esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### High Throughput Data Analysis  
30 million pair-end reads per sample were assessed with 101 bp read length. Reference P1 phage genome sequence (NC_005856.1) and annotations were downloaded from GenBank. Quality and adapter trimming of the short reads was performed using Trimmomatic [65]. Short reads matching known rRNA sequences were removed using the HISAT2 aligner [66]. Read quality reports before and after quality filtering were prepared using the FastQC software v0.11.7 [67]. Filtered reads were aligned to the reference genome using Burrows-Wheeler Aligner with the selected BWA-MEM algorithm [68]. The Sambamba software was used for BAM file processing [69]. Read mapping reports were created using the Qualimap software [70]. RSEM (RNA-Seq by Expectation Maximization) [71] was used to quantify the expression values of genes. Additionally, Salmon [72] was used to quantify the expression values of genes (not used in further analysis). Hierarchical clustering of RNA-seq samples (Pearson correlation metric, centroid linkage) based on the expression values of all genes was performed using standard R functions (R Core) and variance stabilizing transformation was provided by the DESeq2 package [73]. Differential expression analysis between designated groups of samples was performed using the voom+limma pipeline [74]. The false discovery rate (FDR) threshold of 0.01 and a fold change threshold of 1.5 were used in the analysis. Gene Set Annotation (GSA) was done using GSAn 1.0.5, a public web server for characterizing gene lists of high-throughput genomics [75]. Glimma package [76] was used to provide interactive graphics--Interactive HTML Volcano plots, Interactive HTML MA plots (Supplementary_plots_Volcano_MA.rar). GSA is a tool that uses semantic similarity and it is based on the IC (Information Content) proposed by [PERSON] and [PERSON] [77]. Detailed analysis of gene expression and GSA analysis can be found in Supplementary Materials (Table S1). The RNA-seq data have been deposited at the NCBI's Gene Expression Omnibus [78] and are accessible through GEO Series accession number GSE173614.
## 1 Introduction
When low-voltage fault occurs in the grid, the PMSG-based wind turbine cannot transmit active power to the grid effectively, which increases the DC-link voltage and causes damage to the wind turbine [21]. In order to suppress the DC-link overvoltage, a chopper [22] or crowbar [23] can be connected in the DC side of the PMSG-based wind turbine, and the excessive power can be consumed by the passive resistor. It is also feasible to add the energy storage device to the DC side [24,25], which can absorb the excessive energy and maintain the DC-link voltage within the feasible range. In addition, [26] points out that the abrupt change of the grid voltage during the short-circuit fault will also cause frequency instability and endanger the synchronization of wind turbines. These above-mentioned studies [21, 22, 23, 24, 25, 26] on the LVRT are carried out with the wind turbine under the conventional CS control, whereas the short-circuit characteristics of the VS-controlled wind turbine [20] are unknown. Therefore, it is urgent to study the short-circuit characteristics of wind turbines under the VS control and present the corresponding LVRT control strategy. With respect to the PMSG-based wind turbine under the VS control strategy proposed in [20], this paper adds energy storage batteries on the DC side to realize the functions of primary frequency regulation and LVRT, and the corresponding control strategies are presented at the same time. The overcurrent can be suppressed by switching in the virtual resistor in the control loop of GSC during the grid fault. On the basis of analyzing the dynamic characteristics of the VS-controlled wind turbine when the short-circuit fault occurs, this paper proposes a design method of the virtual resistor parameter. At last, based on the PSCAD/EMTDC simulation platform, the feasibility of the control strategy and the correctness of the theoretical analysis are verified. The remainder of this paper is organized as follows. Section 2 introduces the kinetic similarity between the SG and the GSC. Section 3 presents the control structure of the VS-controlled wind turbine with the additional energy storage. Section 4 analyzes the transient current characteristics of the VS-controlled wind turbines after short-circuit fault. Section 5 proposes a design method of the virtual resistor parameter. Section 6 provides simulations based on PSCAD/EMTDC. Section 7 concludes this paper.
