esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Forward Modeling Method
### Underground Electromagnetic Field Simulation Theory  
The borehole-to-surface electromagnetic method (frequency domain) is analogous to the frequency-domain controlled-source electromagnetic method (CSEM). According to [PERSON]'s law and [PERSON]'s law, the electromagnetic fields in CSEM are governed by the quasi-static Maxwell equations, with displacement currents neglected [3; 7]. The quasi-static Maxwell equations are expressed as  
\[\
abla\times\mathbf{E}=-i\omega\mu\mathbf{H}, \tag{1}\]  
\[\
abla\times\mathbf{H}=\sigma\mathbf{E}+\mathbf{J_{s}}, \tag{2}\]  
\[\
abla\cdot\mathbf{H}=0, \tag{3}\]  
\[\
abla\cdot\mathbf{E}=0, \tag{4}\]  
where \(\mathbf{E}\) is the electric field strength, with unit V/m; \(\mathbf{H}\) is the magnetic field strength, with unit A/m; \(\omega\) is the angular frequency, with unit rad/s; \(\mathbf{J_{s}}\) is the current density of the externally applied field source, with unit A/m\({}^{2}\); \(\mu\) is the magnetic permeability, which can be expressed as \(\mu=\mu_{r}\mu_{0}\), where \(\mu_{r}\) is the relative magnetic permeability and \(\mu_{0}\) is the magnetic permeability of free space; \(\sigma\) is the electrical conductivity, with unit S/m.  
Since the magnetic flux density \(\mathbf{B}=\mu\mathbf{H}\), Equations (1) and (2) can also be written as  
\[\
abla\times\mathbf{E}=-i\omega\mathbf{B}, \tag{5}\]  
\[\
abla\times\mu_{0}{}^{-1}\mathbf{B}=\mathbf{J_{c}}+\mathbf{J_{s}}, \tag{6}\]  
In Equations (5) and (6), the unit of the magnetic flux density \(\mathbf{B}\) is \(T\). In Equation (6), the conductive current \(\mathbf{J_{c}}\) obeys [PERSON]'s law:  
\[\mathbf{J_{c}}=\sigma\mathbf{E}, \tag{7}\]  
Figure 2: The borehole-to-surface electromagnetic imaging observation system.  
Eliminating the magnetic flux density \(B\) from Equations (5) and (6) yields a second-order vector Helmholtz equation for the electric field \(E\):  
\[\
abla\times{\mu_{0}}^{-1}\
abla\times{\mathbf{E}}+i\omega\sigma{\mathbf{E}}=-i\omega{ \mathbf{J}}_{s}, \tag{8}\]  
The magnetic field \({\mathbf{H}}\) can be calculated using the following system of equations:  
\[{\mathbf{H}}=-\frac{1}{i\omega\mu_{0}}\
abla\times{\mathbf{E}}\, \tag{9}\]  
Considering the low-frequency emission characteristics, the boundary conditions of the entire system are ideal conductor boundaries: \({\mathbf{n}}\times{\mathbf{E}}=0\), \({\mathbf{n}}\cdot{\mathbf{H}}=0\), where \({\mathbf{n}}\) is the outward unit normal vector of the computational domain boundary.  
Equation (8) can also be written as  
\[\
abla\times\
abla\times{\mathbf{E}}+i\omega\mu_{0}\sigma{\mathbf{E}}=-i\omega\mu_{0} \mathbf{J_{s}}. \tag{10}\]
## 4 Performance Analysis of Islanding Detection Methods
### Quality Factor  
The quality factor is a very important criterion that assesses the effectiveness and performance of IDMs. This factor is defined as \(\pi\) times the proportion of the highest stored energy to dissipated energy per cycle at a specific frequency. The relationship between the quality factor and the non-detected zone is proportional and it is necessary to keep it within the allowed ranges as standards are set.
# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## Materials and Methods
### Materials  
To enable comprehensive inundation mapping across Bangladesh, approximately 11 frames of Sentinel-1 C-band interferometric wide swath (IW) frames with a 250 km swath width were required (Figure 2).  
