esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Cybersecurity Technology and Algorithm Based on Quantum Computing
## 5 Enhancement of Security
### Mersenne Twister  
The Mersenne Twister [5] is a pseudo-random number generation algorithm created by [PERSON] and [PERSON]. It is used in several programs, including C++, Excel, MATLAB, Python, and R and generates random numbers using 624 numbers.  
The Mersenne Twister uses MT19937 with a random repetition period of \(2^{19,937}-1\) and MT19937-64 with 64 bits. This repetition period is the Mersenne prime, which is the number of the Mersenne prime that is the minority of Mersenne number. As you can see from Equation (2), the Mersenne number is a number from the power of two minus one.  
\[M_{n}=2^{n}-1 \tag{2}\]  
The random numbers generated by the Mersenne Twister have a uniform distribution up to 623 dimensions unlike the linear congruential generator. In other words, finding a pattern is difficult, even if a coordinate corresponding to a 623-dimensional hypercube is paired with a random number of 623.  
However, the Mersenne Twister also has the disadvantage of being able to predict the current state or the subsequent random numbers with only a finite number of random numbers knowing their period and range.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
###### Abstract  
Recently, the Korean government has announced a plan to develop a satellite-based navigation system called the Korean Positioning System (KPS). When designing a new Radio Navigation Satellite Service (RNSS) signal, the use of the S-band has emerged as an alternative to avoiding signal congestion in the L-bands, and South Korea is considering using the S-band with the L-bands. Therefore, this study proposed possible S-band signal candidates and evaluated their performance, such as the radio frequency (RF) compatibility, spectral efficiency, ranging performance, and receiver complexity. Several figures-of-merit (FoMs) were introduced for quantitative performance evaluation for each candidate. Each FoM was calculated using an analytical equation by considering the signal design parameters, such as the center frequency, modulation scheme, and chip rate. The results showed that the outstanding candidate signal was different depending on the signal performance of interest and the reception environments. Therefore, we discuss and summarize the signal performance analysis results considering the whole FoMs together. Under the assumptions given in this paper, the binary phase shift keying (BPSK)(1), sine-phased binary offset carrier (BOC\({}_{s}\))(5,2), and BPSK signals were superior for the spectral efficiency, ranging performance, and receiver complexity, respectively.  
RNSS; GNSS; KPS; S-band; satellite navigation; modulation +
Footnote †: journal: _Sensors_
###### Abstract  
Seismic signal processing often relies on general convolutional neural network (CNN)-based models, which typically focus on features in the time domain while neglecting frequency characteristics. Moreover, down-sampling operations in these models tend to cause the loss of critical high-frequency details. To this end, we propose a feedback information distillation network (FID-N) in the non-subsampled contourlet transform (NST) domain to remarkably suppress seismic noise. The method aims to enhance denoising performance by preserving the fine-grained details and frequency characteristics of seismic data. The FID-N mainly consists of a two-path information distillation block used in a recurrent manner to form a feedback mechanism, carrying an output to correct previous states, which fully exploits competitive features from seismic signals and effectively realizes the signal restoration step by step across time. Additionally, the NST has an excellent high-frequency response and powerful curve and surface description capabilities. We suggest converting the noise suppression problem into NST coefficient prediction, which maintains more detailed high-frequency information and promotes the FID-N to further suppress noise. Extensive experiments on both synthetic and real seismic datasets demonstrated that our method significantly outperformed the SOTA methods, particularly in scenarios with low signal-to-noise ratios and in recovering high-frequency components.  
