esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 2 Seed to the Data Acquisition System and Experimental Process  
The dataset used to evaluate this work is acquired from the self-defined bearing testbed of Ulsan Industrial Artificial Intelligent Laboratory (UIAI) at Ulsan University (Ulsan, South Korea). The overall data are collected from bearings which were classified into normal (healthy) condition and bearings with artificial damage. The damaged bearings consisted of those with outer damage, bearings with inner damage, and bearings with roller damage. The test rig setup is described in Figure 1 which illustrates the real testbed image, and the different cases of artificial cracks generated on the bearings. During the data collection, the testbed was driven under a constant speed of 1800 r/min by the three-phase motor. The belt helps to transmit the motion from the rotor shaft to the main shaft which is installed with two testing bearing housings on both sides. The cylindrical roller-element bearing in type FAG NJ206-3-TVP2 was used in this experiment. The AE signal and vibration accelerometer signal are acquired mostly from the target bearing on the left side. A constant load of 100 kgf was applied in both axial and radial directions to the main shaft and the bearing house.  
The AE signal and vibration signal are recorded by the AE sensor of type R15I-AST [25] and accelerometer of type PCB-622B01 [26]. These sensors are both connected with the NI-9234 DAQ device which has four analog input channels and is designed to perform precise measurements from IEPE (Integrated Electronics Piezo-Electric) sensors. The NI-9234 is equipped with built-in anti-aliasing  
Figure 1: The self-defined testbed setup for measuring signals of bearing faults.  
filters that have the ability to automatically regulate the sample rate the user specifies. The signals were collected with a sampling rate of 25 kHz. A detailed description of the dataset acquisition system is shown in Table 1. Each type of fault signal in the bearing is measured continuously for about 5 min, then segmented to 1-s signals for analysis. Therefore, each type of fault includes 309 data samples of 1-s signals. Then the testing bearing is replaced with another one and the test is repeated.
# Hyperspectral Image Classification Using 3D Capsule-Net Based Architecture
## 5 Conclusions  
Due to the high levels of similarity between classes and the high levels of intra-class variation, HSI classification is a difficult undertaking. In this research, we have put forward a Capsule-Net based architecture for HSI classification. The benefit of this strategy is that it only requires a minimal number of tagged samples to be effective. The proposed model can also extract pertinent features while still maintaining the valuable spatial-spectral data for categorization. The challenges of significant intra-class variation and inter-class similarities are being addressed by using spatial-spectral data from 3D Capsule-Net. In the research, this strategy is demonstrated to rapidly and effectively increase classification results. Moreover, it is shown that the HSI classification method can be expanded to include the main high contextual classification task.  
Conceptualization: [PERSON] and [PERSON]; methodology: [PERSON] and [PERSON]; software: [PERSON] and [PERSON]; validation: [PERSON] and [PERSON]; investigation: [PERSON] and [PERSON]; writing--original draft preparation:[PERSON] and [PERSON]; writing--review and editing: [PERSON] and [PERSON]; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2020R1A2C2005612).  
Not Applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Indian Pine Dataset (IPD)** & & & & & \\ \hline  & DSVM & DCNN & CNN & R-VCA & 3 DCNN & Proposed \\ OA & 92.24 & 94.86 & 94.99 & 94.26 & 96.54 & **99.41** \\ AA & 92.13 & 96.72 & 94.81 & 94.08 & 96.47 & **97.93** \\ \(\kappa\) & 92.25 & 94.78 & 94.85 & 94.18 & 96.40 & **99.33** \\ \hline
**Parameter** & **Salinas Dataset (SD)** & & & & & \\ \hline  & DSVM & DCNN & CNN & R-VCA & 3 DCNN & Proposed \\ OA & 95.12 & 96.56 & 97.12 & 96.74 & 96.36 & **98.91** \\ AA & 95.02 & 96.31 & 97.03 & 96.61 & 96.22 & **99.44** \\ \(\kappa\) & 95.07 & 96.30 & 97.15 & 96.47 & 96.28 & **98.785** \\ \hline
**Parameter** & **Pavia University Dataset (PUD)** & & & & & \\ \hline  & DSVM & DCNN & CNN & R-VCA & 3 DCNN & Proposed \\ OA & 92.24 & 94.86 & 94.99 & 94.26 & 96.54 & **99.74** \\ AA & 92.13 & 96.72 & 94.81 & 94.08 & 96.47 & **99.51** \\ \(\kappa\) & 92.25 & 94.78 & 94.85 & 94.18 & 96.40 & **99.66** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of proposed framework with existing techniques.