Figure 2: Sentinel-1 composites VH (red), VH (green), and VH/VV backscatter ratio (blue) images used for inundation mapping of (**a**) March, (**b**) April, (**c**) June, and (**d**) August 2017.  
For the pre-flood (March) and post-flood (April, June, and August) inundation mapping, a total of 44 dual-polarization Sentinel-1 level-1 Ground Range Detected (GRD) products were used. The GRD products consist of focused SAR data that was detected, multi-looked, and projected to the ground range using the WGS-84 Earth ellipsoid model. The ellipsoid projection of the GRD products was corrected using the terrain height specified in the product's general annotation. Both the like-polarized (vertical transmit and vertical receive (VV)) and cross-polarized (vertical transmit and horizontal receive (VH)) channels were used in this study, and data were retrieved as Level-1 GRD products. Sentinel-1 SAR images were useful as the data were freely available within 3 h of acquisition for near real-time (NRT) emergency response and within 24 h for systematically archived data. Table S1 shows the Sentinel-1 images that were used in this study, which were freely downloaded from the Copernicus open access hub data portal of the European Space Agency (ESA).  
In addition to SAR and as part of the flood damage assessment, pre-flood cloud-free Landsat-8 image collections between 1 January and 30 June 2017 were used for land use/land cover mapping in the Google Earth Engine. To support land cover classification, additional ancillary data used in this study included a 30-m resolution shuttle radar topography mission (SRTM) digital elevation models (DEM) [49], which were retrieved from the United States Geological Survey (USGS) archived data portal, as well as road network information from OpenStreetMap [50] and administrative boundary data from the database of Global Administrative Areas (GADM) [51].
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 7 Conclusions  
In this paper, we propose a blockchain-based cooperative authentication mechanism for smart grid devices from different domains. The mechanism enables mutual authentication with security and we have performed a security analysis of the proposed mechanism to demonstrate its ability to meet security requirements. We have verified the mechanism through simulation experiments and the results show that it is efficient in terms of storage, communication, and authentication. The storage overhead is 520 bits, the communication overhead is 1169 bits for initial authentication, the query latency is 51 ms, and the write latency is 126 ms. This is perfectly suited to the scenario of mutual authentication between smart grid devices.  
However, the consensus algorithm used in this paper is the Raft algorithm and, although it can meet the demand for device authentication in smart grids, the efficiency of the Raft algorithm may not be able to meet the requirements of low latency and high throughput as the scale of smart grids continues to expand and application scenarios continue to increase. Therefore, further improvements to the consensus algorithm are needed in future work to improve efficiency in order to better meet the needs of smart grids.  
Conceptualization, Z.W.; methodology, D.Z.; software, Z.W.; validation, Z.W., Y.L., and D.Z.; formal analysis, Z.W.; investigation, D.Z. and G.L.; resources, Z.W. and G.L.; data curation, Z.W. and G.L.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W.; visualization, Z.W. and G.L.; supervision, Z.W.; project administration, Z.W.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 5: Time costs on writing and querying data.
## 4 Experiments
### Data Pre-Processing  
The primary goal of NIDs is to identify attack traffic. As a result, we start by filtering the two datasets, independently choosing the data samples that are marked as normal, and classifying the rest as DoS attacks. One-hot encoding of the categorical features and normalization of the numerical features are typically used to handle the pre-processing of the datasets. However, as previously mentioned, the NSL-KDD dataset contains a more precise amount of data for each attack category. Conversely, the UNSW-NB15 dataset contains a remarkably small number of records for categories such as fuzzers and worms. To address this problem, the training set employs the oversampling technique to ensure that each attack type contains the same number of records. We employ one-hot encoding and normalization for both datasets. Both datasets contain categorical features, which the deep learning model needs to translate into numerical values to produce accurate prediction results. Therefore, at the pre-processing stage, these columns were transformed into numerical values using the pandas Python Library's get dummies function. Because label encoders generate numerous numbers in a single column, the model may interpret these values incorrectly as being in a specific sequence, which could affect the classification. For this reason, one-hot encoding is preferred over label encoders.  