deep learning (DL); convolutional neural networks (CNNs); feedback information distillation network (FID-N); non-subsampled contourlet transform (NST); noise suppression; seismic data +
Footnote †: journal: Applied sciences
## 2 Materials and Methods  
### Material Preparation  
The Zn:Cu:Fe:LiNbO\({}_{3}\) crystals, which contained 1 mol% Fe\({}_{2}\)O\({}_{3}\) and 1 mol% CuO, were grown in air using the Czochralski technique with varying doping concentrations of ZnO (1, 3, 5, and 7 mol%). The optical quality of the crystal is highly dependent on the purity level of its raw materials, namely, Nb\({}_{2}\)O\({}_{5}\), LiCO\({}_{3}\), ZnO, CuO, and Fe\({}_{2}\)O\({}_{3}\). Therefore, these raw materials used for crystal growth must have a minimum purity level of 99.99%. The Nb\({}_{2}\)O\({}_{5}\), LiCO\({}_{3}\), ZnO, CuO, and Fe\({}_{2}\)O\({}_{3}\) materials were thoroughly mixed for over 24 h before being placed into a platinum crucible. They were then heated to 750 \({}^{\circ}\)C for 2 h to eliminate CO\({}_{2}\) and subsequently further heated to 1150 \({}^{\circ}\)C for another 2 h to facilitate the formation of a solid reaction polycrystalline material suitable for crystal growth.  
The growth conditions were selected as follows: the temperature gradient was set at 2 \({}^{\circ}\)C/mm, while the polling rate and rotation rate were controlled within the range of 0.7 to 1.7 mm/h and 15-28 rpm, respectively. After the completion of crystal growth, it was gradually cooled to room temperature at a controlled rate of 35-40 \({}^{\circ}\)C/h. Simultaneously, to mitigate the impact of crystal spontaneous polarization on optical properties, all crystals were subjected to an artificial polarization process for 8 h in an intermediate-frequency furnace with a temperature of 1100 \({}^{\circ}\)C with a nearly zero temperature gradient, and a current density of 5 mA/cm\({}^{2}\), resulting in the attainment of single-domain crystals. The wafers with dimensions of 5 mm \(\times\) 8 mm \(\times\) 2 mm (x \(\times\) y \(\times\) z) were obtained by cutting from the central region of the ZnCu:Fe:LiNbO\({}_{3}\) crystals along the \(y\)-axis. The samples, denoted as Zn1, Zn3, Zn5, and Zn7, had varying concentrations of Zn\({}^{2+}\) ions.
## 4 Experimental Evaluation
### Block Size  
Analysis operations are heavily dependent on the size of each individual block. Appropriate block sizes for volume rendering has been investigated in related work [36], which found that blocks with a \(64\times 64\times 64\) resolution achieved the best result in their use case. A similar block resolution has been used in research related to this work [10], although with no motivation. We analyzed five resolution to determine an appropriate block resolution for our use case: \(50\times 50\times 50\), \(50\times 50\times 100\), \(50\times 100\times 100\), \(100\times 100\times 100\), and \(100\times 100\times 200\).  
Tests were performed on cluster A using the MF data set and a Mean probe. As per the results, shown in Figure 4, a block size of \(50\times 50\times 100\) or \(50\times 100\times 100\) achieved the fastest computation time. We chose to use the block size of \(50\times 100\times 100\) in all successive tests on cluster A presented in this paper. The simulation resolution and the number of processes on cluster B differ from that of cluster A. As a result, it was not possible to use the same block size. Instead, we set the block size on cluster B to \(75\times 75\times 75\), which was the closest alternative.  
Figure 4: The average time required to perform the importance analysis for the MF data set on cluster A. The error bars display the standard deviation of the computation times.  
Figure 3: Visualization of the MF data set generated by the RMI simulation at time (**a**) 0 ms, (**b**) 2 ms, and (**c**) 4 ms; blue represents pure air, red represents pure sulfur-hexafluoride (SF\({}_{6}\)), while yellow represents the binary mixture comprising of the two miscible components.
## 2 Background
### Software-Defined Networks (SDNs)  
Software-defined networks are the latest technology proposed to separate the control plane from the data plane preventing the traditional network's limitations in terms of programmability, flexibility, scalability, and management. Via this separation, performed by well-defined programming interfaces between switches and the SDN controller, the switch shifts to simple forwarding devices, and all control logic becomes centralized in the SDN controller [34].  
Figure 4 shows a simplified SDN architecture. The southbound application software interface (API) represents the interface between the switches and the SDN controller. The OpenFlow protocol, first introduced in [35], is used in this boundary to transfer data from the data plane to the network plane. The northbound API, for its part, enables the communication between the SDN controller and the application running above the network. Finally, the SDN controller has the advantage of being flexible so that it can be adapted to the network environment and the application using the network [36].  
