esa-sceva/satcom-chunk-collection huggingface dataset:
# Brain-Inspired Data Transmission in Dense Wireless Network
## 2 Related Work  
Humans have been always highly inspired by the functioning of our world and nature, and in particular of the human body; these inspirations lead to numerous significant findings and discoveries. In the context of computer science, there are various algorithms and solutions which are motivated by the observation of nature. One can mention for example solutions based on the behavior of bees, bumblebees, or ant-colony, in general, swarm intelligence [22; 23; 24], an algorithm based on findings in genetics, but also solutions like simulated annealing which mimics the behavior of the fabric processing. Recently, the most popular solutions are related to the application of the neural networks in their various configurations (like convolution neural networks, recursive neural networks, etc.), and implementation contexts (computing, feature recognition, automation, networking) [25; 26; 27]. It is also worth mentioning that the behavior of bees (swarm) has inspired the design of the whole communication and networking frameworks and protocols, like ZigBee [28]. However, when projecting the brain-inspirations on wireless communications, the number of publications is not that high, which suggests that this domain of research is still unexplored. However, it is worth mentioning some works, such as [29], where a special class of neuromorphic computing (mainly reservoir computing) is applied for signal detection in multiple input multiple output orthogonal frequency division multiplexing systems. In [30], the authors have proposed the brain-inspired method for constructing a robust virtual wireless sensor network. Next, in [31], the dynamic spectrum management in cognitive wireless ad-hoc networks was investigated. A very promising approach has been presented in [32], where the authors proposed the neural wireless sensor networks inspired by the functioning of the neural network.  
Recently, increased interest is also observed in so-called bio-molecular communications, as presented in [33]. Synaptic transmission and neuro-spike transmission have been analyzed in detail in the context of communications in [34; 35; 36].  
Furthermore, the authors in [37], proposed to use the property of neurons to connect to many other neurons (both as an information source and an information outlet) in sensor networks, where a dynamic node grouping mechanism is proposed. In turn, in [38] one can find an algorithm for selecting nodes and bit rates in sensor networks which bases on the immune system.
## 4 Credibility Verification of Simulation Model
### Comparison of Simulated and Test Waveforms  
In this model, we set simulation parameters consistent with the test to ensure that the simulation accurately reflects the actual conditions. The test parameters are shown in Table 1.  
Under the same operating conditions, the error in overvoltage amplitude between the test overvoltage data and the simulation data is less than 4.0%, which proves that the simulation model has high precision. In this paper, only the difference between the simulation and test amplitudes is compared. In Figure 11, there are some differences between the voltage amplitude before and after the arcing. This is because the simulation model cannot fully reflect the actual circuit structure, so there will be a little difference in voltage amplitude. It can be seen in the Figure 11 that the duration time of the arc is tens of nanoseconds, which is similar to the characteristics of VFTO. These conclusions indicate that the simulation model can sufficiently reflect the test conditions, and the error in the simulation results is relatively small.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Times**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{\begin{tabular}{c} **Pantograph** \\ **Dropping Speed** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cetenary** \\ **Distance** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cetenary Voltage** \\ **Phase** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Operating** \\ **Overvoltage of** \\ **Dropping** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Error of** \\ **Simulation Data** \\ **Compared to Test** \\ **Data** \\ \end{tabular} } \\ \hline \multirow{2}{*}{1} & Test Data & 0.31 m/s & 1.90 m & 89\({}^{\circ}\) & 305.10 kV \\ \cline{2-6}  & Simulation Data & 0.31 m/s & 1.90 m & 89\({}^{\circ}\) & 298.20 kV \\ \hline \multirow{2}{*}{2} & Test Data & 0.32 m/s & 1.90 m & 87\({}^{\circ}\) & 273.90 kV \\ \cline{2-6}  & Simulation Data & 0.32 m/s & 1.90 m & 87\({}^{\circ}\) & 267.90 kV \\ \hline \multirow{2}{*}{3} & Test Data & 0.29 m/s & 1.90 m & 91\({}^{\circ}\) & 253.70 kV \\ \cline{2-6}  & Simulation Data & 0.29 m/s & 1.90 m & 91\({}^{\circ}\) & 243.80 kV \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test and simulated results with parameters.  