Normalization is the process of rescaling the data into a specific range to minimize redundancy and speed up the model's training. This study also employs the use of min-max normalization [44], which rescales the data range to [0, 1].  
\[X[i]=\frac{X[i]-X_{min}}{X_{max}-X_{min}} \tag{1}\]
## 5 Conclusions  
The combined structure of FSO and optical network using a 5G based F-OFDM transceiver is presented in this paper. It is discussed that fiber nonlinearities, FSO pointing errors, and co-channel interference have limited the optical network and FSO performance. The given model is evaluated theoretically in terms of CDF, outage probability, PAPR, and BER. The simulation results were investigated using input power, output power, FSO transmission range, divergence losses, and CDF. The significant performance of the proposed FSO model was recorded in comparison with current approaches. The encouraged BER is received against fiber nonlinearities, FSO pointing errors, and co-channel interference. In the future, we can further develop the achievements of the presented model by applying machine learning procedures.  
Conceptualization, M.K. and F.A.; methodology, F.M.; software, Y.K.; validation, S.A., N.U. and A.A.; formal analysis, M.K.; investigation, F.A.; resources, J.K.; data curation, F.A.; writing--original draft preparation, M.K.; writing--review and editing, F.A.; visualization, F.M.; supervision, J.K.; project administration, F.A.; funding acquisition, N.U. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The comparison of F-OFDM based 5G transceiver with OFDM and UFMC in terms of PAPR and CDF.  
Figure 9: Eye diagram representation for different optical beams: (**a**) single beam, (**b**) double optical beams, (**c**) triple optical beams, and (**d**) four optical beams.  
**Funding:** Authors would like to thank Taif University Researchers for Supporting Project Number (TURSP-2020/228), Taif University, Taif, Saudi Arabia.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** Authors would like to thank Taif University Researchers for Supporting Project Number (TURSP-2020/228), Taif University, Taif, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Marine Environment Monitoring Network Model
### Channel Correlation of Underwater Nodes  
In underwater acoustic communication, channel noise is very complicated; for instance, sea tides, wind waves, and ship activities will produce noise. In this paper, the noise of the underwater acoustic channel is uniformly represented as \(\sigma^{2}\). At the same time, the attenuation of underwater acoustic channels is severe. If the absorption coefficient of the ocean is \(a(f)\), the transmitting frequency is \(f\), the distance of each user node is \(d(km)\), and the underwater acoustic propagation coefficient is \(\epsilon\). Then, the attenuation formula of the underwater acoustic channel can be expressed as follows:  
\[A(d,f)=d^{\epsilon}[a_{0}(f)]^{d} \tag{1}\]  
where the variable d km represents the distance between any two nodes, \(\epsilon\) represents the underwater acoustic propagation coefficient, and the ocean absorption coefficient \(a_{0}(f)\) can be expressed as [20]:  
\[a_{0}(f)=10^{0.0111\frac{f^{2}}{1+f^{2}}+4.4}\frac{f^{2}}{4i00+f^{2}}+2.75 \times 10^{-5}f^{2}+3\times 10^{-4} \tag{2}\]  
Suppose that the channel set of nodes i is \(h_{i}=[h_{i1},h_{i2},\ldots h_{iL}]\), and L corresponds to the underwater path L in the channel response between user i and the CH node. Then, the channel response auto-correlation matrix of user i is \(R_{ii}=E\{h_{i}h_{i}^{H}\}\). By decomposing the characteristic of the moment, \(R_{ii}\) can be further decomposed into  
\[R_{ii}V_{i}=SV_{i} \tag{3}\]  
If the sub-correlation matrix contains all paths, then the eigenvector V can represent the vector containing single path information [21], the eigenvalue S in the above formula is taken as the weight coefficient of V, and the multi-path information of node i can be expressed as follows:  
\[R_{i}=\sum_{i=1}^{L}S_{iL}V_{iL} \tag{4}\]Then, the channel correlation between user node i and node j can be expressed as follows:  
\[\text{E}_{\text{i,j}}=\frac{E\big{\{}R_{i}^{H}R_{j}\big{\}}}{E\big{\{}R_{i}^{H}R_{ i}\big{\}}\|E\Big{\{}R_{j}^{H}R_{j}\Big{\}}} \tag{5}\]
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
## 3 Future Challenges  
Large constellations will require a paradigm shift with respect to the way space missions are currently handled, with major challenges involving technical, management and regulatory aspects. The main ones are discussed hereafter by analyzing the work that has been done so far and outlining possible future developments.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 4 Experimental Results and Analyses
### Experiments with Trial Data  
Simulation results showed the validity of the proposed method. Then, the feasibility and practicality of the proposed algorithm in engineering was verified by trial data.  