# The Impact of Antenna Height on 3D Channel:
## 2 Methods and Numerical Results
### Numerical Results  
The simulation scenario is part of the center of Paris, as shown in Figure 1. The size of the area is 500 m \(\times\) 300 m, and the average building height is approximately 21 m [35]. We configure the transmit power of the BS as 0 dBm and the carrier frequency as 2.4 GHz (these two values can be set as any reasonable values at low frequency). For sake of convenience, we regard there are 4 \(\times\) 1 omnidirectional antennas arranged in horizontal plane in the BS. The maximum reflection and diffraction numbers are 3 and 7, respectively. The parameters are listed in Table 1.  
Figure 1 illustrates that there are three typical street scenarios: a straight street (red rectangular area), a forked road (black rectangular area), and a crossroad (dark red rectangular area) [36]. These three typical scenarios are displayed in Figure 2 and the parameters for each scenario are listed in Table 2. During simulations, \(h\) is the height of the BS, and \(n\) is the index of MSs from 1 to 100,000, which means 100,000 receivers are uniform distributed around the BS in total. We deployed the BS at (0, 0, h) and the receivers at (x\({}_{n}\), y\({}_{n}\), z\({}_{n}\)) in the global coordinate system. We regard the angles above the antenna height as having a negative degree (<0\({}^{\circ}\)), and the angles below the antenna height as a positive degree (>0\({}^{\circ}\)). Without loss of accuracy, we have not considered the direct component.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Size of Area**} & **Average** & \multirow{2}{*}{**BS Power**} & **Carrier** & **Max** & **Max** \\  & **Buildings** & & **Frequency** & **BS Antenna Type** & **Reflection** & **Diffraction** \\  & **Height** & & & **Number** & **Number** \\ \hline
500 m \(\times\) 300 m & 21 m & 0 dBm & 2.4 GHz & Omnidirectional 4 \(\times\) 1 & 3 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of the experimental scenarios.  
Figure 1: Part of the center of Paris (the red rectangular area, the black rectangular area, and the dark red rectangular area represent one of the straight streets, forked roads, and crossroads, respectively) [35].
## 3 Results  
It was found that the study groups did not differ in terms of age, darts experience, perceived self-efficacy, or in any of the variables that were controlled in the procedure that could interfere with performance (Table 1). Thus, no differences were found for state anxiety, perceived pressure, or attentional focus. Furthermore, the sphericity condition (Mauchly's W) was met (\(p=0.082\)). In addition, Levene's test indicated equality in variances between the two groups for all phases studied (Ph1: \(F_{(1,47)}=0.119\), \(p=0.730\); Ph2: \(F_{(1,47)}=2.557\), \(p=0.116\); Ph3: \(F_{(1,47)}=0.038\), \(p=0.846\); Ph4: \(F_{(1,47)}=0.589\), \(p=0.447\); Ph5: \(F_{(1,47)}=0.019\), \(p=0.891\); Ph6: \(F_{(1,47)}=0.372\), \(p=0.545\)).  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **G1** & **G2** & **Total** & & **\(p\)** \\ \hline Women & 9 (37.5) & 9 (36.0) & 18 (36.7) & \(\chi^{2}_{(1,49)}=0.012\) & 0.913 \\ Age & 17.21 (0.42) & 17.08 (0.28) & 17.14 (0.35) & \(t_{(47)}=1.268\) & 0.212 \\ Self-efficacy & 31.67 (3.92) & 31.12 (4.00) & 31.39 (3.93) & \(t_{(47)}=0.483\) & 0.632 \\ Recent Practice & & & & & \(\chi^{2}_{(3,49)}=2.609\) & 0.456 \\  & \(\leq\)5 & 13 (54.2) & 15 (60.0) & 28 (27.1) & & \\  & 6 to 15 & 6 (25.0) & 8 (32.0) & 14 (28.6) & & \\  & 16 to 25 & 2 (8.3) & 0 (0.0) & 2 (4.1) & & \\  & \(>\)25 & 3 (12.5) & 2 (8.0) & 5 (10.2) & & \\ Exp. Darts & 3.67 (1.99) & 3.20 (1.78) & 3.43 (1.88) & \(t_{(47)}=0.865\) & 0.391 \\ State anxiety & 14.25 (7.66) & 17.64 (7.41) & 15.98 (7.65) & \(t_{(47)}=1.574\) & 0.122 \\ Pressure & Ph2 & 5.63 (1.91) & 5.60 (2.52) & 5.61 (2.22) & \(t_{(47)}=0.039\) & 0.969 \\  & Ph3 & 6.13 (2.36) & 6.04 (2.78) & 6.08 (2.56) & \(t_{(47)}=0.115\) & 0.909 \\  & Ph4 & 6.29 (2.42) & 6.04 (2.99) & 6.16 (2.70) & \(t_{(47)}=0.323\) & 0.748 \\ \hline \end{tabular}
\end{table}
Table 1: Socio-demographic variables and variables controlled for in the procedure (self-efficacy, dart experience, state anxiety, perceived pressure, and attentional focus) for each group.  
