esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The study of nonlinear ultrasonics for nondestructive evaluation of engineering materials has predominately focused on the generation of higher harmonics from a monochromatic source and combinational harmonics from the mixing of narrowband waves. Many authors (e.g., [1, 2, 3]) have used second harmonic generation as a sensitive indicator of material degradation. In an effort to minimize the effects of measurement system nonlinearity other authors (e.g., [4, 5, 6]) have mixed waves to create mutual interactions that transfer wave energy to the sum and difference frequencies. Acoustoelasticity is another nonlinear ultrasound phenomenon that has been used to nondestructively determine stress (e.g., [7, 8]). In comparison, broadband Rayleigh waveforms have rarely been used for nondestructive evaluation purposes even though the wave propagation physics are well defined by focused laser ultrasound efforts in the 1990s and 2000s (as reviewed below).  
The objective of this article is to show that the nonlinear evolution of broadband Rayleigh waveforms generated by short-duration laser pulses provide a novel nondestructive material characterization capability that could be useful for in-situ process monitoring during the additive manufacturing of metals. Specifically, the directed energy deposition method of additive manufacturing is addressed here. While the laser ultrasound system would be capable of typical linear measurements, nonlinear measurements sensitive to the material microstructure are the focus. Ultimately, this capability could improve the quality assurance testing of high-value components and provide feedback for process control.  
We start by reviewing literature on laser generated finite amplitude surface waves, after which models that characterize the waveform evolution are described. These models will be crucial for solving the inverse problem for the material parameters based on the waveform evolution, although that is not part of the current work. Then we turn to the application of additive manufacturing (AM) and describe the laser ultrasound system that is integrated into a directed energy deposition additive manufacturing (DED-AM) chamber and the methods that we use. Before presenting DED-AM chamber results we show the waveform evolution with propagation distance for Ti-6 Al-4V. Then selected results for Ti-6 Al-4V and IN-718 depositions are reported for intentionally adjusted DED-AM process parameters.
## 2 Study Area and Datasets Used
### Datasets  
The CMIP5 model data of 12 out of 34 of the CMIP5 model datasets have been used in the analysis. Detailed information on the models used is given in Table 1. The simulation is a historical experiment that gives historical climate data from 1850 to 2005 using historical forcing such as solar variation, volcanic eruption, stratospheric and anthropogenic aerosol emissions, and greenhouse gas concentrations [25].  
Observed precipitation and temperature data are necessary for bias correction purposes. Since observed data has problems such as missing data, an inadequate number of weather stations, and discontinuity, a global reanalysis of weather data has been used. The reanalysis data used in this study are obtained from CFSR (Climate Forecast System Reanalysis), comprehensive global data with high-resolution, coupled atmosphere, ocean, land surface, and sea ice systems, which provide the most accurate estimation of meteorological data [26]. CFSR data are available from 1979 to the present, and comprise gridded weather data with a spatial resolution of 0.35\({}^{\circ}\).  
Figure 1: Location map (**left**) and DEM (**right**) of the Upper Bhima sub-basin.
## 4 The Design of Controller  
The PPC method can quantitatively describe the transient and steady-state performance of the controlled system. Under the framework of PPC, its controller design strictly follows three key steps: prescribed performance constraints, space equivalent mapping and Nonlinear controller design. This section mainly describes how the controller is designed. The above steps of controller design are mainly described in this section. The control block diagram of space robot system is as Figure 2 follows:
## 4 A Distributed Mapping Service
### An Algorithm for Distributed Map Construction
#### 4.1.1 Cluster Head Collects Data From Its Vicinity  
Sensed data is sent to the cluster head in the form of a tuple \((x,v,t)\) where \(x\) is the 2D Cartesian coordinates of the sensing node, \(v\) is the value of the measured parameter(s), and \(t\) is a time-stamp denoting the time at which the data packet was created. If a sensor node is in the initial data collection phase, it computes a local accuracy model, \(\mathrm{T}\), and buffers all its sensors readings. In latter data transmission phases, a sensor node classifies each new reading according to the local accuracy model and then sends its cluster head only significant readings. \(\mathrm{T}\) can be simply a pre-defined threshold that when exceeded it signifies an important change in the state of the monitored condition or it can be a more complex model.  
Though attempting to achieve a desired global objective, sensor nodes interact with each other only within a restricted neighbourhood, defined using the Map Generation service. A sensing node only buffers data packets for neighbouring nodes that have a significant effect on its local map. For instance, [PERSON]'s method defines an arbitrary number of nodes, \(N\), within a particular radius, \(R\), from the node as the set of nodes which has effect on the local map. For more information about how \(N\) and \(R\) are calculated see [18].  