Within the context of Industry 4.0, SDNs address the nature of the wired/wireless IIoT nodes using the rules created in the SDN controller. These rules route the data packet coming from one switch to another or to the station connected to the switch [17]. The SDN-based IIoT to support Industry 4.0 contains three layers:  
* Application layer: The application layer contains the IIoT processing system located in the data.
* Control layer: In the control layer, the IIoT applications are connected to the IIoT infrastructure through the SDN controller.
* Infrastructure layer: The infrastructure layer consists of IIoT sensors and actuators that connect to the network gateway and the whole backbone network [37].  
Figure 3: Wireless Sensor Network (WSNs).
###### Abstract  
Non-orthogonal multiple access (NOMA) has been widely acclaimed as a promising solution to enhance spectral efficiency, increase user fairness, and scale up the number of users in wireless networks by enabling multiple users to share the symmetrical wireless resources. This work is concerned with the development and implementation of blind reconfigurable intelligent surface (RIS)-aided ordered NOMA (ONOMA) for smart reflector (SR) and access point (AP) configurations. For the RIS-SR-ONOMA and RIS-AP-ONOMA configurations, the closed-form probability of outage expressions is derived, and their performance is reported for distinct values of passive reflector elements. Through optimal power allocation, the downlink (DL) sum capacity is maximized. RIS-aided ONOMA reduces outage rates and increases sum capacity over the traditional ONOMA system. It is discovered that the RIS-aided ONOMA system increases the sum capacity by \(\approx\)33% at 20 dB signal-to-noise ratio (SNR) for 32 passive reflector elements. The addition of passive reflector elements and symmetrical allocation among users improves the performance of RIS-SR-ONOMA and RIS-AP-ONOMA in terms of outage probability, sum capacity, and probability of error. The proposed work can be employed in applications such as vehicular ad hoc networks, where obtaining precise channel information is difficult due to the rapid mobility of the vehicles.  
accs point (AP); ordered non-orthogonal multiple access (ONOMA); probability of outage; reconfigurable intelligent surfaces (RIS); smart reflectors (SR); sum capacity; transmit power optimization +
Footnote †: journal:  
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## 4 Numerical Modeling
### SAP Model  
The 3D FE model built with SAP2000 is shown in Figure 4a. The model is constituted of a Timoshenko linear visco-elastic beam element fixed at the base representing the pier of the bridge, with a 5% of Rayleigh damping ratio.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Code** & **Fa** & \begin{tabular}{c} **D** \\ **(s)** \\ \end{tabular} & **PGA** & \begin{tabular}{c} **I\({}_{\mathbf{A}}\)** \\ **(cm/s)** \\ \end{tabular} & \begin{tabular}{c} \(f_{\mathbf{PEAK}}\)** \\ **(Hz)** \\ \end{tabular} & \begin{tabular}{c} \(f_{\mathbf{MEAN}}\)** \\ **(Hz)** \\ \end{tabular} & \
\begin{tabular}{c} **T\({}_{\mathbf{MEAN}}\)** \\ **(s)** \\ \end{tabular} &
\begin{tabular}{c} **D\({}^{\bullet}\)** \\ **(s)** \\ \end{tabular} \\ \hline n. 1 & 1.5 & 44.17 & 0.326 & 0.87 & 2.66 & 4.48 & 0.304 & 4.609 \\ \hline n. 2 & 1.0 & 44.17 & 0.214 & 0.37 & 2.66 & 4.48 & 0.304 & 4.609 \\ \hline n. 3 & 0.75 & 44.17 & 0.163 & 0.21 & 2.66 & 4.48 & 0.304 & 4.609 \\ \hline n. 4 & 0.4 & 44.17 & 0.086 & 0.06 & 2.66 & 4.48 & 0.304 & 4.609 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the main features of the considered seismic inputs.  
Figure 4: The Figure shows: (**a**) the 3D SAP 2000 model of the beam element fixed to the ground; (**b**) a sketch of the simplified geometry and soil layering.  
