esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Experiment 2: DoS Attack on Physical Devices  
Given the outcome of the first experiment as explained in Section 4.1, our second experiment investigated the impact of DoS attacks on physical devices. This experiment follows the same attack methodology as the first experiment; however, it is a hardware-based experiment with a range of operating systems used. In other words, the second experiment was designed to be the physical counterpart of the first experiment. In particular, the hardware used was Raspberry Pi Zero and USB Power Analyser, as can be seen in Figure 1, while the software used was:  
* Tiny core Linux (as close as possible to a bare-bones Linux installation);
* Yocto OS (an IoT-based distribution that may fail to compile for Pi Zero);
* Raspberry Pi OS (a Debian-based distribution to provide a possible contrast);
* Buildroot embedded Linux (a backup option for Yocto OS);
* Kali Linux (a backup option for Tiny Core Linux);
* Temp monitoring script;
* Benchmarking software;
* Balena-Etcher ISO to bootable media converter;
* Hping3 package.  
The same three-stage approach was used, similar to the first experiment; however, this time, the devices reside on the same physical network and without Internet connectivity. The attacks used the following default/well-known ports to keep the tests fair; they also did not use any ports above 1023 as those ports are usually not utilised. The UDP flood attack used port 80, SYN flood port 80, and ICMP flood targeted the ping functionality of the device which does not use a port. The estimated throughput of packets a second for each attack was: ICMP flood 5000/s, SYN flood 600/s, and UDP flood at 2500/s. In particular, the script was first run on Raspberry Pi OS, then on Tiny Core and finally on Yocto OS. However, due to technical issues, Raspberry Pi OS, Kali Linux, and Buildroot OS were used instead. During the experiment, the following variables were measured:  
* A note of the device's average core temperature is taken during normal operation and again during the attack to enable a vector of comparison between them;
* Power draw of the device during normal operation and under attack;
* Another PC intercepting network traffic and running Wireshark in mon0 mode.  
A simplified version of this attack is presented in Figure 2. Wireshark was used for monitoring purposes. In total, three devices were connected to one network router, which was utilised as a switch and was used to enable the communication of the attacker and victim (Raspberry Pi Zero W), and the capture of packets sent between them to be able to calculate different attack loads that the Raspberry Pi can handle.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 2 Small Satellites for VIS, TIR, and MW Remote Sensing
### Current Status of Small Satellite Payload for Earth Observation  
Small satellites for EO often carry optical or microwave sensors, providing observations with a range of spatial, spectral, and temporal resolutions. Here, we will briefly provide an overview of these sensors (values are taken from manufacturer or space agency websites). In Table 2, we list a number of small satellites (still active or ended), equipped with either remote sensing optical or microwave payloads. These examples have been developed during the last few decades for the EO, with characteristics (e.g., payload characteristics, revisiting time, and costs) that can be fully or partially suitable to image and monitor the study cases presented in this work. In Table S1 in the Supplementary Material, we reported the same parameters, but for medium to large satellites, as comparison.
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 4 Experimental Results and Discussion
### Effect of Negative Stiffness of the Damper on the Performance of the Cable  
Negative stiffness characteristics of semi-active/active control systems have been found and investigated in the area of structural vibration control [39; 40; 41; 42]. The role of negative stiffness in the semi-active control of magnetorheological dampers has been well demonstrated by [PERSON] [43]. According to the research results above, it has been confirmed that the negative stiffness is capable of improving the energy dissipation ability of a conventional damper. In view of such benefits, several passive negative stiffness dampers were also proposed to enhance the vibration control performance of the cable, such as a viscous damper with a negative magnetic stiffness spring [44], an oil damper with two pre-compressed springs [45], and a viscous inertial mass damper [46].  