The trial data was derived from near the Airport Road. The INS/GNSS integration system was composed of a laser inertial navigation system (LINS) and GNSS. The accuracy of the LINS is 1.5 n mile/h. The output rate is 400 HZ. The position and velocity accuracy of GNSS are 10 m and 5 m/s, respectively. The output rate is 1 HZ. The true value of the navigation parameters are given by the PHINS, which was provided by the IXSEA company. The experimental platform is shown in Figure 13.  
As the radio signals fall right into the navigation signal pass band or there is multipath interference caused by buildings, the noise of the GNSS navigation parameters is time-varying. The time-varying interference noise is shown in Figure 3.  
The BBVIF anti-jamming algorithm with Gaussian distribution was compared with the VBAKF algorithm with Inv-Gamma or Inv-Wishart distribution by using the trial data. The RMSEs of the velocity and position are shown in Figures 14 and 15 and Table 4. Furthermore, driving trajectories of the anti-jamming algorithms described above and the reference value provided by PHINS are shown in Figure 16. To see the driving trajectories more clearly, we have magnified a corner of the trajectories, and this is shown in the bottom right corner of Figure 16.  
The results based on the trial data show that all three methods can complete the task of anti-jamming, which is the same as the simulations. The accuracy of the navigation parameters from the BBVIF with Gaussian distribution is higher than the VBAKF with Inv-Gamma or Inv-Wishart distribution. The velocity and position accuracy of the proposed method is approximately 3 times higher than the existing VBAKF method. It is because of the time-varying noise is more similar to Gaussian. Therefore, the BBVIF with Gaussian distribution estimate is more accurate than the other two algorithms. This experimental result proved that the proposed algorithm is feasible and practical in engineering applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Gaussian** & **Inv-Gamma** & **Inv-Wishart** \\ \hline Ve (m/s) & 0.834 & 2.601 & 2.144 \\ Vn (m/s) & 0.603 & 2.268 & 1.789 \\ Pe (m) & 1.239 & 3.887 & 3.502 \\ Pn (m) & 1.089 & 3.592 & 3.342 \\ \hline \hline \end{tabular}
\end{table}
Table 4: RMSEs of velocity and position using the trial data.  
Figure 16: Driving trajectories near the Airport Road.  
Figure 15: RMSEs of the position using the trial data.
## 2 HSI Procedure
### Preprocessing
#### 2.2.3 Savitzky-Golay (SG)  
The Savitzky-Golay filter is a powerful noise-reducing and signal-smoothing tool in HSI. This digital filter applies local polynomial regression to smooth data while preserving important features [49]. SG is a finite impulse response (FIR) filter, and since it is a zero-phase filter, it can also keep the fundamental information of the signals unaltered [50]. By adjusting the polynomial order and filter length, SG can compute up to the fifth derivative, ensuring optimality conditions are met for accurate results [51]. SG is also known for its ability to smooth signals while preserving important high-frequency components, unlike standard FIR averaging filters, which tend to eliminate both noise and high-frequency content. SG filters achieve this by fitting a polynomial to the data points and minimizing the least-squares error within the window, which leads to better preservation of signal details compared to traditional FIR filters that might blur or remove critical frequency [52]. By enhancing the metrics of SNR and MSE, the spectral data clarity is increased by SG, which helps in the finer analysis of raw data. In fact, the effectiveness of this approach depends upon the optimization of parameters such as polynomial order and window length. Some studies reported that certain combinations of these parameters result in optimal performance in classification. There are, however, certain limitations of SG, and hence some alternative methods, including those coming under the category of FFT, are also explored for spectrum processing [53; 54; 55].  