The elastic modulus of the reinforced concrete pier is a non-cracked modulus (that is, the stiffness reduction due to cracking is not considered--this assumption will be the same for the PLAXIS 3D model, described in the following Section 4.2). The beam element is 12 m in height and its rectangular section is 6 m \(\times\) 2 m. The dead load of the deck is applied by a nodal force at the top of the beam element and it is considered a mass source for the seismic analysis. To define the seismic input to apply to the bridge mass, a seismic site response analysis (SSRA) was performed, adopting the model shown in Figure 4b. Here, the considered stratigraphy is reported, along with the sketched geometrical characteristics of the model. The SSRA consisted of simulating the propagation of the seismic waves from the input base rock to the ground surface, assuming a non-linear constitutive model for the soil. For the SSRA, a 1D propagation analysis by means of the PLAXIS software and assuming a non-linear soil behavior (termed as _HS-small_) was performed.
## 3 Experimental Data and Processing Strategies
### Experimental Data Sets  
To validate how our approach performs when the data are interrupted during an RT-PPP time and frequency transfer, we sampled observation data every 30 seconds from 6 stations of the IGS network, covering DOY (days of the year) 182-212 in 2018. The detailed information from 6 stations are listed in Table 2. High-performance receivers have the advantage of being able to perfectly synchronize the internal clock to an external signal source. In this contribution, the experimental data sets are divided into two main parts:  
1. [label=(0)]
2. Simulation data: The daily observations and products will be randomly interrupted four times for 10 min, 30 min, 60 min and 90 min, respectively. The precise orbit and clock products released by IGS are utilized in this part.
3. Real-time product: The real-time products used in this study were developed by GMV (GMV Aerospace and Defence) (CLK81).
# A Real-Time On-Orbit Signal Tracking Algorithm for GNSS Surface Observations
## 7 Discussion and Algorithm Variations
### Interpolated Prediction of Specular Point Parameters  
Additional accuracy in the initialization of the specular point position can be achieved by estimating various parameters in the delay and Doppler open look tracking estimates using interpolated prediction techniques (such as polynomial fitting) for each parameter over short intervals (from 3 s to 1 min or more). This has the significant advantage of reducing the specular point calculation duty cycle which can result in significant on-board savings of available computational resources. Additionally, there are publically available orbit propagation tools which can be integrated into a forward orbit prediction module, including the SGP4 [33] or SPoCK [34] orbit propagation tool sets.  
By using a general interpolation technique or propagated positions and velocities of the transmitter and receiver it is possible to forward-predict open loop tracking parameters such as the specular point \(\vec{S}\), or the delay and Doppler estimates directly, \(D\) and \(\tau\). Over ocean, this forward prediction can be very successful and greatly reduce the computational burdens. Over land with varying terrain, the specular point, delay and Doppler are less likely to change in a polynomial predictable fashion. For this reason it is recommended to constrain predictive algorithms to more uniform surface regions (such as ocean and sea ice). The general steps in the implementation of a forward propagated polynomial open loop tracking algorithm include,Depending on available computer resources, propagating forward the receiver satellite and transmitting satellite location using an existing orbit propagator, at 10 s intervals over a minute into the future for example.
2. Estimate the surface specular points at each predicted future step.
3. Use a standard polynomial fitting function to generate a function to forward predict the specular location, from which the delay and Doppler information can be quickly calculated. Or alternatively, perform a polynomial fit on the predicted delay and Doppler values directly.
4. Use the generated polynomial function to calculate the open loop delay and Doppler over the entire measurement reflection track, while the reflection quality indicator indicates a viable measurement is possible.
## 4 Discussion  
### The Design and Analysis of the SFA Encoder  
Since AlexNet [38] came into the world in 2012, convolutional networks have dominated computer vision and related fields. A large number of models to obtain SOTA performance are designed based on convolutional networks and their variants [39; 40; 41]. However, convolution has a limited receptive field and insufficient global modeling ability, which hinders its further development in vision and related fields. Later, ViT [42] introduced Transformer, a dark horse in the NLP field in 2017, into the computer vision field. The powerful global modeling capabilities of Transformer take computer vision in a new direction, but the computing costs of the attention mechanism are a headache for scholars.  