When a negative stiffness viscous damper is quite close to the cable end, the supplemental modal damping ratio of the stay cable \(\zeta_{i}\) can be expressed as [36; 40]  
\[\zeta_{i}\approx\frac{\eta}{1+\eta^{2}}\frac{a/l}{1+\overline{k}} \tag{9}\]  
where \(\overline{k}\) and \(\eta\) respectively denote the dimensionless negative stiffness and the dimensionless damping coefficient, which are given by  
\[\overline{k}=ka/T,\;\eta=\frac{c}{\sqrt{Tm}}\frac{i\pi a/l}{1+\overline{k}} \tag{10}\]  
where \(a\), \(k\), and \(c\) represent the location, the negative stiffness, and the damping coefficient of the damper, respectively; \(T\), \(m\), \(l\), and \(i\) denote the tension force, the mass per unit length, the length, and the mode order of the cable, respectively.  
When the optimum damping coefficient \(c^{\text{opt}}\) is determined by  
\[c^{\text{opt}}=\frac{T}{a\omega_{i}}(1+\overline{k}), \tag{11}\]  
the maximum modal damping ratio of the cable \(\zeta_{i}^{\text{max}}\) can be obtained as  
\[\zeta_{i}^{\text{max}}=\frac{1}{1+\overline{k}}\frac{a}{2l}. \tag{12}\]  
Equation (12) clearly indicates that the incorporation of negative stiffness into a linear viscous damper can enhance the maximum modal damping ratio of the cable. For the new self-powered MR damper system in this paper, the test results shown in Sections 4.2 and 4.3 have demonstrated that both the self-powered MR damper and the generator are able to exhibit negative stiffness behavior for the higher-order mode of the cable. Additionally, the negative stiffness increases as the mode order of the test cable increases. In summary, the proposed self-powered MR damper control system shows superiority over traditional passive MR dampers or viscous dampers.
# Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use
## 2 SmartBAN Specifications  
### Physical Layer  
#### 2.1.1 Frequency Band  
The SmartBAN PHY consisted of two different types of logical channels operating in the 2.4 GHz unlicensed industrial, scientific and medical (ISM) frequency band. These were the control channel (CCH) and the data channel (DCH). The CCH is utilized only for broadcasting a CCH beacon (C-Beacon) by the hub. On the other hand, bidirectional sensor data and control and management information data between the hub and the nodes are transmitted using the DCH.  
The utilized spectrum was divided into 40 channels, each having a 2 MHz bandwidth. The 40 center frequencies were equally distributed between 2.402 GHz and 2.480 GHz. Three channels, with channel numbers 1, 12 and 39, were assigned for the CCH, and the other 37 channels were reserved for the DCH. One CCH and one DCH were utilized within one SmartBAN, and those were selected by the hub. Different DCHs were used by neighboring SmartBANs for coexistence management. Unlike Bluetooth Low Energy (BLE), channel hopping is not supported in SmartBAN PHY.
# A Feature Extraction Method Using Auditory Nerve Response for Collapsing Coal-Gangue Recognition
## 5 Conclusions  
In this work, a feature extracted method to identify coal and gangue based on the AN response model and SVM was proposed. By using the vibration signal of hydraulic support's tail beam, the features were extracted using auditory spectrum and consisted of each characteristic frequency of the AN response model's output, which fully reflected the difference between coal and gangue. The feature selection method based on variance was employed to choose an effective feature subset to reduce redundant information. Finally, the selected feature subset was inputted into SVM for coal-gangue identification, and recognition results were further compared with traditional methods. The main contributions of our proposed method are stated as follows:  
(1) We proposed an original feature extraction method that integrated the AN response model with auditory spectrum. In this system, the AN response model was applied to process the coal and gangue's acoustic pressure signal to simulate the decision-making judgment of workers' ears during the actual coal mining process. The auditory spectrum, constituted of each characteristic frequency of the AN response model, was used to extract features.  
(2) To verify validity of features extracted by the auditory spectrum, each type of feature's behavior at various frequencies was analyzed, and a feature selection method integrated with variance was developed to decrease the noise and redundant information of the initial feature set based on distribution properties of each feature at different frequencies.  