Figure 10: Testing system.
# Changing Dynamics of SARS-CoV-2: A Global Challenge
## 4 Variants of SARS-CoV-2
### Variants of Concern
#### 4.2.2 The Beta Variant (B.1.351)  
The Beta variant was first reported in South Africa in August 2020, and its prevalence escalated from 11% in October to 87% by December 2020 [54]. Currently, the variant is present in about 117 countries, including the United States, Sweden, Germany and France (15 December, GISAID).  
It has a total of 11 mutations and shares D614G and N501Y with the Alpha variant (Figure 6). Apart from those in the spike region, this variant contains successive deletions of the amino acids L242, A243 and L244, and replacements D80A, D215G, A701V, K417N and E484K (Figure 2c) [55]. Two lineages of B.1.351 were detected in November 2020, denoted as variant B.1.351-V2 (with an extra N-terminal mutation, L18F), and variant B.1.351-V3 (it possesses an additional R246I mutation in loop N5; however, the D215G mutation is missing) [37].  
Figure 6: (**a**) Venn plot depicting the common spike glycoprotein mutations between the variant of concerns (VOCs)—Alpha, Beta, Gamma, Delta and Omicron. (**b**) the table showing the list of common spike glycoprotein mutations for the same. Figure has been drawn using Prism software. *: Found only in some of the sequenced alpha strain.  
Figure 7: Alignment between sequences of spike glycoprotein of SARS-CoV-2 variants. Amino acid variations within each variant are marked at the particular positions. (In order from up to down; wild type (PDB ID: 6 VYB), Alpha (PDB ID: 7 LWT), Beta (PDB ID: 7 LYQ), Gamma (accession no.: QQX12069.1), Delta (accession no.: QW057033), Eta (accession no.: QW017721.1), Iota (accession no.: QTP80309.1), Kappa (accession no.: QTY54081.1), Lambda (accession no.: QRX62290.1)) (alignment of sequences using Bioedit).  
Due to D614G and the three RBD mutations, N501Y, E484K and K417N (Figures 6 and 7), the affinity of B.1.351-V1 for hACE2 magnifies by 19-fold [37]. The K417T/N mutation lowers the hACE2 binding affinity, but helps the virus to evade neutralizing antibodies [56]. The K417N destabilizes the RBD-down conformation, hence promoting the open conformation similar to D614G and N501Y. E484K lacks interactions with the neighboring RBD required for stabilizing the down conformation, hence the mutant favors only RBD-up conformation; it also has the ability to prevent the binding of neutralizing antibodies to the virus [37]. As the cumulative effect of all these mutations, the transmissibility of Beta has increased by ~20-113% when compared to the wild-type strain [14] (Table 1).
# Research on the Dynamic Characteristics Analysis and Power Control Method of Heat Pipe Reactors
## 2 Dynamic Modelling of HPR
### Neutron Dynamics Model  
To design the reactor power control method, it is necessary to focus on the total power of the reactor only. Therefore, the point reactor neutron dynamics model is used to describe the relationship between nuclear power and reactivity. The equation of a point reactor with six groups of delayed neutrons is shown in Equation (1):  
\[\left\{\begin{array}{l}\frac{dn_{r}}{dt}=\frac{e-\beta}{l}n_{r}+\sum\limits_ {i=1}^{6}\lambda_{i}c_{i,r}\\ \frac{dc_{i,r}}{dt}=\frac{\beta}{l}n_{r}-\lambda_{i}c_{i,r}\end{array}\right. \tag{1}\]  
where \(n_{0}\) is the initial neutron density at steady state; \(n_{r}=n/n_{0}\) is the ratio of neutron density to the initial neutron density, which can represent the relative nuclear power; \(c_{i,r}=\) is the ratio of \(i\)th group precursor density to \(n_{0}\); \(\beta_{i}\) is the fraction of \(i\)th group delayed fission neutrons; \(\lambda_{i}\) is the effective \(i\)th group precursor radioactive decay constant; \(\beta\) is the total fraction of delayed fission neutrons; and \(l\) is the effective prompt neutron lifetime.  
