esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 CEMs--State of the Art
### Nitrogen-Rich Organic Ligands  
According to literature reports, the ligands most typically used to produce coordination energetic materials are hydrazine, aliphatic amines, heterocyclic compounds, and mixed-functionality compounds, such as heterocyclic compounds functionalized with amine groups (Table 1). Many authors [4] indicate the dependence of performance parame  
Figure 1: CEM synthesis reaction example using iron(III) nitrate(V) and an alkylenediamine.  
ters of CEMs on the content of nitrogen atoms in the ligands composing the CEMs. It should be noted, however, that such structure-property relationships are typically established within a narrow subgroup of ligands that are similar in terms of their chemical structure.  
From the perspective of coordination energetic materials, high nitrogen content in the ligand molecule is desirable, due to the increased nitrogen/carbon dioxide ratio in the decomposition products of CEMs. High nitrogen content also reduces the emission of the toxic carbon monoxide in the case of CEMs, whose decomposition requires the consumption of significant amounts of oxygen from air (Figure 2). High nitrogen content is also associated with increased density [5] and heat of formation of the CEM. Increasing these two parameters has a direct, positive effect on the parameters of detonation of the CEM [6].  
Ethylenediamine is a frequently used bidentate ligand that contains 46.61 wt. % nitrogen. CEMs containing ethylenediamine ligands typically show good thermal stability and average sensitivity to mechanical stimuli [7]. Hydrazine is another commonly used bidentate ligand, containing a remarkable 87.42 wt. % nitrogen. CEMs containing hydrazine ligands typically show an affinity to decompose via detonation and exhibit mechanical sensitivity comparable to that of initiating EMs [8; 9].  
Among other works utilizing ligands that are aliphatic compounds, it is worth mentioning the use of oxidized triaminoguardiines, which contain 83.97 wt. % nitrogen. Literature reports state that CEMs containing such ligands are practically insensitive to mechanical stimuli, exhibit high thermal stability and high density, and are predicted to have detonation velocities in the order of 5-9 km/s [10].  
Heterocyclic compounds, such as triazoles, tetrazole, and their derivatives, are another group of organic ligands. Such ligands contain from approx. 40 up to even 80 wt. % nitrogen and exhibit a great variety of properties [11]. CEMs containing fulminate anions are interesting examples of compounds containing such heterocyclic ligands [12].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Chemical Structure** & **Nitrogen Content [wt. \%]** & **Name Abbreviation** & **Ref.** \\ \hline
1 & 87.42 & Hz & [7] \\
2 & 83.97 & OxTANQ & [10] \\
3 & 73.66 & bisTetrAzM & [12] \\
4 & 66.64 & 4A124 TrAz & [12] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected organic ligands reported to be used to produce CEMs, sorted by their nitrogen atom content (by weight).  
Figure 2: Structure of selected organic ligands reported to be used to produce CEMs.
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 5 Evaluation
### SCS-HK with Self-Interference Cancellation
When using either 24 dBm or 26 dBm of jamming power, the impact of the SCS-HK scheme is obvious. However, once again, it is not possible to strictly say which combination is the best, since the choices present trade-offs between the packet loss and data errors that we accept on [PERSON] and the received packets and correct data that we allow [PERSON] to collect. The most interesting combinations are addressed below. Fixing the same order of magnitude on [PERSON]'s BER (\(10^{-5}\)), it is important to mention that the combination of 26 dBm jamming power and 44 bits punctured significantly outperforms the 24 dBm jamming power with 65 bits punctured, achieving \(3.54\times 10^{-1}\) and \(1.10\times 10^{-1}\) on [PERSON]'s BER, respectively. This means the SCS-HK scheme and self-interference/jamming are two complementary approaches to increase the security level for wireless networks. In conclusion, it is possible to secure a communication only using the noise generation; however, when combined with the SCS-HK scheme, for a selected combination of parameters (i.e., number of bits punctured and jamming power) it is possible to increase [PERSON]'s BER with only a minor effect on [PERSON], thus improving the security level.