(3) Several traditional methods and a recent coal-gangue recognition method on the basis of feature selection, described in [PERSON] et al. (2013), were utilized to demonstrate our proposed method's effectiveness. Through a comparison of the recognition results among the features of kurtosis, sum energy of IMFs, energy entropy of wavelet packet coefficient, spectral centroid, mean of MFCC, and MF-score for feature selection, it indicated that the proposed method had outstanding performance. The positive result of this research clearly demonstrated the potential of this proposed approach in coal-gangue identification.  
Future work will focus on the impact characteristics of different coal-gangue mixing ratios. Moreover, we also look forward to conducting more studies on improving the AN response model for practical applications.  
Conceptualization, H.L., H.P., and S.Y.; methodology, H.L., X.C., and S.Y.; software, T.W. and S.W. ([PERSON]); validation, H.P., X.D., and S.W. ([PERSON]); writing, H.P. All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China (grant number 2018 YFC0604503) and the Priority Academic Program Development of Jiangsu Higher Education Institutions funded this work.  
The authors declare no conflict of interest.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 5 Verification of the NOMA Simulator
### Exemplary Simulated Results  
#### 5.1.1 Channel Coding  
Channel coding was used to detect and correct errors in the transmitted data. Redundancy is added to the transmitted signal and then at the receiver side, which are used by the receiver to recover distorted information. The proposed NOMA link-level simulator employs convolutional code for channel coding. These codes have been standardized in LTE; therefore, for our simulator, we also considered them with different coding rates. The performance of uncoded and coded NOMA-based systems was evaluated in terms of BER.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Value** & **Parameters** & **Values** \\ \hline Cells & 7,19 & Thermal noise & \(-\)174 dBm/Hz \\ Antenna Config. 1 Tx, 2 Rx & Intercell interference & One-tier, two-tier & \\ Cell Radius & 500 m & Channel Equalization & FDE-MMSE \\ Intersite distance & 1 km & Modulation scheme & BPSK, QPSK, 16 QAM \\ Interservice interference & None & Power Allocation & \(P_{1}=(0.1\text{--}0.3)\), \(P_{2}=(0.9\text{--}0.7)\) \\ Noise & AWGN & Interference Cancellation & Perfect, Iterative \\ UEs/cell & 2,3 & Synchronization & ideal \\ Multiplexed UEs/Cell & 2,3 & Channel Estimation & Ideal \\ Carrier frequency & 2 GHz & Cyclic Prefix & 20\% \\ System BW & 20 MHz & UE Noise & 9 dB \\ \hline \end{tabular}
\end{table}
Table 1: Link-level and system-level parameters for exemplary results.  
Figure 6: Proposed system model for coded MC-NOMA with linear precoding.  
Figure 7 shows the performance comparison of uncoded NOMA and coded NOMA in the presence of perfect SIC with different code rates, k for both NEUs and FEUs. For a BER of \(10^{-3}\), \(E_{b}/N_{o}\) required for uncoded NOMA and coded NOMA with \(k=\frac{1}{2}\) for NEU is 25 dB and 22.5 dB, respectively. This is because the near user experiences interference at the SIC level, and the absence of redundancy increases the interference by the high-power user. The far user simply subtracts the near user signal as noise from the combined received signal because the power level of the near user signal is at the noise level at the far user's distance. Similarly, for the FEU, \(E_{b}/N_{o}\) required for the uncoded and coded system with \(k=\frac{1}{2}\) is 20 dB and 17 dB, respectively, for a BER of \(10^{-3}\). It shows a 3-dB gain in the case of the FEU as compared to the NEU, where the achieved gain is 2.5 dB. As k is changed from \(\frac{1}{2}\) to \(\frac{1}{4}\), the coded system performs better when \(E_{b}/N_{o}\) is equal to 21.5 dB and 16.5 dB for NEU and FEU, respectively, at a BER of \(10^{-3}\). The performance of coded NOMA is better than that of uncoded NOMA in terms of the BER. This supports the fact that increased redundancy in coded NOMA enhances the performance, but at the cost of increased computational complexity compared to uncoded NOMA.