Reactivity feedback in the HPR is mainly caused by the Doppler and expansion effects of the fuel and monolith. \(\alpha_{f}\) and \(\alpha_{m}\) are the total feedback coefficients for the fuel and  
Figure 1: Structure diagram of “MegaPower” HPR core and heat exchanger [2].  
monolith, respectively, which both incorporate the Doppler and expansion effects, and are derived from the literature [28]. The reactivity feedback equation is shown in Equation (2).  
\[\left\{\begin{array}{l}\rho=\rho_{r}+\rho_{f}+\rho_{m}\\ \rho_{f}=\alpha_{f}\cdot\Delta T_{f}\\ \rho_{m}=\alpha_{m}\cdot\Delta T_{m}\end{array}\right. \tag{2}\]  
where \(\rho_{r}\) is the reactivity of the external input, \(\rho_{f}\) is the reactivity caused by fuel temperature change, and \(\rho_{m}\) is the reactivity caused by monolith temperature change. \(\Delta T_{f}\) and \(\Delta T_{m}\) are the deviation in the fuel temperature and the monolith temperature relative to the initial temperature, respectively.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 3 From S-MIM to F-SIM
### F-SIM Forward Link (DVB-S2)  
The F-SIM specifications do not force the use of a specific forward link but only require that it supports certain features. In practice, the forward link is implemented with the DVB-S2 protocol, using MPE encapsulation to support the IP. Signaling is transported as multicast IP streams containing compressed Protobuf structures. DVB adaptive coding and modulation (ACM) is supported, with the following advantages:  
* Supporting terminals with different performances, e.g., different antenna sizes;
* Providing high availability, by using strong DVB-S2 physical layer configurations (MODCODs) when required by weather conditions;
* Increase coverage up to the limits of the satellite beam;
* Avoiding bandwidth waste, by switching to more efficient MODCODs if the link budget permits this action.  
Extensions to DVB-S2X very low signal-to-noise ratio (VL-SNR) modes have been tested, as well as the use of Generic Stream Encapsulation (GSE). The DVB-S2 signal also contains an NCR (network clock reference) counter running at 27 MHz, in order to help the terminals to correct the local clock, in order to transmit signals with a very accurate frequency and symbol rate. Alternatively, the terminals can use the symbol rate of the forward link as the frequency reference.
# A Multi-Factor Approach for Selection of Developers to Fix Bugs in a Program
## 3 Method
### Overview
#### 3.1.1 B-Based Analysis Score  
In B-based analysis, for a new bug report \(B_{new}\), we use ML-KNN algorithm to predict its resolvers (i.e., developers who have experience with bug resolution) and output a score for each potential resolver [42].  
First, we find the k-nearest neighbors for \(B_{new}\) by the k-nearest neighbor algorithm. Developer recommendations are provided based on the developers of these k bug reports. Second, we use the Euclidean distance formula (see Equation (1)) to calculate the distance between the bug reports (i.e., measure the similarity between bugs) and find k bug reports with the smallest distance from the new bug report \(B_{new}\).  
\[\rho=\sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right)^{2}} \tag{1}\]  
Figure 1: Overall framework of the ABC-DR method.  
We use the following features to characterize a bug report:  
1. Terms: It is a set of preprocessed data, a collection of bug report descriptions that appear in the summary and description of the bug report. Data preprocessing involves removing words that are not meaningful and reducing the word to the root form, i.e., stemming. Each stem is a feature. Its value is determined by the frequency with which the word appears in the bug report.
2. Topics: We use the LDA model for topic analysis, simplifying the reports into a set of topics, and determining the probability that the reports belongs to the topics [43; 44]. Each topic is a feature, and the feature value is the probability that the topic belongs to a bug report.
3. Products: This refers to the affected product in the bug report. Each product is a binary feature [10]. There are only two possible values. If the product is affected by a bug, the eigenvalue is 1, else the eigenvalue is 0.
4. Components: This refers to the component affected by the bug in the bug report, either affected or not affected. If the component is affected by a bug, the eigenvalue is 1, else the eigenvalue is 0.  
Third, we use the multi-label classification method to infer the resolvers for the new bug \(B_{1}\) based on the resolvers of its k-nearest neighbors. We treat each developer as a class label, each bug report as a data element, and a bug report with resolvers as a training data element. In this way, the step of providing the resolvers for bug reports is reduced to a multi-label classification problem [45]: given a data element (i.e., a new bug report) and predicting its label (i.e., the resolvers).  