It should be noted that, in response to the control question regarding the perceived aim of the study (to check whether the real aim had successfully been masked to avoid demand characteristics), only 12.2% of the participants believed that the study aimed to analyze the influence of feedback on the execution of dart-throwing (13.7% of G1 and 8% of G2).
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 4 Slope-Based RAIM Availability Assessment
### Slope-Based RAIM Availability Assessment Method
Compared with the ideal-PL-based method, the slope-based method separates the process of numerical iteration from on-board RAIM availability assessment, reducing the burden of on-board computing.
## 5 Evaluation Metrics
### Spearman Rank-Order Correlation Coefficient (SROCC)
#### 5.1.3 Root Mean Squared Error (RMSE)  
The RMSE gauges the difference between the expected and actual quality ratings. The RMSE is defined as,  
\[RMSE=\sqrt{\frac{1}{M}\sum_{l=1}^{M}(q_{pred,l}-q_{true,l})^{2}} \tag{12}\]  
\(M\) is the total number of test images, \(q_{pred,l}\) is the projected quality rating for image \(l\), and \(q_{true,l}\) is the actual quality rating for image \(l\). The smaller the RMSE, the greater the model's performance.
## 2 Preliminaries and Problem Formulation  
### IEEE 802.15.4 CSMA/CA Protocol  
In this section, a brief overview of the IEEE 802.15.4 CSMA/CA protocol is first provided, focusing on the details that are related to the proposed study. A more comprehensive introduction can be found in [27].  
The 802.15.4 is part of the IEEE family of standards that defines the physical layer (PHY) and medium access layer (MAC) for wireless personal area networks (WPAN). It is intended for devices with low power data rate, low complexity and stringent power requirement. The raw data rate in industrial, scientific and medical (ISM) band is 250 kbps. The basic time unit \"slot\" is defined as the unit backoff period which contains 10 bytes. In the time-slotted beacon-enabled mode, a superframe structure is used to govern the packet transmission. Each superframe begins with a beacon sent by the coordinator, followed by an active portion and an optional inactive portion. The active portion consists of a contention-access part and an optional contention-free part.  
This paper considers the slotted CSMA/CA in the contention access portion. In a slotted CSMA/CA network, a node trying to transmit a packet would first initialize the counter called the number of backoffs (NB). Then the node performs the backoff algorithm to delay for a random number of time slots which is uniformly distributed in the range of \([0,\quad 2^{BE}-1]\), where BE is the backoff exponent. When the backoff period is over, the node performs a clear channel assessment (CCA) to detect whether the channel is idle. The node would begin to transmit data if two consecutive CCAs are idle. Otherwise, the node would increase the number of NB and BE by one, without exceeding the maximum backoff limit \(m\) and maximum backoff exponent \(m_{b}\) respectively. The packet will be discarded if NB exceeds \(m\). Otherwise, the node will start to transmit a packet after performing two CCAs to confirm that the channel is idle. An ACK would indicate successful transmission. If the node fails to receive ACK due to collision or ACK timeout, the packet will be considered a failure assuming that no retransmission mechanism is implemented.