## 2 Materials and Methods
### Characterization  
The thickness of the thin films was evaluated with a Dektak 8 stylus profiler (stylus diamond tip radius = 12.5 \(\upmu\)m, vertical range = 5 nm-1 mm, stylus tracking force = 0.03-15 mg). The morphology of the prepared thin films was investigated with a JEOL JSM-7400F microscope (JEOL, Tokyo, Japan)with an acceleration voltage of 3 kV-6 kV. In order to test the strength of adhesion of the TiO\({}_{2}\) layer to the stainless steels, the Scotch tape test was carried out, where an adhesive tape was placed on top of the coating. The coating is considered satisfactory and strong if the TiO\({}_{2}\) layer remains intact after the removal of the tapes. The crystalline structure of the TiO\({}_{2}\) thin film deposited on stainless steel was determined by an X-ray diffractometer (Siemens D5000 XRD unit, Aubrey, TX, USA ) in 2\(\theta\) range from 20 to 90\({}^{\circ}\) by 0.07\({}^{\circ}\)\(\cdot\)s\({}^{-1}\) increasing steps operating at 40 kV accelerating voltage and 40 mA current using Cu K\(\alpha\) radiation source with \(\lambda\) = 1.5406 A.
# AUV Adaptive Sampling Methods: A Review
## 3 Target of Interest
### Physical Features
#### 3.1.2 Upwelling Fronts  
A quasi-adaptive sampling method was introduced by [PERSON] et al. [15] in an initiative, to integrate human initiative (human-in-the-loop) and machine initiative. The adaptive sampling process consisted of an initial reconnaissance survey and the collected data (the depth of temperature transition and the water temperature) was sent to human scientists so as to confirm the presence of a front. Then, permission was given to the AUV to allow it to localise the centre of the front through tracking and mapping. As a comparison, [PERSON] et al. [16] proposed a 2D front detection method with no human-in-the-loop to detect and track the front of an upwelling. It was applied to demonstrate detection of a coastal upwelling at a fixed latitude and resulted in a two-dimensional zig-zag search in the horizontal plane crossing the front boundary [6]. Then, the 2D approach was improved to a 4D front detection method [17] so as to perform four-dimensional detection and tracking over time. This adaptive front detection method is discussed further in Section 4.2, which focuses on the discussion of front and boundary determination.  
The virtual body and artificial potential (VBAP) method for adaptive control of a fleet of gliders was presented in the Autonomous Ocean Sampling Network II (AOSN-II) project sponsored by the Office of Naval Research [18; 19; 20]. This method relies on a leading virtual body in the centre to coordinate a group of point masses (vehicles denoted by their centre of gravity). A group of vehicles were designed to adaptively track the front of the ocean phenomena or seek the spot with maximum value (of a parameter) while contracting or expanding the group formation. The cooperative and coordinated control strategy used, in terms of utilising multiple gliders, is discussed further in Section 5.2.
## 1 Introduction  
The horizontal-to-vertical spectral ratio (HVSR) method, also known as H/V, has become a standard and widespread observational method of soil amplification and fundamental frequency estimations due to its effectiveness, environmental friendliness, relative simplicity and low cost [1; 2; 3; 4]. Since the first works [5; 6], many scientific papers have been dedicated to HVSR modeling and inversion problems. However, there is an extreme lack of studies on the application of this method in marine conditions, i.e., using records from ocean-bottom seismographs (OBS).  
In this article, we have tried to develop an understanding of this method as applied to the marine environment. To achieve this objective, we used long-term OBS records obtained in the Laptev Sea, which is one of the most interesting regions from a seismotectonic point of view. This is especially true for the northern part of the Laptev Sea, the area of the outer shelf and the continental slope, where the Gakkel spreading ridge junctions with the shelf structures. In this area, many also assume the existence of a large strike-slip fault zone [7; 8; 9; 10].  