To avoid sending large amounts of data across the network, various cluster heads collect data from their members and create summary data sets as defined by the In-network Processing model. The size of a summary data set is determined according to the accuracy level required by the complete network map. These summary messages are then transmitted to the sink.
## 3 Numerical Results
### Linear Electron Plasma Wave Damping  
In this section, the effect of collisions on linear damping of spatially one-dimensional electron plasma wave (EPW) is simulated based on Boltzmann equation with electron-ion collision operator. The complete Boltzmann equation is written as [57]  
Figure 12: Density distribution of the Gaussian beam at different non dimensional time steps in non dimensional physical space for \(2.5\times 10^{15}\) particle density.  
\[\frac{\partial f}{\partial t}+\mathbf{u}\cdot\frac{\partial f}{\partial\mathbf{x}}+\mathbf{a} \cdot\frac{\partial f}{\partial\mathbf{u}}=-C_{ei}f, \tag{45}\]  
where the collision operator \(-C_{ei}f\) is given by  
\[C_{ei}f=-\
u_{ei,th}|\mathbf{u}_{th}|^{3}\frac{\partial\left(\mathbf{P}\right)}{ \partial\mathbf{u}}\cdot\frac{\partial f}{\partial\mathbf{u}}, \tag{46}\]  
where \(\
u_{ei,th}\) denotes the thermal electron-ion collision frequency and \(\mathbf{u}_{th}\) is velocity of particle thermal motion. Tensor \(\mathbf{P}\) is defined by  
\[\mathbf{P}=\frac{1}{\mathbf{u}^{3}}\left(\mathbf{u}^{2}\mathbf{I}-\mathbf{u}:\mathbf{u}\right). \tag{47}\]  
In the EPW case, the wavenumber is set to be 0.3. In the low perturbation amplitude regime, the initial electron distribution is  
\[f=\rho\left(\frac{\lambda}{\pi}\right)e^{-\lambda\left(\mathbf{u}^{2}\right)} \left(1+\alpha\cos\left(kx\right)\right), \tag{48}\]  
where \(\alpha=0.0001\) and \(k=\frac{\pi}{250}\). The 1D computational domain is discretized with 64 grid points. In microscopic velocity space, 128 discrete points are used. To compared with the result of Reference [57], the thermal electron-ion collision frequency \(\
u_{ei,th}\) is set to be 0.05. The amplitude of the perturbation's electric field E is plotted on a log scale as a function of time in Figure 13.  
Where \(\omega_{pe}\) is the circular frequency of initial perturbation. The result obtained from present methods is consistent with Reference [57]. The decaying process fit well with exponential decaying line. Because of the initial perturbation of particle distribution, the electric field is also damped in oscillatory process. In traditional numerical method based on continuous assumption, it is very hard to capture these phenomena for the lack of direct modelling of particle motion. For the particle-based method, the huge number of particles always produces unacceptable computational cost. In UGKS, particle motion is described in modelling of flux through cell interface under discrete microscopic velocity space. For every discrete distribution function, computational cost only comes from reconstruction in flux evaluation. However, its accuracy is the same as particle-based method since all particles are considered based on statistical concept using distribution function. With the suitable collision model, a different state of plasma can be accurately obtained.  
Figure 13: Amplitude of the perturbation’s electric field for particle density perturbation \(\Delta N/N=10^{-4}\).
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 6 Future Monitoring of the Perth Basin
Whilst the method is transferable to other settings, in practice, the results may underestimate the time required to accurately measure multi-year deformation rates, as we have not accounted for seasonal and other non-linear effects. Studies of annually varying signals in GPS time series suggest that 2-3 years of data are required to characterise and remove the seasonal component and avoid biasing the long-term velocity estimate ([PERSON], 2017; [PERSON], 2018). Such non-linear seasonal signals are better characterised with acquisitions at higher temporal resolution, as shown by the time series in Figure 6, and would therefore be best measured at the full repeat of Sentinel-1A and -1B. Identifying and measuring small, non-linear, seasonal and broad-scale subsidence/uplift such as that in the Perth Basin is also complicated by the shape of the deformation signals, which are not necessarily spatially distinguishable from atmospheric phase features (Stein  
associated with volcanic inflation [84], do not necessarily occur in a predictable way (e.g., patchy subsidence: [54; 77]) (Figure 4). The results of this study demonstrate that, even over the short 0.7-y observation period, Sentinel-1A acquisitions over the Perth Basin have provided improved information about the larger spatial extent of deformation. However, the results in Table 1 show the benefit of the newly-implemented, higher-frequency Sentinel-1B acquisition strategy, which is planned to continue for longer than the duration of the Sentinel-1A dataset and the required detection periods calculated in Table 1[26]. With this longer time series of data, we will be better equipped to robustly measure subsidence throughout the Perth Basin, firstly confirming whether, in addition to Hillarys, the areas south of Fremantle and in the Swan Valley are undergoing broad-scale, long-term subsidence and, secondly, differentiating between seasonal and long-term displacements in order to constrain the elastic and inelastic storage properties of the regional aquifers [2].
