esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### Tidal Flat DEM Construction  
In this study, the satellite images were taken as inputs. A method of image preprocessing was employed including image filtering, enhancement, geometric correction and segmentation operations. The binary images of tidal flats were extracted and the vector waterlines were constructed from the processed images. It is worth noting that, waterlines in layer aquaculture regions were still extracted along the edge of sandbanks. A marine hydrodynamic simulation model in the coastal area of central Jiangsu Province was adopted and run to simulate the water levels, with inversion of the water levels at 5 min intervals. The observed and forecast water level data from the tidal gauge stations and the satellite altimetry data (Jason-1) were used to validate the hydrodynamic model simulation results. All waterlines were resampled into points at 30 m intervals and linked to the hydrodynamic model to obtain the elevation of each water point. The cell size of DEMs was set to 60 m as the resolution of the satellite images varied from 10 to 56 m. Then, a gridded DEM of the tidal flats in the central Jiangsu coast was created from the discrete points of waterlines using the Kriging interpolation (Figure 2a). More details about the DEM construction can be found in the work of [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [45].  
Seven seasonal scale DEMs were established using the waterline method based on multi-temporal RS images. The time periods are December 2008--February 2009, March 2009--May 2009--August 2009, September 2009--November 2009, December 2009--February 2010, March 2010--May 2010 and June 2010--August 2010, representing seven seasons as follows winter 2008, spring 2009, summer 2009, autumn 2009, winter 2009, spring 2010 and summer 2010 (Figure 3). The accuracy of DEMs has been validated in a previous study and the mean error is 45.13 cm [50]. The main error sources in building tidal flat DEMs are from water level heighted process and interpolating process [51].  
Figure 2: Flow chart illustrating the methodology used in this study. (**a**) Digital elevation model construction; (**b**) aquaculture region extraction; and (**c**) sedimentary evolution based on a time series.  
These DEMs provide the foundation for seasonal and intra-annual pattern analysis of the impact of layer cultivation on tidal flat sediment.
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 5 Model Simulation and Experiments
Therefore, to deploy active defense technology in the special environment of satellite networks, the setting of change frequency is the key bottleneck, and it is necessary to comprehensively consider the network environment and security requirements in practical applications, so as to obtain availability-security-overhead balance. In addition, it is crucial to study efficient satellite-ground synchronization authentication technology and data migration scheme, and to improve the transmission efficiency of satellite-ground and inter-satellite links, and to improve the service response as well as satellite processing business capacity, in order to reduce the impact of active protection technology on network performance and to achieve low overhead processing capability of satellite networks.
# A Survey on Massive MIMO Systems in Presence of Channel and Hardware Impairments
## 5 Hardware Impairments
### I/Q Imbalance  
One of the hardware impairments in digital communication systems is in-phase/quadrature (I/Q) imbalance. In communication systems, the real part of the baseband signal is mixed with the high-frequency carrier and the imaginary part is mixed with the \(\frac{\pi}{2}\) phase-shifted version of the carrier (i.e., a quadrature carrier). In an ideal system, the amplitude gain of the mixers for real and imaginary parts are identical and the difference between the in-phase and quadrature carriers is exactly \(\frac{\pi}{2}\). However, in practical systems, mismatches between the amplitude and phase of the in-phase and quadrature components occur, which causes I/Q imbalance [64]. I/Q imbalance can occur at the transmitter and the receiver and is usually modeled as linear combination of the signal and its complex conjugate [65]. For example if \(x\) is the signal to be transmitted then the signal effected by the transmitter I/Q imbalance is modeled as  
\[y=x+\alpha x^{*}, \tag{9}\]  
where \(x^{*}\) is complex conjugate of \(x\) and \(\alpha\) is a real number smaller than 1.  
For massive MIMO systems with I/Q imbalance, the CSI knowledge is not sufficient and I/Q imbalance needs to be estimated and compensated. In [66] a new precoder which jointly compensates I/Q imbalance and multi-user interference was proposed, which processes the real and imaginary part of the signal, separately. The results show that in massive MIMO systems with regularized ZF (RZF) precoder, the effect of I/Q imbalance does not vanish even when the number of BS antennas tends to infinity. However, with the new proposed precoder, this effect is vanished as the number of BS antennas increases. In [67], the effect of I/Q imbalance on channel estimation and channel reciprocity of massive MIMO systems in TDD mode were studied. Therefore, channel estimation in the uplink and achievable rate in the downlink were investigated in presence of both BS and user I/Q imbalance. The results show that only the receiver I/Q imbalance at the BS effects the channel estimation accuracy. It is also proved that the downlink sum-rate is limited either by the transmit and receive I/Q imbalance at the BS or by the receive I/Q imbalance at the user side.