The presence of permafrost is widespread on the shelf of the Laptev Sea [11; 12; 13]. At the same time, in the central part of the outer shelf of the Laptev Sea, there is a zone of sparse permafrost and open talks, which is confirmed by both the modeling results [12] and the data from offshore geophysical surveys [14; 15].  
In the Laptev Sea, a large number of methane outflows from the seabed have been found, especially on the mentioned central part of the outer shelf [16; 17; 18]. Recent studies show a deep thermogenic origin of the escaping gas on the outer shelf of the Laptev Sea [19]. Methane of deep origin can come from great depths along faults and reach the seafloor through wide talks.  
Until now, the application of the HVSR technique for the determination of the thickness of unfrozen sediments above the submarine permafrost has been very limited [20; 21]. Many questions regarding the application of this method in marine shelf conditions, especially in the Arctic, and in particular in the presence of sea ice cover, remain open. Thus, the objective of the paper is to describe the features of applying the HVSR modeling and inversion techniques to seismic records obtained by OBSs on the outer shelf of the Laptev Sea, and to estimate the depth of the main shear wave impedance contrast, which can be associated with the transition from ice-free sediments to ice-bonded permafrost. In this case, the primary task is to describe the features of the recorded ambient seismic noise, which must be taken into account during HVSR modeling and interpretation. The processing of the ambient seismic noise recorded in the Laptev Sea, as well as the wave recorder data and the ERA5 and EUMETSAT reanalysis data, allowed the study of the dependency of seafloor seismic noise on the presence of sea ice cover, as well as sea waves in various frequency bands and weather conditions, wind speed in particular.
# Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks
###### Abstract  
Phase-shifting profilometry (PSP) has been widely used in the measurement of dynamic scenes. However, the object motion will cause a periodical motion-induced error in the phase map, and there is still a challenge to eliminate it. In this paper, we propose a method based on three-stream neural networks to reduce the motion-induced error, while a general dataset establishment method for dynamic scenes is presented to complete three-dimensional (3D) shape measurement in a virtual fringe projection system. The numerous automatically generated data with various motion types is employed to optimize models. Three-step phase-shift fringe patterns captured along a time axis are divided into three groups and processed by trained three-stream neural networks to produce an accurate phase map. The actual experiment's results demonstrate that the proposed method can significantly perform motion-induced error compensation and achieve about 90% improvement compared with the traditional three-step phase-shifting algorithm. Benefiting from the robust learning-based technique and convenient digital simulation, our method does not require empirical parameters or complex data collection, which are promising for high-speed 3D measurement.  
fringe projection profilometry; motion-induced error; deep learning +
Footnote †: journal: Applied  
1
Footnote 1: Copyright** © 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# On-Board Real-Time Ship Detection in HISEA-1 SAR Images Based on CFAR and Lightweight Deep Learning
## 4 Experimental Results and Analysis
### Experimental Results
#### 4.2.3 Results of Ship Detection with Simulated SAR Image
It can be seen that the ship detection system we proposed was robust, with a detection accuracy rate of 88.58%, a false alarm rate of 14.12%, and a missed detection rate of 11.42%, some false alarms and missing ships are shown in Figure 10. Furthermore, the running time was improved and it met the mission requirements for real-time ship detection using the on-board platform of the satellite. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Method** & **Detected Ships** & **TP** & **FP** & **FN** & **Precision** & **Recall** & **OA** & **FA** & **MA** & **Running Time (s)** \\ \hline Proposed method & 262 & 225 & 37 & 29 & 85.9\% & 88.6\% & 77.3\% & 14.1\% & 11.4\% & 67.18s \\ Only YOLOv4-tiny & 274 & 232 & 42 & 22 & 84.7\% & 91.3\% & 78.4\% & 15.3\% & 8.7\% & 156.83s \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The ship detection results of the the simulated image.