The SFA Encoder used in this paper uses recursive gated convolution and Transformer for high-order interaction to obtain global information about images, so as to deal with the problem of high within-class variability in complex scenes of remote sensing images. Compared with Transformer, the advantages of convolution are computational simplicity and the need for less data to train, while the disadvantage is that the receptive field is not large enough. The recursive gated convolution enables the global information to  
Figure 7: Partial segmentation result. The first column is the original image, the second column is labeled image, the third column is the segmentation result of GLFFNet, and the fourth column is the segmentation result of UNetFormer.  
interact, simulates the self-attention operation of Transformer, expands the receptive field, and obtains more global feature information. It has been experimentally proven that it is feasible to use recursive gated convolution instead of the self-attentive operation of Transformer, but the accuracy is not enough. Therefore, this paper introduces the attention mechanism of Transformer to recursive gated convolution, integrates the advantages of recursive gated convolution and Transformer, and designs the SFA encoder. As shown in Table 1, the results demonstrate that the SFA encoder outperforms encoders based mainly on recursive gated convolution, or Transformer.
# Mapping Phonation Types by Clustering of Multiple Metrics
### Basis for Clustering
The SB reaches its minimum as the spectrum becomes more dominated by the fundamental partial. The CPPs continues to climb as phonation becomes more stable and less breathy. The crest factor is starting to increase. Range C (70... 79 dB) might be termed 'loose' phonation: the CSE is approaching zero, meaning that only a small instability remains in the EGG waveform; conversely, the CPPs is now picking up rapidly, indicating increased stability in the acoustic waveform. \(Q_{\rm ci}\) is turning around and starts to rise as adduction increases. The (log of) \(Q_{\Delta}\), or the peak rate of contacting, has almost reached its maximum and will increase only slowly from here. The harmonic content of the acoustic signal is now increasing, as evidenced by a steady growth both in the SB and in the crest factor. In range D (79... 93 dB), phonation is 'firm' and stable. The CSE is zero, and the CPPs is near its maximum. The \(Q_{\rm ci}\) continues to increase steadily, together with the SB and the crest factor, while the \(Q_{\Delta}\) has settled at a high value. Finally, in range E (>93 dB), the acoustic metrics level out, or even reverse, in a manner that is reminiscent of spectral saturation [21]. Only \(Q_{\rm ci}\) still manages to increase a little, implying a slight increase in adduction. This range might perhaps be called 'hard' phonation. This example is intended to show how we might infer with some confidence the character of the phonation, even though we have access only to external signals and not to endoscopic imaging or internal aerodynamics. The changes with increasing SPL appear to comply consistently in all six metrics with the proposed phonatory scenarios, A to E. This particular example was chosen because in this task and with this subject, the \(f_{\rm o}\) dependency of most metrics was simple enough to be factored out by a linear transform. This is not generally the case: in most participants, when phonating over the larger range of the full voice range, the metrics exhibit considerable non-linear variation also with \(f_{\rm o}\), which is challenging to visualize effectively. This is where clustering becomes useful, as we hope to demonstrate. Clearly, the above observations suggest the possibility of classifying phonation types by clustering combinations of metrics. From A through E, those combinations have provided essential phonetic information. Though they will require independent physiological evidence for their validation, the different phonatory characters, when played back selectively, are also (informally) audible to the authors. The clustering of these six (and/or any other) metrics in combination will allow us to plot the outcomes not just along the SPL axis but across the 2D voice field, so as to also visualize dependencies on \(f_{\rm o}\).
# An Efficient SS-MAC Protocol for IEEE 802.15.4-Based WSNs of Cluster Tree Topology
## 3 The Proposed SS-MAC Protocol
### Setup Phase (SP)  
The task in SP is to construct a cluster tree topology and collect data from member nodes to CHs. Topology construction is carried out in three steps: first, the sink broadcasts a tree topology construction (TREE_CON) message, indicating that all CHs in the network establish a tree routing according to the DMH-LEACH algorithm proposed in reference [22]; second, each tree node (i.e., CH) broadcasts a CH broadcast declaration (CH_DEC) message, and non-cluster head nodes reply with a join cluster unit request (JC_REQ) message to request to join the nearest CH after receiving CH_DECs; finally, CHs allocate a short address and a CP slot to each node that requested to join, and respond to each node with a control slot allocation (CS_AOC) message.  
The SP can be divided into four sub-periods, as shown in Figure 2. The following sections will introduce the four sub-periods separately.