The ML-KNN method outputs the relative likelihood of the label being assigned to a data point [46]. After applying this method, we assign a score to each potential developer \(di\), indicating the possibility that \(di\) is a resolver for the new bug report \(bi\), represented by \(B_{score_{hi}}(di)\).
# Transmitter and Receiver Circuits for a High-Speed Polymer Fiber-Based PAM-4 Communication Link
###### Abstract  
A high data rate RF-DAC and a power detector (PD) are designed and fabricated in a 250 nm indium phosphide (InP) double heterojunction bipolar transistor (DHBT) technology. A communication link using the Tx-Rx over polymer microwave fiber (PMF) is measured. The link consists of a pulse amplitude modulation (PAM) modulator and a PD as a demodulator, as well as a one-meter-long dielectric waveguide. The working frequency range of the complete link is verified to be 110-150 GHz. The peak output power of the PAM modulator is 5 dBm, and it has a \(-3\) dB bandwidth of 43 GHz. The PD consists of a parallel connected common emitter configured transistor and a common base configured transistor to suppress the odd-order harmonics at the PD's output, as well as a stacked transistor to amplify the output signal. Tx and Rx chips, including pads, occupy a total area of only 0.83 mm\({}^{2}\). The PMF link can support a PAM-4 signal with 22 Gbps data transmission, and a PAM-2 signal with 30 Gbps data transmission, with a bit error rate (BER) of \(<\)10\(-\)12, with demodulation performed in real time. Furthermore, the energy efficiency for the link (Tx + Rx) is 4.1 pJ/bit, using digital data input and receiving PAM-2 output (5.6 pJ/bit for PAM-4).  
DHBT; energy efficient; InP; modulator; PAM; PD; PMF; polymer microwave fiber; power detector; pulse amplitude modulation; RF-DAC
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 3 Method
### Area Consistency Validation Method  
The combination of multi-source RS images has been widely used for real-time monitoring of the Yellow Sea green tides, but the classification results of different images in the same period and area are usually different. In order to use multi-source RS data uniformly for green tide extraction and study its growth curve prediction, it is necessary to evaluate the consistency from extraction results of different remote sensing data sources. However, due to the fast drift speed of green tide, which can reach roughly 1-5 km/day [33], multi-source RS data will have at least a few hours' intervals, which is enough to produce differences in green tide drift and deformation. Therefore, it is difficult to conduct pixel-level consistency validation, such as Kappa. So, we chose to validate the consistency by comparing the area of the results, as shown in Equation (4).  
\[Acc=\left(1-\left|\frac{S_{i}-S_{j}}{S_{j}}\right|\right)\times 100\% \tag{4}\]  
where Acc is the area consistency coefficient of green tide extraction results among each RS satellite data. \(S_{i}\) and \(S_{j}\) are the reference data and the sample data, respectively.
# A Method of Predicting Critical Derailment Speed and Analysis of Derailment Process
## 5 Derailment Process Analysis
### Wheel-Rail Attack Angle  
The change curve of the front wheel-rail attack angle of the vehicle at the critical derailment speed is shown in Figure 10. When the train runs into the transition curve, the wheel-rail attack angle fluctuates. When the vehicle runs to the latter half of the transition curve on group A lines, the wheel-rail attack angle of the front wheelset increases suddenly. The maximum value of the wheel-rail attack angle is about 0.065 rad. The fluctuation amplitude of the wheel-rail attack angle reduces when the vehicle runs on the line of group B, and the value return to 0 rad gradually. After the train runs into the circle curve whose radius is 800 m, the train derails. On the other two lines in group A, the wheel-rail attack angle reduces to 0.005 rad quickly and changes with a large amplitude. With the vehicles  
Figure 8: Wheel lifting height at inner rail side.  
Figure 9: Wheel lifting height at outer rail side.  
running on group B lines, the wheel-rail attack angle stabilizes to 0 rad when the train runs into the circle curve.  
Figure 11 shows the change curve of the rear wheel-rail attack angle of the vehicle under the critical derailment speed. On groups A and B in the transition curve, the train's rear wheel-rail attack angles have a large change but will not produce abrupt changes.  