## 5 Results and Discussion of Findings  
The results presented in this section are focused on the performance of BEOSA and BIEOSA in comparison with those of similar methods, i.e., the binary dwarf mongoose optimizer (BDMO) [3], the binary simulated normal distribution optimizer (BSNDO) [9], the binary particle swarm optimizer (BPSO) [53], the binary whale optimization algorithm (BWOA) [54], the binary grey wolf optimizer (BGWO) [55] and the binary sailfish optimizer (BSFO) [56] algorithms. The selection of these algorithms was based on their outstanding performance, as described in various reports, and their status as state-of-the-art methods for binary optimization. We note that our evaluations of most of these algorithms applied the same parameterizations, e.g., the number of iterations and parameter settings. The following subsections are organized as follows. Firstly, we provide a comparative analysis of the various methods based on their fitness performance and the number of selected features. We then examine the classification accuracy of each method as compared with others. Next, we compare the cost functions of all methods and show the impact of the choice of classifiers on the feature classification procedure. Finally, we report the computational time of each method and discuss our findings. The following subsections using tabular and graphical means for the sake of clarity.
## 4 Analysis and Suppression Method of Short Circuit Current in MMC-Based MTDC Power Grid
### Short Circuit Current Calculation Method  
Various types of DC short circuit faults may occur in an MMC-based MTDC power grid, such as pole-to-pole short circuit fault and single-pole-to ground short-circuit fault. Existing research on DC short circuit current calculation mainly focuses on the pole-to-pole short circuit fault, because the pole-to-pole fault current of which is the most serious fault currents of all types of DC fault [13; 34; 35; 36; 40; 41; 42]. Therefore, the calculation of pole-to-pole short circuit current can provide guidance for parameter design and protection schemes of DC power grids.
## 2 Analysis Method
### Electromagnetic-Mechanical-Acoustic Coupling  
#### 2.4.1 Electromagnetic-Mechanical Coupling  
The electromagnetic and mechanical domains are coupled by the force factor which is the product of flux density and coil length. The vibration force is contributed by the electric current in the driver unit. The force can be calculated as  
\[F=Bli, \tag{5}\]  
Figure 4: Equivalent circuit of mechanical domain.  
Figure 3: Equivalent circuit of electromagnetic part.  
where \(F\) is the vibration force, \(Bl\) the force factor, and \(i\) the current. In Equation (5), the current is solved in the electrical domain. The calculation method is demonstrated in Equation (1). Conversely, the back EMF in electrical domain is contributed by the diaphragm velocity. The back EMF is calculated as  
\[V=Bl\dot{X} \tag{6}\]  
where \(V\) is the back EMF and \(\dot{X}\) the velocity. In Equation (6), the velocity is solved by the 1-DOF equation in the mechanical domain.
# Parameter Optimization for Ship Antiroll Gyros
## 3 Study of the Antirolling Characteristics of the Gyro  
Following Equation (11), this Section describes the antirolling characteristics of a public service ship and its supporting antiroll gyro and the influence of various parameters of the antiroll gyro on the roll angle of the ship. The ship is presented in Figure 3, and the parameters of it are presented in Table 1. The structure and parameters of the supporting antiroll gyro are presented in Table 2 and  
Figure 2: Structure and principle of the antiroll gyro.  
Figure 4. The gyro's rotor had an axisymmetric structure, and the intermediate rotating shaft and rotating outer ring were connected by rib welding.  
A comparison and analysis of the motion equation of the rolling ship and antiroll gyro indicated that the antirolling effect of the ship was related to the rotational momentum moment \(h_{0}\), the precession momentum moment \(J\), and the gyro's damping coefficient \(C\).
# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 4 Results
### Vehicle Detection in Continuous Records  
Because practical applications involve records longer than 5 s, we tested the framework for detecting vehicles using the 15 min continuous waveform dataset described in the previous section. The single-channel waveforms were cut into windows 5 s long, with a gap between consecutive windows of 1 s to reduce the potential for redundant detections (Figure 8).  