# A New Tooth Profile Modification Method of Cycloidal Gears in Precision Reducers for Robots
## 4 Meshing Contact Characteristics of the Modified Profile
### Transmission Error of the Modified Profile  
Theoretically speaking, in the cycloid-pin transmission, when the pin gear rotates by a certain angle \(\phi_{1}\), the cycloidal gear rotates by a corresponding angle \(\phi_{2}\). The ratio of \(\phi_{1}\) to \(\phi_{2}\) is called the theoretical gear ratio. However, the actual transmission ratio of cycloid-pin gear pair is not equal to the theoretical one because of the influences of various factors such as profile modification, manufacturing error and force deformation of the cycloidal gear. The difference between the actual and theoretical output angles of the cycloidal gear is called transmission error. The transmission error is an important index for evaluating the profile modification quality. Its existence directly affects the meshing performance of the cycloid-pin gear and causes certain shock and vibration [12; 20; 22]. However, the profile shape of the cycloidal gear has a great influence on the transmission error and the lost motion.  
The transmission error can be expressed as:  
\[\delta=(\phi_{2}-\phi_{20})-z_{p}/z_{c}\mathbf{\cdot}(\phi_{1}-\phi_{10}), \tag{11}\]  
where \(\phi_{10}\) and \(\phi_{20}\) are the initial rotation angles of the pin gear and the cycloid gear, respectively.  
According to the pressure angle variations shown in Figure 3, the modification at the position where the pressure angle is the smallest should also be the smallest. Generally, the position is used as the reference point for modification and its transmission ratio at this position is approximately equal to the theoretical transmission ratio.  
The process of solving transmission error is shown in Figure 8. Firstly, according to the variation trend of the pressure angle, the parameter \(\varphi_{0}\) of the modification reference point of the cycloidal gear can be determined. By solving Equation (10), the initial engagement angles \(\phi_{10}\) and \(\phi_{20}\) of the pin gear and the cycloidal gear at the modification reference point can be obtained. Secondly, by changing the pin gear angle \(\phi_{1}\) with a certain step size \(\Delta\phi\) (\(\phi_{1}=\phi_{10}\pm n\Delta\phi\), where \(n\) is the number of iterations) and substituting \(\phi_{1}\) into Equation (10), a series of corresponding angles \(\phi_{2}\) of the cycloidal gear can be calculated. Finally, the transmission error curve can be obtained by substituting meshing contact parameters \(\phi_{10}\), \(\phi_{20}\), \(\phi_{1}\) and \(\phi_{2}\) into Equation (11).
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Advanced Antenna Designs for High-Performance Satellite Communication Systems
#### 3.6.5 Example Designs  
A lot of research has been performed to advance antenna technology in demanding satellite applications. In the following subsections, some innovative designs are discussed. A summarized version of this discussion can be found in Table 11.  
Figure 4: Antenna terminology diagram. A novel design utilizing the capabilities of a reflector is proposed by ref. [118]. The design is a dual-polarized, horn-fed reflector with an angle-insensitive reflective metasurface element in its middle, which ensures an effective phase compensation across a variety of incident angles. The proposed and tested idea shows that the design can achieve high gain at 48 dBi and a low-SLL of \(-32\) dB, making it suited for satellite LEO applications in the X-band. Waveguide antennas, on the other hand, can be more compact and low-profile constructions, such as the proposed designs in refs. [119; 123]. A fully metallic low-profile antenna in the Ka-Band consisting of 2 PPWs (ref. [119]), one for beamforming and one for control radiation, can achieve high gains for a variety of angles, as well as a beam scanning range from \(-60\) to \(+60\) degrees, with stable sidelobe levels below 17 dB. With the elimination of dielectric materials, the cell achieves an increased efficiency of 67%. In addition, the
# The Role of Sensory Cues in Collective Dynamics: A Study of Three-Dimensional Vicsek Models
## 2 Methods
### System dynamics  
The 3D Vicsek model consists of \(N\) particles that move within a cubic domain of side length \(L\), with the average particle density given by \(\rho=N/L^{3}\). The model assumes that each agent travels at a constant speed, and the domain has periodic boundary conditions. In the original 3D Vicsek model, every particle has a spherical sensing region with a radius \(R\)[23]. Particles within this radius, including the particle itself, are considered its 'neighbors'. During each discrete-time step, a particle updates its heading direction to align with the average direction of its neighbors.  
