are the primary factors influencing the induction heating process. The parameters that can be varied using Equation (1) are, for example, the penetration depth at a given frequency, or the particular temperature distribution throughout the workpiece cross-section.
# A Data-Driven Approach to Improve Customer Churn Prediction Based on Telecom Customer Segmentation
## 5 Factor Analysis to Characterize Expense, Call, and SMS Attributes  
### Expense Factor Analysis  
#### 5.1.1 Variable Selection  
Factor analysis refers to the concept that significant and measured variables can be decreased to less latent variables with common variance [33]. Some factors are unobservable and unmeasurable, but variables can be reduced into the same group based on similar characteristics to test the relationships [34]. Expense data, such as monthly fee, package type, or mobile terminal price data, can be used to distinguish and characterize customers into different customer consumption tags [29]. Cost and expense management is critical to the operation of companies, and the factor analysis approach could be used to study the expense and cost data and to understand the relationships between the variables [35]. Telecom customer cost data, such as wireless data fees, are suitable for use in factor analysis and could be used to understand customer behavior [36]. Thus, the telecom customers' expense data were selected to conduct the following factor analysis. All expense-related factors, including the (1) total fee receivable for the month, (2) fixed monthly costs, (3) local fee, (4) roaming fee, (5) Unicom's network fee, (6) China Mobile's fee, and (7) fixed-line fee, were used to conduct the factor analysis and analyze the characteristics of the cost factors. Later, Kaiser-Meyer-Olkin (KMO) and Bartlett tests were applied to identify whether these factors are suitable for factor analysis.
# Response Statistics of a Shape Memory Alloy Oscillator with Random Excitation
## 5 Conclusions  
In this paper, we explored the nonlinear dynamical responses of a SMA oscillator model with a narrow-band random excitation. The multiple-scale method was employed to achieve the theoretical analysis of the proposed stochastic SMA oscillator system. We uncovered the fact that the obtained analytical solutions coincided perfectly with the numerical ones for both the deterministic and stochastic cases. The stochastic switch and bifurcation were observed from the stationary PDFs and the time history responses with the change of the noise intensity. The results show that the PDFs changed from one peak to double and then gradually to one peak again with the increase of the noise intensity, which indicates that the noise intensity has a great influence on the properties of SMA materials and can lead to stochastic switch and bifurcation. We also explained the phenomena of stochastic switch and bifurcation via the time history responses at different noise intensities.  
From the perspective of practical applications, a random disturbance with small intensity will destroy the stable structure of the SMA oscillator system, which causes the original system to produce strong disordered oscillations and operational collapse in a stable state. Therefore, it is of great significance to study the influences of random disturbances on the evolution of the dynamic behaviors of the system for the safe and reliable operation of the SMA oscillation system.  
Conceptualization, R.G. and J.L.; methodology, R.G. and Q.L.; software, R.G. and Q.L.; validation, R.G.; formal analysis, R.G., J.L. and Y.X.; investigation, R.G.; resources, R.G. and J.L.; data curation, R.G.; writing--original draft preparation, R.G.; writing--review and editing, J.L., Y.X. and Q.L.; visualization, R.G.; supervision, J.L. and Y.X.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NSF of China, grant number 11972019.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 8: Time history of the original SMA oscillator system with the same temperature and the fixed system parameters \(\zeta=0.1\), \(\beta=-1.3\times 10^{3}\), \(\gamma=4.7\times 10^{5}\), \(\theta=1.9\), \(f=0.6\), \(\Omega=2.0\), and different noise intensities: (**a**) \(h=0.05\); (**b**) \(h=0.1\); (**c**) \(h=0.15\); (**d**) \(h=0.3\). The initial conditions are set to \(x(0)=0.01\), \(\dot{x}(0)=0.01\), corresponding to the low-amplitude oscillation state.
## 2 Pre-Existing Circuit Structures of ESD for Distributed ERT System  
### Overall Structure of Distributed ERT System  
A distributed ERT instrument is usually comprised of one main control structure and multiple distributed structures with the same architecture. For discussion convenience, we used a Wenner test as an example, however other tests are similar. Details are shown in Figure 1. The ports AMNB exist in the main control structure, and the electrodes exist in the distributed structures. The two parties interrelate with each other through the bus, consisting of four lines. To accomplish a single test, the four testing electrodes must be connected to AMNB in the correct order. The complete ERT survey is composed of back-to-back single resistivity tests. The task of ESD is to set up all test connections one by one in predetermined order. ESD is composed of many switch structures (SS). They exist in two parities, the main control and the distributed structure, as shown in Figure 1. They are respectively called SS1 and SS2. Herein, SS1 is responsible for access of AMNB to the bus and SS2 is responsible for access of the four testing electrodes to the bus. Then, via the bus, two elements, AMNB and four testing electrodes, are interconnected.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 2 Related Work
### USV Platforms  
Several previous works have developed USVs for aquatic monitoring and marine environments. [PERSON] et al. [4] stated that most efforts are related to general ocean sensing and water quality monitoring, while this work focuses on fish farming where there are not many technological solutions. [PERSON] et al. [5] proposed a group-based underwater wireless sensor network to monitor the fecal waste and uneaten feed deposited on the seabed. The nodes' mobility, grouped with the number of nodes, give them a coverage of 100% of the tanks. [PERSON] et al. [6] presented a centralized approach based on ZigBee with a master-slave architecture to measure temperature and Potential of Hydrogen (pH) levels. They also developed a power consumption algorithm to improve the node lifetime. [PERSON] et al. [7] developed a robotic raft with searching and fish tracking algorithms which, in several aquatic environments, has shown to have good results. [PERSON] et al. [8] presented an IoT-based system to process the tracking, collect, and monitor the water quality. Each node is equipped with sensors, a microcontroller and Long Range (LoRa) capabilities. The system has an alert triggering mechanism to alarm the authorities in the case of any changes in water quality, similar to the work in [9], although not applied to USV systems.  
None of the previous approaches considered the interaction between the USVs in the swarm to optimize the aquatic monitoring process. Our proposal is to develop a robust swarm of robots with monitoring capabilities in an IoT platform with multiple communication technologies, with opportunistic and delay tolerant communications, giving space for a multi-hop data forwarding approach.