Thanks to the feature extraction implemented in the convolutional layer, CNN was able to detect vehicles of different classes with overlapping seismic records. In the example of Figure 9, a truck, a lightweight car, and a motorcycle passed the geophone in quick succession.  
We used a 90% probability threshold to determine the predicted vehicle class. The 15 min record included 93 different vehicles. Table 4 shows the performance of the three models in terms of precision and recall per vehicle class. Precision represents the percentage of correct declarations among all declarations made by the model, and recall represents the percentage of correct declarations among all declarations:  
\[\text{Precision}_{Class}=\frac{TP_{Class}}{TP_{Class}+FP_{Class}} \tag{6}\]  
\[\text{Recall}_{Class}=\frac{TP_{Class}}{TP_{Class}+FN_{Class}} \tag{7}\]  
where _TP_ stands for true positive, _FP_ stands for false positive, and _FN_ stands for false-negative [42]. We used visual data, as shown in Figures 8f-h and 9a to determine the true positive/true negative and ensure calculating the real accuracy for our method. By clear margins, CNN had the best precision and RNN had the best recall.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **DNN** & **CNN** & **RNN** \\ \hline Time (s): Total training (Average per epoch \({}^{1}\)) & 87 (0.89) & 112 (0.74) & 56 (0.69) \\ Accuracy (\%): Training (Validation) & 98.6 (95.6) & 99.1 (94.7) & 99.2 (86.1) \\ Loss: Training (Validation) & 7.80 \(\times\) 10\({}^{-2}\) (0.293) & 2.77 \(\times\) 10\({}^{-2}\) (0.240) & 3.52 \(\times\) 10\({}^{-2}\) (1.070) \\ Mean square error: Training (Validation) & 6.02 \(\times\) 10\({}^{-3}\) (0.019) & 3.58 \(\times\) 10\({}^{-3}\) (0.023) & 2.98 \(\times\) 10\({}^{-3}\) (0.065) \\ \hline \hline \end{tabular} \({}^{1}\) Each epoch includes 4560 waveforms of 5 s each.  
\end{table}
Table 2: Performances of networks for training (3420 waveforms) and validation (1140 waveforms).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Template Matching** & **DNN** & **CNN** & **RNN** \\ \hline Time (ms) & 560 & 74 & 67 & 55 \\ Accuracy (\%) & 77.3 & 97.8 & 96.6 & 85.3 \\ Mean square error & N/A & 0.009 & 0.014 & 0.063 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performances and running time (1140 waveforms) of networks and template matching.
## 4 What Is NFV?  
NFV is a concept brought by network service providers. It is utilized in the core and C-RAN of the cellular network [14]. In 2012, a significant evolution occurred when the world's seven leading telecom operators, including AT&T, Orange, Deutsche Telekom, and China Mobile, collaborated to release a white paper based on network function virtualization. The main objective of this document is to highlight the key advantages and associated challenges bound to NFV development and deployment. This white paper serves as the foundation of the NFV concept, underlining its potential to remodel the telecom industry by virtualizing network functions and abstracting them from dedicated hardware [17].  
The European Telecommunications Standards Institute (ETSI) has taken responsibility for the standardization of NFV and established a group known as the ETSI Industry Specification Group (ISG) for NFV. Since 2012, ETSI has continued to enhance the development of NFV by releasing significant milestones known as \"Releases\". These Releases are issued approximately every two years. The ETSI Releases are essential for guiding the adoption of NFV within the telecom industry, promoting interoperability and tackling the challenges and complexities of virtualization [88].  
As we know, every mobile network operator has a significant amount of specialized equipment and hardware dedicated to performing different network functions. These functions have dedicated hardware to support their functionality. These specialized middle boxes are hard to manage, and it is not a scalable solution to expand the network. When new mobile technologies like 5G and beyond came along, it used to mean a significant amount of work for operators. They had to change out hardware and redesign their entire network. Along with these, this dedicated hardware consumes considerable power [17]. Utilizing the NFV, these network functionalities can be performed in software that is installed in Virtual Machines (VMs), as shown in Figure 9.