The position of particle \(i\) at the time step \(k\) is given by the vector \(\mathbf{x}_{i}(k)\in\mathbb{R}^{3}\), and its direction of motion is represented by the unit vector \(\mathbf{v}_{i}(k)\in\mathbb{R}^{3}\). The heading direction of this particle \(i\) at the next time step \(k+1\) is updated as follows:  
\[\mathbf{v}_{i}(k+1)=\mathcal{N}\left(\mathcal{N}\left(\sum_{j\in\Lambda_{i}(k )}\mathbf{v}_{j}(k)\right)+\boldsymbol{\xi}_{i}(k)\right), \tag{1}\]  
where \(\mathcal{N}(\mathbf{u})=\mathbf{u}/\|\mathbf{u}\|\) represents the unit vector in the direction of \(\mathbf{u}\), and \(\Lambda_{i}(k)\) is the set of neighbors for particle \(i\) including itself that lie within a sphere of radius \(R\) centered at \(\mathbf{x}_{i}(k)\). The vector \(\boldsymbol{\xi}_{i}(k)\) introduces intrinsic noise to the heading direction, deviating it from the average neighbors' heading, and is uniformly sampled from a sphere with a radius of \(\eta\). Finally, the position update for particle \(i\) is given by:  
\[\mathbf{x}_{i}(k+1)=\mathbf{x}_{i}(k)+v_{0}\mathbf{v}_{i}(k+1), \tag{2}\]  
where \(v_{0}\) represents the constant speed, assumed to be the same for all particles at all times.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 4 Real-Time Experiments and Results
### Signal Processing in the Controlled Implementation  
This section describes in detail the processing and parameters used in the controlled implementation processing. Figure 17 shows two added modules (marked in pink color): (i) the first to add a complex Gaussian noise (AWGN, additive white Gaussian noise) noise in the time domain. This module permits modifying the SNR in the controlled implementation to have an environment to close to reality as much as possible. Noise is added to the signal perceived by the \(SU_{3}\) before processing it; (ii) the second is the block that stores the data and does the statistics to know the efficiency of this work in a real wireless communication environment.  
Figure 18 shows the structure of the artificial noise addition block, where there are three input parameters: (i) the \(P_{SUi}\) signal, which is the power with each device \(i\) transmits, (ii) the SNR value that the controlled experiment will have (this value is the same for each SDR device; see Table 3), and (iii) the complex signal \(r_{i}(n)=x_{li}(n)+jx_{Qi}(n)\) perceived by each \(SU_{i}\). The experimentation values for the controlled implementation are shown in Table 3. Each \(SU\) was sensed for 16.66 h at each SNR value, perceiving 2048 samples per frame for the HackRF One and 1024 samples per frame for the RTL-SDRs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(SU_{1}\) & \(SU_{2}\) & \(SU_{3}\) \\ \hline Device & HackRF One & RTL-SDR 0005 & RTL-SDR 0002 \\ \hline SNR values & \(-5\), \(-4\), \(-2\), \(-1\), \(0\), \(1\), \(2\), \(3\), \(4\), \(5\), \(6\), \(8\), \(10\), \(12\), \(14\), \(16\), \(18\), and \(20\) dB \\ \hline Rx frames per SNR value & 10,000 & 10,000 & 10,000 \\ \hline Samples per frame & 2048 & 1024 & 1024 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Controlled implementation parameters.  
Figure 17: Controlled implementation steps.  
The artificial noise addition block uses the following pseudocode to calculate the noise that will be added to the \(r_{i}(n)=x_{li}(n)+jx_{Qi}(n)\) signal:  
* Function Artificial_Noise_Addition (\(r_{i}(n)\), \(P_{SUli}\), SNR_value)
* Sigma = **float** (\(P_{SUli}/(10^{**}(\text{SNR\_value}/10))\))
* mu = 0
* real = **np.random.randn** ((len(pxx)))*(sigma**0.5) + mu
* imag = **np.random.randn** ((len(pxx)))*(sigma**0.5) + mu
* \(AWGN_{i}(n)\) = _real_ +_j*imag_
* \(r_{i}(n)=r_{i}(n)+AWGN_{i}(n)\)
* _return(\(r_{i}(n)\))_  
Lastly, the _Computing statistics and performance_ block stores the \(Occupation(R^{\prime}_{i-dBM}(k))\) signal that indirectly contains (i) the start and end edges of a transmission, which form the frequency windows, and (ii) whether these windows correspond to noise or a _PIU_ transmission. Subsequently, the signal transmitted by each \(PU_{i}\) is compared to the signal received by each \(SU_{i}\).