Before reaching the critical derailment speed, the train on group B line runs unstably with the process of speed increasing. In order to study whether the wheel-rail attack angle of the train changes abnormally or not, the train runs on the group B line at a nonlinear critical speed. The relationship of the attack angle between the front wheel and the track is obtained and is shown in Figure 12.  
Figure 11: Changing curve of the rear wheel–rail attack angle.  
Figure 10: Changing curve of the front wheel–rail attack angle.  
It can be seen that the train that runs on the line of the 1400 m radius derails, but the other two trains do not derail. After the trains enter the transition curve, the attack angle of the front wheel pairs of the train running on group B lines shows similar change trends as that of group A at the critical derailment speed. The maximum value of the attack angle of group B is 0.06 rad.  
It can be seen that when the speed is lower than the nonlinear critical speed, but the attack angle of the front wheel rises rapidly to 0.06 rad, the vehicle must derail. When the speed of the vehicle is larger than the nonlinear critical speed, the wheel-rail angle attack changes suddenly. The vehicle may not derail, but it will be in a critical unstable state. When the speed reaches the critical derailment speed, the wheel-rail attack angle will not change suddenly. Therefore, detecting the abnormal change of the wheel-rail attack angle of the train is an effective means to monitor the derailment tendency of the train. When the attack angle reaches 0.06 rad, timely measures should be taken to avoid derailment accidents.
# Cascaded Frequency Selective Surfaces with Matryoshka Geometry for Ultra-Wideband Bandwidth
## 5 Conclusions
The proposed cascaded FSS with matryoshka geometry enabled the use of two compact, single-unit cell FSSs of 24 mm\({}^{2}\) with a spacing of 12 mm to provide both a 2 GHz bandwidth in the range from 1.78 GHz to 3.82 GHz and a narrower bandwidth of 1.2 GHz, in the range from 2 GHz to 3.2 GHz with a spacing of 4 mm. Data conceptualization and writing--original draft preparation, I.C. ; writing--review and editing, F.M. and W.Q. ; fabrication and measurement, I.C. ; data analysis, I.C., F.M. and W.Q. ; supervision, W.Q. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the Brazilian Coordination for the Improvement of Higher Education Personnel CAPES--Finance Code 001. Data Availability Statement: The data presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to [EMAIL_ADDRESS]. The authors declare no conflicts of interest.
## 7 RF-EH Circuit  
Although RF power is always present in the surrounding environment, the power density is generally low. In fact, regular RF power harvesting systems have small RF-to-DC PCE, notably when the harvested radio frequency power is limited. The small, converted energy is difficult to apply due to the presence of various losses and limitations in all parts of the RF power harvesting system. Hence, the design of each module must be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Environment**} & \multirow{2}{*}{**Band**} & **Number of** & **Maximum PIN,** & \multirow{2}{*}{\(\mathbf{S_{BA}}\) **Threshold**} \\  & & **Stations** & **dBm** & **nW/cm\({}^{2}\)** \\ \hline \multirow{3}{*}{Urban} & GSM-900 & 8 & \(-\)19.2 & 230 \\  & GSM-1800 & 7 & \(-\)31.3 & 450 \\  & DTV (during & & & \\  & switch over) & 10 & \(-\)34 & 40 \\  & GSM-900 & 2 & \(-\)22.3 & 230 \\ Semi-urban & GSM-1800 & 3 & \(-\)43.5 & 450 \\  & DTV (during & & & \\  & switch over) & 0 & \(-\)37 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summarizes the RF measurements in urban, semiurban, and rural areas [26].  
well-developed to ensure the whole system becomes feasible [76]. A typical RF-EH circuit is composed of an antenna, an energy conversion module, a matching network, and a load.  
A rectifier, RF input filter, antenna, and impedance matching network were all part of the rectenna system design. The RF energy is detected and received by the antenna, and then converted to a DC signal by the rectifier. The RF input filter, also known as the \"pre-rectifier,\" suppresses the harmonics generated by a nonlinear component in the rectifier. The power transfer is maximized by the matching network between the rectifier circuit and the input filter. Regardless of the application, the fundamental operation of a rectenna system is the same [77]. Based on the taxonomy shown in Figure 2, this study looked at the main parts and structure of research on radio frequency energy harvesting.