## 2 Related Work
### Offloading Scheme Based on Deep Reinforcement Learning  
Some of the literature tries to solve the computation offloading problem with deep reinforcement learning. Users can benefit from offloading tasks to the MEC server. [PERSON] et al. considered the MEC server to perform random computing tasks and involve a time-varying wireless channel model, proposed the weighted total cost function of transmission delay,energy consumption and bandwidth allocation by using computational offloading, and proposed an actor-critic based asynchronous method to minimize the total system cost [12]. [PERSON] et al. aimed to minimize the long-term average transmission power consumed by users by dynamically optimizing user association and power allocation in each slot [13]. [PERSON] et al. proposed a new method based on a deep Q-network (DQN) to obtain a near-optimal computation offloading strategy for MEC networks [14]. The authors proposed a method which adopts deep deterministic policy gradient to make optimization decisions for computation offloading according to the real-time state of the network and the properties of the task. Considering multitasking, heterogeneity of edge subnets and mobility of edge devices, the algorithm can learn the network environment and generate computation offloading decisions to minimize task latency [15].
# Flexible Phased Antenna Arrays: A Review
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AESA & Active Electronically Scanned Array \\ AM & Additive Manufacturing: Layer-by-layer fabrication of 3D objects using digital designs \\ AME & Additive Manufacturing Electronics \\ AP & All Polymide: High-temperature, flexible substrate made entirely of polyimide \\ CP & Circular Polarization \\ CNT-TFT & Carbon Nanotube Thin-Film Transistors: Transistors made of carbon nanotubes for electronics \\ DC & Direct Current \\ EIRP & Effective Isotropic Radiated Power \\ ESA & Electronically Steerable Antenna \\ ESAA & Electronically Steerable Antenna Array \\ FFF & Fused Filament Fabrication: 3D printing method using melted thermoplastic filaments \\ Flex Sub & Flexible Substrate \\ FSK & Frequency Shift Key: Modulation scheme where data are encoded by varying frequency \\ FSS & Frequency Selective Surface: Surface that selectively transmits or reflects specific frequencies \\ FPA & Flexible Phased Array \\ FPGA & Field Programmable Gate Array: reconfigurable integrated circuit enabling fast operations \\ GaAs & Gallium Arsenide: High-speed semiconductor used in RF and microwave electronics \\ IC & Integrated Circuit \\ IMU & Internal Measurement Unit: Sensor module measuring orientation and motion \\ IP & Inkjet-printing: Non-contact printing technique depositing functional inks for electronics \\ LCP & Liquid Crystal Polymer: Thermoplastic with excellent RF and mechanical properties \\ LHCP & Left-Hand Circular Polarization \\ LNA & Low-Noise Amplifier \\ LP & Linear Polarization \\ MEMS & Micro-Electromechanical System \\ PA & Phased Array \\ PCB & Printed Circuit Board \\ PDMS & Polydimethylsiloxane: Flexible, biocompatible silicone used in soft electronics \\ PEI & Polyetherimide: High-strength, heat-resistant polymer used in aerospace and electronics \\ PESA & Passive Electronically Scanned Array \\ PET & Polyethylene terephthalate: Flexible and transparent polymer used in substrates \\ PP & Polypropylene: Thermoplastic used for flexible and chemical-resistant packaging \\ PSCU & Power Synthesis and Control Unit: Module managing power distribution in arrays \\ PTFE & Polytetrafluoroethylene: Non-stick, low-loss dielectric material used in RF applications \\ PV & Photovoltaic \\ RFAA & Rigid-flex antenna array: Hybrid antenna combining rigid and flexible sections \\ RHCP & Right-Hand Circular Polarization \\ RFIC & Radio Frequency Integrated Circuit \\ RFID & Radio Frequency Identification \\ RX & Receiver \\ SATCOM & Satellite Communication \\ SLA & Stereolithography: 3D printing method using photopolymerization with a laser \\ SLL & Side Lobe Level \\ SmallSat & Small Satellite \\ SNP & Silver Nanoparticle: Nanoscale silver particles used in conductive inks \\ SPI & Serial Peripheral Interface \\ TX & Transmitter \\ UAV & Unmanned Aerial Vehicle \\ UHF & Ultra-High Frequency \\ UVO & Ultra-Violet Ozone: Surface treatment method to modify materials \\ \end{tabular}
# A Survey on Approximation in Parameterized Complexity: Hardness and Algorithms
## 4 Algorithms
### Clustering  
Clustering is a representative task in unsupervised machine learning that has been studied in many fields. In combinatorial optimization communities, it is often formulated as the following: Given a set \(P\) of points and a set \(F\) of candidate centers (also known as facilities), and a metric on \(X\supseteq P\cup F\) given by the distance \(\rho:X\times X\to\mathbb{R}^{+}\cup\{0\}\), choose \(k\) centers \(C\subseteq F\) to minimize some objective function \(\text{cost}:=\text{cost}(P,C)\). To fully specify the problem, the choices to make are the following. Let \(\rho(C,p):=\min_{c\in C}\rho(c,p)\).  