Smoothing is widely used in conjunction with other preprocessing methods to refine spectral data by removing noise at a small scale and enhancing clarity [9]. It is very effective in high-noise conditions, as it emphasizes the shape rather than the height of the spectrum. However, careful attention should be given to the choice of window size, because if the window size is too small, noise can increase, while if it is too large, significant features may be masked [56]. When combined with SG, it can improve the clarity of spectral features, and enable easier identification and analysis [41].  
SG is generally used in spectroscopic applications where the attenuation of high-frequency noises is intrinsic. The application of SG is quite common as a preliminary step toward improving spectral resolution and eliminating baseline variations in commonly applied spectral derivation methods [9].  
When a fractional-order SG filter was used along with enhancing water content prediction in corn leaves, results were optimal when combined with SG and wavelength selection methods [57]. SG convolution smoothing was also applied to HSI for the accurate evaluation of chicken flesh composition [58]. Additionally, the importance of SG when performing predictions on egg freshness was demonstrated, especially when integrated with advanced techniques for wavelength selection [47]. Since SG was impactful in predicting solid food quality parameters, it is recommended to investigate its potential on the measurement of liquid quality parameters.
# Power Control Techniques for Interference Management-
### Current Review Limitations  
The existing reviews have limitations, as illustrated in Section 1.1. Recent trends show an exponential increase in published articles, including review articles, in general [20]. Literature reviews are broadly classified as either \"narrative\" or \"systematic\" [21; 22]. Literature reviews on power control have presented constitute narrative reviews. The authors could not identify reviews that addressed interference management. Furthermore, we identified two review papers: [PERSON] and [PERSON] [19] and [PERSON] et al. [20]. The authors in [19] presented a systematic review methodology for evaluating power control issues in wireless networks of the work published from 1992 to 2011. Reference [20] used a
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 1 Introduction
Possible attacks on the application layer attempt to overwhelm the network with a large number of sensor stimuli [20] or to inject spurious or replayed packets into the network at leaf nodes [21]. The goal of these attacks is to provoke increased traffic and starve network resources. Again, data authentication can protect the network from those attacks. Table 1 presents the security attacks and countermeasures in WSNs at different communication layers. There are numerous solutions trying to fight the security issues in wireless sensor networks. Software-based solutions try to establish new or try to optimize existing security algorithms for the applications in WSNs. However, software solutions have often to deal with insufficient processing resources and lack of memory [22; 23; 24]. Hardware-based solutions rely on the application of dedicated hardware accelerators, which enable fast execution of complex crypto-operations. Although additional hardware increases the chip area and power dissipation, it has been shown that hardware accelerators for crypto-operations (crypto-processors) consume less energy per bit than the standard software-based implementations [25; 26; 27]. However, the additional chip logic increases the microcontroller's chip area and static power dissipation during sleep mode. The latter can be reduced by employing the power-gating [28]. This paper introduces a new hardware solution which implements advanced crypto-algorithms in a sensor node microcontroller at a low energy cost. It combines dedicated hardware crypto-accelerators and a low power 16-bit processor core, which asynchronously communicate with peripheral components. The next sections discuss and justify (comparing to the exiting solutions) the proposed solution (Section 2), present the architecture of the node microcontroller (Section 3), and describe the implementation and verification details (Section 4). Finally, the conclusions are drawn in Section 5.