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 2 Ad Hoc Separation Conops  
The concept of the ADSM refers to the application of a variable separation value for each aircraft pair within the same volume of airspace, depending on a set of factors, namely: aircraft models, aircraft masses, aircraft speed, encounter geometry (intersection angle), avoidance maneuver (AM), FL, Human Factor (HF), and wind conditions. These separation minima values must always be greater than the corresponding en-route minimum wake vortex separation obtained as an input from [28]. The CONOPS of Ad Hoc separation was developed in previous work. For a better understanding of this, readers are referred to [29]. This section provides a brief explanation of the Ad Hoc CONOPS.  
In the first stage of the development of this concept, ADSM are only determined for the horizontal dimension. The main feature of this new mode of operation is that there is a set of possible separation minima values within the range of 3 Nautical Miles (NM)-5 NM. Therefore, this concept not only implies applying different separation minima values depending on the situation, but also reducing the separation minima wherever possible. The possible Ad Hoc separation values within the defined range are 3 NM, 4 NM, or 5 NM. At further stages of the concept's development, other separation values could be applied with a higher resolution (i.e., 3.5 NM). These extreme values (3 NM-5 NM) are considered for the following reasons:  
* Upper limit value (5 NM): currently, in en-route scenarios with surveillance, the defined horizontal separation minimum is 5 NM.
* Lower limit value (3 NM): in en-route phase when the preceding aircraft belongs to an equal or lower category than the succeeding one, the MWS separation may be smaller than 5 NM [28]. There are several projects which have studied or are studying the reduction of MWS and proposing new en-route MWS values: [28; 30; 31]. Therefore, it was decided to take the value of 3 NM as the lower limit of the range of variable separations.
# Experimental Study on the Efficacy of Water Infusion for Underground Mining of a Coal Seam
## 1 Introduction
With all the above effects, the lateral movement of coalfaces can be reduced and finally, the stability of the underground soft coal mining can be enhanced and the mining velocity can be sped up. In order to prove the abovementioned viewpoints, a comprehensive experimental study should be performed. In this research, a series of tests (both in the laboratory and field) were carried out to explore the efficacy of the water infusion method for underground mining coalfaces, focusing on the new aspects of longwall face failure depth (i.e., spalling depth), which is the key factor for assessing the stability of coalfaces in underground mining construction, and the mining speed. Various aspects of moisture content, specific gravity, unit weight, permeability, and strength properties with respect to moisture content and time were first assessed in the laboratory to evaluate the appropriate parameters (e.g., water injection time and the optimum moisture content) for water infusion works in the field. Subsequently, for the field test, the spalling depth (i.e., longwall face failure depth) and mining velocity were monitored successively at various actual coalfaces for both case studies (with and without using the water infusion method). The efficacy of this method was evaluated based on a comparison of the aspects of spalling depth and mining speed for both case studies.
## 3 Results and Analysis
### Mechanical Parameters  
The thrust to the sliding body can be measured by the mechanical transducer between the push plate and the iron box, and the pressure on the ring dynamometer is measured to indirectly represent the axial tension within the anchor cable. Figure 4 shows the thrust and the tension curve. Before 70 s, the thrust to sliding body fluctuates between 0-100 N, which is relatively smooth. The axial tension within anchor cable is at a low level, which indicates that the force from the pedestal to the ring dynamometer is not significant in this period. After 70 s, the thrust to sliding body increased rapidly and reached the peak value at around 85 s. The axial tension curve shows a similar pattern to the thrust. A small rise of the axial tension occurs at 70 s, and a large jump occurs at around 85 s. On the whole, the change trend of the two mechanical parameters is relatively synchronous, which preliminarily suggests the rationality and effectiveness of the axial tension measurement using the proposed flexible device. The axial tension within the anchor cable can indirectly reflect the change trend of the thrust to the sliding body, which is of great significance for understanding the stress state of the slope.