## 6 Systems Encapsulation and Installation
* _RF antennae friendliness:_ the RF antennae are installed within the tube and they are fully protected from the threats of the external environment. Additionally, the ability of using all the internal space of a tube facilitates the encapsulation of very long RF antennae, so it is easy to incorporate antennae from \(\lambda/4\) up to \(\lambda\) (\(\lambda\) is the wavelength of a radio signal, expressed in units of meters). Figure 29: **(a)** A SensoTube-based WSAN node installed in an orchard. All the electronics and the RF antenna of the node have been enclosed within the tube. The solar panel has been adapted at the top cap of the tube (**b**) in such way so as to be easily added or removed. For example, the wavelength of a 433 MHz RF signal is around 69 cm. In this particular case, the ideal antenna should be a 69 cm long wire. In general, antennae close to the wavelength of the incorporated RF signal can benefit the RF signals propagation performance, and they allow for low-cost and low energy antenna driving circuits. * _Zero RF signal attenuation:_ the PVC material doesn't block the propagation of radio signals. A typical example is the use of PVC-based constructions to hide the antennae of the cellular networks on the terrace of the block of flats at the cities. * _Neat cabling:_ all the cables of the WSAN system can be tidily routed along the inner side of the tube. Hence, the cabling is protected from the environmental influences. * _Greater energy storage units performance:_ The temperature and solar radiation at the bottom part of the tube, which is buried underground, permits batteries and several other alternative energy storage units to maintain their nominal efficiency, capacity and lifetime. * _Farm machine friendliness:_ tubes do not require sharing and cableways for their support. Thus, they allow the unrestricted movement of the various machines and equipment used in farm management.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 6 Conclusions
This research was funded in part by Natural Science Foundation of Anhui Province, China, grant number 2108085 QD176, by National Natural Science Foundation of China, grant number 42104019, 42171141, 42174022, by the open research fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote sensing, Wuhan University, grant number 20P04, by the Fundamental Research Funds for the Central Universities of China, grant number JZ2020 HGTA0087, by the Key Laboratory of Geospace Environment and Geodesy, Ministry of Education, Wuhan University, grant number 19-01-03, by the Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, grant number DLLJ202001. Not applicable. The spaceborne GNSS-R data from TDS-1 mission is available at www.merbys.co.uk. The sea ice type (SIT) and sea ice concentration (SIC) products are downloaded from the EUMETSAT OSISAF data portal on 18 February 2021 ([[http://osisaf.met.no/](http://osisaf.met.no/)]([http://osisaf.met.no/](http://osisaf.met.no/)), accessed on 11 September 2021). The authors would like to thank the TechDemoSat-1 team at Surrey Satellite Technology Ltd. (SSTL) for providing the spaceborne GNSS-R data. Our gratitude also to Ocean and Sea Ice Satellite Application Facility for the sea ice edge product used in comparisons. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
CDV  
Crystal Delay Waveform  
CYGNSS  
Cyclone Global Navigation Satellite System  
DDM  
Delay-Doppler Map  
DDWA  
Delay-Doppler Map Average  
DDW  
Differential Delay Waveform  
EIRP  
Effective Isotropic Radiated Power  
EUMETSAT  
European Organization for the Exploitation of Meteorological Satellites  
FYI  
First-Year Ice  
GNSS  
Global Navigation Satellite System  
GNSS-R  
Global Navigation Satellite System Reflectometry  
Figure 16: **RF** for OW-sea ice classification using the data in November as the training set. The data in (**a**) February, (**b**) April, (**c**) November, and (**d**) December are used as the test set in turn. Figure 17: **RF** for OW-sea ice classification using the data in December as the training set. The data in (**a**) February, (**b**) March, (**c**) April, and (**d**) November are used as the test set in turn. Figure 18: **RF** for OW-sea ice classification using the data in February as the training set. The data in (**a**) February, (**b**) March, (**c**) April, and (**d**) December are used as the test set in turn.