## Abbreviations  
The following abbreviations are used in this manuscript:  
**Notation**: **Definition**: FSO & Free-space optical  
Tx & Transmitter  
Rx & Receiver  
SNR & Signal-to-noise ratio  
BER & Bit error rate  
PDF & Probability density function  
HD & Heterodyne detection  
IM/DD & Intensity modulation/direct detection  
PD & Photo-detector  
**Parameters**: **Definition**: \(I_{l}\) & pathloss  
\(I_{a}\) & foggy channel  
\(I_{p}\) & pointing error  
\(l\) & the length of the propagation link (km)  
\(\alpha\) & signal attenuation random variable (dB/km)  
\(\beta\) & continuous scale parameter  
\(\Gamma(\cdot)\) & Gamma function  
\(k\) & continuous shape parameter of fog  
\(V\) & visibility  
\(\lambda\) & wavelength  
\(q\) & size distribution of the scattering particles  
\(\omega_{z}\) & beamwidth of a Gaussian beam  
\(a\) & aperture radius  
\(s\) & the radial displacement between the centers of the beam and the detector  
\(A_{0}\) & fraction of the collected power at \(s=0\)  
\(v\) & ratio between the beamwidth and aperture radius  
\(\omega_{z_{q}}\) & the equivalent beamwidth  
\(\text{erf}(x)\) & error function  
\(\xi\) & ratio of the equivalent beam radius at the receiver  
\(\sigma\) & standard deviation of the pointing error displacement at the receiver  
\(\overline{C}\) & ergodic capacity  
\(\gamma^{\prime}\) & instantaneous SNR  
\(\mu^{\prime}\) & average electrical SNR
## 2 Experimental Design and Data Collection
### Data Acquisition and Preprocessing  
The voltage frequency of the input three-phase motor is 50 kHz, the sampling number is 10,000 as the sampling condition, and the sample duration is 0.2 s, including 10 cycles of voltage waveform. When there are 15 or more fault arc features in the sample, the sample is identified as a fault sample, and a label is added at the end of the data according to its fault type. The host used in this experiment is configured with an Intel (R) Core (TM) i7-7700 hq processor, the GPU is an NVIDIA GeForce GTX 1080 Ti dual graphics card, and it has 16.00 GB running memory. Under the Windows 10 system, Python is used to realize the algorithm of the experimental system. Two thousand sets of data were collected for each group of experiments, and the training and testing datasets were allocated by 7:3.
# Dynamic Stability Analysis of Isolated Power System
## 5 Impact of the Isolated System's Response on Settings of HVDC BtB Converter
### Response of Isolated EPS to SC Disconnection  
The initial conditions for the digital simulation were as follows: 1. The operating SC (synchronous condenser) disconnects with (\(-10\) MW) load; 2. SC frequency control gain is 1250 MW/Hz and corresponds to 0.8% droop; 3. SC frequency control gain is 250 MW/Hz and corresponds to 4% droop; 4. the ramp speed of active power is 100 MW/s; 5. the dead-band of the system's frequency change is 10 mHz, at a gain of \(K=1250\) MW/Hz; 6. SC reactive power is not transferred to the isolated EPS, and \(Q=0\) MVar. The simulation results are shown in Figures 13-16. FC here denotes Frequency Control.  
Figure 14: (a) Active power response of the HVDC BtB converter. (b) Reaction of the HVDC BtB converter when FC is off.  
Figure 13: (a) Frequency change in the isolated power system—synchronous condenser disconnection. (b) Active power response of the HVDC BtB converter when \(db=10\) mHz.  
The results in Figures 13-16 show that both with the gain factor \(K\) = 1250 MW/Hz and with \(K\) = 250 MW/Hz, damped active power oscillations in the HVDC BtB converter reaction were observed. It is recommended to use a gain of \(K\) = 250 MW/Hz during the operation of an isolated EPS.  
After SC disconnection, the frequency of the isolated EPS begins to increase. The highest point when the frequency controller is off is 50.08 Hz, and 50.04 Hz when the frequency controller is on [20,22,32,35].  
Due to a possible 330 kV voltage spike after disconnecting the SC (synchronous condenser), it is recommended to turn off the SC with reactive power generation \(Q\) = 0 MVar [23,36].  
Response of Isolated EPS to SC Disconnection, When 200 MW Generator Has Temporary Droop Settings and Time Constants  
A digital simulation was performed with the standard and temporary parameters of the unit operating in generator mode:  
Generator temporary droop, \(r\) = 1.5;  
Turbine regulator temporary time constant, \(T_{r}\) = 10 s.  
The generator's standard frequency controller parameters were set as follows:  
Gain \(K\) = 250 MW/Hz, dead-band = 200 mHz, droop \(r\) = 2.5, turbine regulator time constant \(\bar{T}r\) = 15 s and ramp = 100 MW/s. The simulation results are shown in Figures 17 and 18.  
Figure 16: Voltage fluctuations on the 330 kV side.  
Figure 15: (a) Active power compensation of the thermal power plant at different FC parameters. (b) Active power compensation of the thermal power plant at different FC parameters.  
When using the temporary parameters of the unit operating in the generator mode, a smaller frequency increase is seen; in addition, the amplitude of the active power oscillations of the HVDC BtB converter decreases from 10 MW to \(\sim\)5 MW [20, 22, 32, 35, 36, 37, 38].