* Objective function: Three well-studied objective functions are
* \(k\)-Median (\(\text{cost}(P,C):=\sum_{p\in P}\rho(C,p)\)).
* \(k\)-Means (\(\text{cost}(P,C):=\sum_{p\in P}\rho(C,p)^{2}\)).
* \(k\)-Center (\(\text{cost}(P,C):=\max_{p\in P}\rho(C,p)\)).
* Metric space: The ambient metric space \(X\) can be
* A general metric space explicitly given by the distance \(\rho:X\times X\to\mathbb{R}^{+}\cup\{0\}\).
* The Euclidean space \(\mathbb{R}^{d}\) equipped with the \(\ell_{2}\) distance.
* Other structured metric spaces including metrics with bounded doubling dimension or bounded highway dimension.  
While many previous results on clustering focused on non-parameterized polynomial time, there are at least three natural parameters one can parameterize: The number of clusters \(k\), the dimension \(d\) (if defined), and the approximation accuracy parameter \(\varepsilon\). In general metric spaces, parameterized approximation algorithms (mainly with parameter \(k\)) were considered very recently, but in Euclidean spaces, many previous results already give parameterized approximation algorithms with parameters \(k,d\), and \(\varepsilon\).
# Comparing End-to-End Machine Learning Methods for Spectra Classification
## 4 Experiments and Results
### Feature Importance Attribution Analysis Based on Gradient Backpropagation
It can be seen from the importance maps that the features with higher separability achieved higher feature importance values, especially the obvious features with lower intensity values in the latter half of the spectral curve, which are also well-captured. For convolutional attention networks, most of the obvious features are well-emphasized, and the importance contribution values of the features with low separability is much lower than that of the features with high separability. In the convolutional SCT model (Figure 6g), the feature importance value of some overlapping regions is even zero, which perfectly suppresses the indistinguishable features with low separability and low classification confidence. In addition, the shape of its feature importance curve is similar to that in the 1D FCNN model, indicating that it has high interpretability. Compared with the convolutional SC attention model (Figure 6h), it shows its advantages of introducing temporal attention. This indicates that the attention mechanism applied in this model can promote the distinguishable features and suppresses the unobvious and misleading features. Among all these models, only some of the non-obvious features in the convolutional SCT attention model can reach an importance value of 0, which benefits from the self-attention mechanism applied across three dimensions. We further show its feature importance attribution map of the test dataset in Appendix G.  
The gradient-based feature importance analysis can also be used as one of the references for classification performance, which can be combined with loss function for model training and evaluation. Usually, if the model is not well-trained, the feature contribution map will be rather messy, distinguishing features will not dominate the classification task,  
Figure 7: The weights learned in each FC layer of the 1D FCNN model. and many unobvious features will have a great impact; thereby, reducing the credibility of the classification result.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 2 Multi-Frame PCA-Based Coding Method
### Format of Each Matrix  
In encoder, when the sampling rate is 48 kHz, the frame has 240 spectral coefficients after MDCT (in this paper, the MDCT frame size is 5 ms with 50% overlap). There are many forms of matrices like 6 \(\times\) 40, 12 \(\times\) 20, 20 \(\times\) 12, and so on; each format of matrix brings different compression rates. In a simple test, several formats of original matrix were constructed. Then, a subjective test was devised using those different dimensional rotation matrices. 10 listeners recorded the number of dimensions when the restored audio had acceptable quality. Then, the compression rate was calculated by the number of dimensions. As is shown in Figure 4, the matrix has the largest compression rate when it has 16 rows. So, the matrix \(X_{\left[\begin{array}{cc}16&15\end{array}\right]}\) with 16 rows and 15 columns is selected for transient frame in this paper. That means a 240-coefficient-long frequency domain signal is divided into 16 samples, each sample having 15 dimensions.