## 6 Experiments
### Datasets
#### 6.2.3 Cicidos2017 Dataset  
The CICIDS2017 [34] dataset was created by the Canadian Institute for Cybersecurity at the University of New Brunswick, Canada. The dataset was created by capturing network traffic for 5 days and then labeling the attack and normal data using CICFlowMeter tool based on timestamps, source and destination IPs, source and destination ports, protocols, and attacks. The attacks include brute-force FTP, brute-force SSH, DoS, heartbleed, web attack, infiltration, botnet and DDoS. In the preprocessing phase, we retained data instances related to only the DDoS attacks and filtered out all the other attacks.  
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Training**} & \multicolumn{3}{c}{**Testing**} & \multicolumn{3}{c}{**Verification**} \\ \cline{3-10}  & & **Total** & **Benign** & **DDoS** & **Total** & **Benign** & **DDoS** & **Total** & **Benign** & **DDoS** \\ \hline \multirow{3}{*}{NSL-KDD} & Sup & 113,268 & 67,341 & 45,927 & 16,164 & 9608 & 6556 & 1000 & 900 & 100 \\  & & & (59.45\%) & (40.55\%) & & (59.44\%) & (40.56\%) & & & \\ \cline{2-10}  & Unsup & 68,014 & 67,341 & 673 & & & & & & \\  & & & (99\%) & (1\%) & & & & & & \\ \hline \multirow{3}{*}{UNSW-NB15} & Sup & 25,265 & 13,000 & 12,265 & 12,090 & 8000 & 4090 & 1000 & 900 & 100 \\  & & & (51.4\%) & (48.6\%) & & (66.17\%) & (33.83\%) & & & \\ \cline{2-10}  & Unsup & 23,230 & 23,000 & 230 & & & & & & \\  & & & (99\%) & (1\%) & & & & & & \\ \hline \multirow{3}{*}{CICIDS2017} & Sup & 641,974 & 375,101 & 265,873 & 179,549 & 125,728 & 53,821 & 1000 & 900 & 100 \\  & & & (58.59\%) & (41.41\%) & & (70\%) & (30\%) & & & \\ \cline{2-10}  & Unsup & 379,861 & 376,101 & 3760 & & & & & \\ \cline{1-1}  & & & (99\%) & (1\%) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data instances within datasets used for experimentation.
## 3 Testing Results
### Dynamic Parameter Analysis  
In Figure 5, dynamic elastic modulus (E\({}_{\text{d}}\)) and damping ratio (D\({}_{\text{r}}\)) are utilized to characterize the dynamic properties of the cyclic stress hysteresis curve for the marble samples across every fatigue loading stage [24; 25]. These parameters offer insights into how the material behaves dynamically under cyclic loading conditions, providing information on its stiffness (E\({}_{\text{d}}\)) and energy-dissipation capability (D\({}_{\text{r}}\)). The definition of these two parameters can be found in the literature ([PERSON] et al. [26]). It can be seen that D\({}_{\text{r}}\) displays different patterns for the tested marble samples, and the curve shape is affected by RBL (Figure 6a-d). This result indicates that accumulative damage and crack propagation behaviors are influenced by rock bridge length; moreover, the structural deterioration during fatigue loading is influenced by rock bridge length. For the sample with an RBL of 10 mm, the Dr shows a pattern of first increasing then decreasing, and then increasing again. For the sample with an RBL of 20 mm, it first decreases and then increases until failure. For the sample with an RBL of 30 mm, it increases slowly at the first several CLSs and then increases quickly until failure. For the sample with an RBL of 40 mm, it first decreases and then increases until failure. The differential change trend of D\({}_{\text{r}}\) reflects the fracture mechanism, and it is influenced by the rock bridge between the two fissures. In the fatigue loading stage, D\({}_{\text{r}}\) presents a fluctuation trend, indicating the propagation and interlocking of cracks.  
Concerning the evolution of E\({}_{\text{d}}\), Figure 7a-d present a decreasing trend with CLS, indicating that increasing load cycles leads to a decrease in E\({}_{\text{d}}\). However, E\({}_{\text{d}}\) first presents an increasing and then deceasing trend at different CLSs. The analysis of dynamic elastic modulus (E\({}_{\text{d}}\)) confirms the earlier findings from the cyclic stress-strain curves, demonstrating that greater plastic strain leads to a larger hysteresis loop and a reduced dynamic elastic modulus (E\({}_{\text{d}}\)). However, Ed exhibits a sharp decline towards sample failure across all tested samples.  