# A Review of Counter-UAS Technologies for Cooperative Defensive Teams of Drones
## 2 Definitions and Basic Concepts for Cooperative Drone-Based Counter-UAS Systems
### Counter-UAS Systems  
In the literature, it is possible to find numerous surveys that have explored the characteristics of anti-drone systems (see for example [5; 9; 10; 11; 12; 13]). In this regard, the taxonomy presented in [9] is of particular interest, in which CUS are grouped into two categories: ground-based and sky-based, depending on their deployment, respectively, on the ground or in the air using drones or other flying platforms (for example, stratospheric platforms). Ground-based systems can be of the static type, if installed, even temporarily, in a fixed manner within the perimeter to be defended, or of the mobile type if installed on-board land vehicles or transported by hand by humans (human-handled). Sky-based systems are implemented on board drones, UAS, balloons or stratospheric platforms, and deployed as needed. They differ in high altitudes and low altitudes, depending on the operating altitude.  
The two types of CUS oppose each other with respect to the level of operational mobility and the characteristics of weight, size and energy required for operation (size, weight and power, SWaP). Ground systems have the clear advantage of being able to count on weak SWaP requirements (increasingly from static systems to human-handled ones), but have little flexibility in terms of adapting to the unpredictable behavior of malicious drones. On the other hand, sky-based systems have greater adaptability thanks to the inherent maneuverability and flexibility that are afforded with the much more stringent SWaP requirements due to the limited power of the batteries and the low payload capacity for the lighter flying platforms. In choosing the platform to be adopted, it is important to consider its advantages and disadvantages and the operational scenario in which the solution is used.  
It is also possible to create a hybrid CUS as a heterogeneous and cooperative network of different platforms (both ground-based and sky-based) to balance the limits that each solution would have if used individually. Indeed, although a CUS can consist of a single platform, it is difficult for such a solution to deal with the threats represented by a malicious drone or even by several malicious drones, so solutions that offer greater reliability and spatial coverage are represented by CUSs comprised of multiple platforms. In this case, the platforms are networked to cooperate, maximizing the effectiveness.
## 3 Methodology
### Trnsys Modeling  
TRNSYS is an acronym for \"Transient System Simulation Program\", and is a complete and extensible simulation environment for transient simulations of systems [20]. The TRNSYS 17 simulation program was used to perform the simulations needed for the performance evaluation and optimization. The interrelationship of various system components is shown in Figure 4. The water-based PV/T collectors can be regarded as a flat-plate collector with a single glazing sheet, and their thermal performance can be evaluated based on the Hottel-Whillier model [16]. The main component of the TRNSYS deck file is Type 45a. This component models a thermosyphon solar water heater system which includes a flat-plate solar collector, a stratified storage tank and water as working fluid [20]. The system is analyzed by respectively dividing the collector and the water tank into a number of nodes. The thermal performance of the flat-plate solar collector is modeled using the Hottel-Whillier model. The thermosyphon loop is divided into a number of segments, and [PERSON]'s equation is applied to each segment. The flow rate is obtained by numerical solution of the resulting equations [20]. TRNSYS type 50a (PV-thermal collectors) is used to model the electric energy output of the PV/T panel. The temperature gradients across the thickness of the cell/plate composite are neglect for the PV cells are bonded to plate surface tightly and the cell thickness is very small. The temperature of the cell/plate composite is calculated by iteration. The cell efficiency is assumed to decrease linearly with the temperature of cell/plate composite. The heat loss coefficient of the PV/T panel is assumed to be constant. The values of the water temperature and flow rate to collector output from TRNSYS type 45a are used as the inputs of TRNSYS type 50a. The electric energy output is determined based on the assumption of maximum power point tracking (MPPT).  
[PERSON] et al. performed a series of tests for water-based sheet-and-tube PV/T solar collectors [17]. The parameters for thermal efficiency found by [PERSON] et al. are adopted for the water-based sheet-and-tube PV/T solar collector in this study. When the tested flow rate is 0.038 kg/s, the collector efficiency equation is [17]:  
\[\eta=0.566-6.08\frac{T_{in}-T_{a}}{G}, \tag{9}\]  
where \(T_{in}\) and \(T_{a}\) are respectively the water inlet temperature and ambient air temperature (\({}^{\circ}\)C), \(G\) is the solar irradiation (W/m\({}^{2}\)). During the simulation process of TRNSYS type 45a, a certain modification will be made to Equation (9) according to actual flow rate.  
Figure 4: Data flow diagram in TRNSYS component representation.  
The orientation of the PV/T collector is set as due south. The main parameters for the developed TRNSYS deck file are listed in Table 2. The monthly average cold water temperatures in Guangzhou are presented in Figure 5.