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Sample Preparation
#### 4.1.3 STXs  
STXs are universally prepared using acidic or buffered extractions owing to their chemical instability under basic conditions. As these are a hydrophilic class of toxins, the extraction solvents used have generally been acidic, typically aqueous acetic [55; 56; 57] or hydrochloric acid [58], with buffered water being used in one instance [59]. Elevated temperatures are frequently used to extract STXs, including boiling of the solvent/sample mixture to facilitate extraction efficacy. Interestingly, in one study [59], acidic water and acidic methanol:water mixtures were evaluated and found to produce significantly lower recoveries of STX specifically, while the use of neutral pH phosphate buffer increased recoveries to >50%, which they speculated was due to reduced solubility in low pH.
# BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets
## 4 Experimental Analysis
### Different Scenario
#### 4.2.2 Different Heart Rate  
Performances for the change of heart rate are collected in Table 7. Both identification and verification presented a huge decrease in performance for one-day enrollment in Table 7. In contraposition to those results in Table 6, this verification data belongs to a different acquisition day, adding extra variations that may not be related to the heart rate. However, comparing results in the same scenario in Table 5 with those in Table 7 help in the assumption that the performance decrease is due to the heart rate variation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset** & \multicolumn{2}{c}{**S2**} \\ \hline  & **Visit** & \multicolumn{2}{c}{**D1V2**} \\ \hline  & **d** & **Metric** & **Accuracy (\%)** & **EER (\%)** \\ \hline  & **0.3** & 37.59 & 3.64 \\ One-day enrollment & **0.5** & 67.32 & 5.45 \\  & **0.7** & **68.8** & **3.64** \\  & **0.9** & 63.78 & 4.54 \\ \hline  & **0.3** & 80.42 & 1.82 \\ Two-days enrollment & **0.5** & 73.88 & 3.64 \\  & **0.7** & 79.45 & 1.99 \\  & **0.9** & **85.14** & **1.28** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Identification and verification results for scenario S. The best considered options for one-day and two-days enrollment are in bold font.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset** & \multicolumn{2}{c}{**S2**} \\ \hline  & **Visit** & \multicolumn{2}{c}{**D2V2**} \\ \hline  & **d** & **Metric** & **Accuracy (\%)** & **EER (\%)** \\ \hline  & **0.3** & 42.91 & 11.43 \\ One-day enrollment & **0.5** & 44.36 & 9.83 \\  & **0.7** & **50.62** & **7.27** \\  & **0.9** & 29.26 & 14.16 \\ \hline  & **0.3** & **59.91** & **3.64** \\ Two-days enrollment & **0.5** & 58.58 & 3.64 \\  & **0.7** & 56.28 & 5.45 \\  & **0.9** & 58.04 & 3.90 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Identification and verification results for scenario Ex. The best considered options for one-day and two-days enrollment are in bold font.  
On the other hand, Table 7 presents results with two-days enrollment. Results were still not good in terms of identification, remaining below 60%. However, in verification, EERs decreased noticeably. It was proved that adding an extra day of information for training improves the system noticeably in terms of verification, once it was compared to Table 7. In \(d=0.9\) EER dropped almost 10% and close to 7% in the case of \(d=0.3\).
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 3 Performance Analysis
### Successful Transmission Probability (\(Stp\))  
In this subsection, we derive the STP expression for both WiFi and mmWaves communication systems. The STP general expression can be written as follows [36]:  
\[STP=Pr\Big{\{}SNR>=\gamma_{{}_{th}}\Big{\}}, \tag{16}\]  
where \(\gamma_{{}_{th}}\) presents the minimum required \(SNR\) value to successfully decode the transmitted signal at the receiver.
## 5 Data Analysis
### MLP Training, Validation, and Testing  
The results of MLP network training, validation, and testing over separate datasets along with the Mean Squared Error (MSE) and the Root Mean Squared Error (RMSE) are represented in Figures 8-11. The correlation of original data with predicted data is also shown in those graphs.  
In the following, the results of the proposed methodology are presented for two different cases: (1) modelling in the presence of the outliers and (2) modelling after removing the outliers. The condition NOx > 3 \(\times\) STD (standard deviation) was used as a criterion to reject outliers from the dataset. In this research, as was noted before, 70% of the data were used as the training dataset, 15% for validation, and the remaining 15% for testing.