Figure 4: Axial stress versus strain for marble specimens subjected to cyclic loading. **(a–d)** Rock bridge lengths are 10 mm, 20 mm, 30 mm, and 40 mm, respectively. The numbers in the figures indicate the cyclic loading stage.  
Figure 5: \(Cont.\)  
Figure 5: Description of maximum axial, radial, and volumetric strain with cycle number at different cyclic loading stages. ((**a–d**): The maximum axial strain versus cycle number; (**e–f**): The maximum radial strain versus cycle number; (**g–l**): The maximum volumetric strain versus cycle number).  
Figure 6: _Cont_.
## 2 Mission Concept
### Mission Analysis  
Light-1 lifetime mission requirement is a six-month operation. Since the payload requires a low altitude as an area of interest for scientific purposes, Light-1 will be deployed from the International Space Station (ISS) at an altitude of 400 km and an inclination angle of 51.6\({}^{\circ}\). Lifetime and access time analysis were conducted using the System Tool Kit (STK) to ensure Light-1 fulfils the mission requirements. The process is outlined below, along with the results:
# Performance Analysis of Existing ITS Technologies: Evaluation and Coexistence
## 2 Fundamentals and Related Work
### Its-G5 MAC/PHY Layer  
The current IEEE 802.11p was standardized in 2009 as an extension of IEEE 802.11a (Wi-Fi standard), which is expected to support V2X communications due to outside the context of BSS (OCB) mode. This latter adopts OFDM at the PHY layer and the carrier sense multiple access protocols with collision avoidance (CSMA/CA) protocol at the MAC layer to manage the access to the communication medium. The OFDM PHY divides the available frequency spectrum 10 MHz used for the vehicular environment at 5.9 GHz into narrower subchannels (156.25 kHz). Thus, the high-rate data stream is split into various lower-rate data streams transmitted simultaneously over several subcarriers, where each subcarrier is narrow-banded. There are 52 subcarriers, where 48 are used for data, and 4 are pilot carriers. The OFDM PHY layer supports eight different transfer rates, achieved using various modulation schemes and coding (MCS) rates, as shown in Table 1. All transmission is mandatorily transmitted with a coding rate of 1/2, which corresponds to a transfer rate of 3 Mbit/s, 6 Mbit/s and 12 Mbit/s [3]. Therefore, the duration of one OFDM symbol is 8 us, and in each symbol, different data bits can be carried, depending on the modulation scheme and coding. Therefore, the packet duration depends on the selected transfer rate and the packet's length. For example, a message duration of 350 bytes (without overhead) with MCS 2 requires 0.466 ms to be transmitted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**MCS**} & **Transfer Rate [Mbit/s]** & **Modulation Scheme** & **Coding Rate** & \
\begin{tabular}{c} **Data Bits per OFDM** \\ **Symbol** \\ \end{tabular} &
\begin{tabular}{c} **Coded Bits** \\ **per OFDM** \\ **Symbol** \\ \end{tabular} \\ \hline MCS0 & 3 & BPSK & 1/2 & 24 & 48 \\ \hline MCS1 & 4.5 & BPSK & 3/4 & 36 & 48 \\ \hline MCS2 & 6 & QPSK & 1/2 & 48 & 96 \\ \hline MCS3 & 9 & QPSK & 3/4 & 72 & 96 \\ \hline MCS4 & 12 & 16-QAM & 1/2 & 96 & 192 \\ \hline MCS5 & 18 & 16-QAM & 3/4 & 144 & 192 \\ \hline MCS6 & 24 & 64-QAM & 1/2 & 192 & 288 \\ \hline MCS7 & 27 & 64-QAM & 3/4 & 216 & 288 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transfer rates, modulation schemes and coding rates in 802.11p [3].  
The algorithm deployed by 802.11p to allow transmission at the MAC layer is called enhanced distributed coordination access (EDCA). This one is based on the primary distributed coordination function (DCF), a CSMA/CA algorithm and added QoS attributes. ITS-G5 is an asynchronous ad hoc protocol that starts listening to the channel before transmission for a predetermined listening period and transmits directly if the channel is perceived as idle. Otherwise, the station will perform a back-off procedure that defers its access to a randomized period. The EDCA mechanism defines four access categories (ACs) that provide support for the delivery of traffic.