## 3 Results
### Subcellular Localization of GLR3.3 and GLR3.5  
Tomato _GLR3.3_ and _GLR3.5_ are homologous to Arabidopsis _GLR3.3_ and _GLR3.6_, respectively [9], which have been reported to participate in response to nematode attack and cold tolerance in tomato. To determine the subcellular localization of tomato GLR3.3 and GLR3.5, GLR3.3-GFP, GLR3.5-GFP, and GFP were transformed into _Agrobacteriumium tunefaciens_, and then transiently expressed in _N. benthamiana_. Confocal microscopy of leaf  
Figure 1: DNQX treatment increases the susceptibility of tomato to _B. cinerea_. Four-week-old tomato plants were treated with 1 mM DNQX or H\({}_{2}\)O, 12 h prior to inoculation with _B. cinerea_: (**a**) pseudocolor images of tomato leaves showing \(\Phi\)PSII at 3 dpi; (**b**) quantitative values of \(\Phi\)PSII at 3 dpi; (**c**) transcript abundance of _B. cinerea actin_ in tomato leaves inoculated with _B. cinerea_ at 1 dpi. The results in panels (**b**,**c**) are presented as mean values \(\pm\) SD; \(n\) = 4. Statistically significant differences between treatments (_p_ < 0.05, Tukey’s test) are shown by different letters. The experiments described above were carried out three times, with the same findings each time.  
epidermal cells showed that the control GFP signal exhibited a localization in the cells' membranes, cytosols, and nuclei (Figure 2). The fluorescent signals of GLR3.3-GFP and GLR3.5-GFP were colocalized with FLS2-mCherry at the plasma membranes of the cells. It was not observed that GLR3.3 and GLR3.5 colocalized with histone 2B-mCherry at the nuclei. The results suggest that tomato GLR3.3-GFP and GLR3.5-GFP localize at the plasma membrane of a cell.
# Making Shopping Easy for People with Visual Impairment Using Mobile Assistive Technologies
## 2 Research Methodology  
In order to identify most of the available MAT solutions for PVI, we searched the following databases: Springer, Science Direct, Web of Science, Institute of Electrical and Electronics Engineers (IEEE) Xplore, Google Scholar, Association for Computing Machinery (ACM) Digital Library and Microsoft Academic. We used the following keywords to search for peer reviewed journal articles: (\"Assistive technology\" OR \"Assistive technology devices\" OR \"Mobile assistive technology devices\" OR \"navigation solution\" OR \"shopping\") AND, (\"visual impairment\" OR \"blind \"), (\"avoiding obstacles\" OR \"write \" notes\" OR \"text to speech\") AND (\"visual impairment\" OR \"blind \"). We set the search period to articles published between January 2010 and December 2018. The search query returned 8893 records. Duplicates were removed, reducing the search results to 842 articles. Then, we eliminated 433 results by restricting to articles in the English language, articles that describe research intervention for PVI based on their titles, and articles that are free and downloadable. Next, all keywords were screened, which eliminated 206 articles, because they were not technical papers, or they were literature reviews or surveys.  
Abstracts of the resulting 203 papers were then screened for relevance to our research goals. One hundred and thirteen of the articles were deemed inappropriate because they did not study visual impaired or blind populations, or they were not related to MAT. Next, two different researchers conducted a full text article review of the remaining 90 articles. Forty-six articles were eliminated due to not helping PVI in the shopping process and avoiding obstacles. The resulting 44 articles met all inclusion criteria and were evaluated in this study. Figure 1 shows the flowchart of choosing methodology based on PRISMA flowchart [35].
# A FBG Intensity Modulation System Combined with an Optical Whispering Gallery Mode Edge Filter
## 5 Conclusions  
In this study, we utilized a FBG sensor and WGM edge filter to construct an intensity modulation system. Additionally, we interrogated and measured the vibration strain signals in order to compare them with the opto-electrical signals recorded from the FBG sensor combined with the WGM edge filter to verify the feasibility of the intensity modulation system. When the PZT was vibrating at different frequencies, the FBG sensor adhered to the PZT would fluctuate and the spectra of the resonant wavelength would be recorded and compared with the signals of the strain gauge. According to the results of the experiments, when the resonant wavelength of the WGM edge filter was 1535.10 nm, the filtration was better and the noise was lower within 100 Hz. Moreover, as the frequency of PZT was at 400 and 1000 Hz, a better SNR was measured at wavelength 1542.05 nm of the edge filter. The intensity modulation system consisting of a FBG sensor combined with a WGM edge filter thus exhibited the characteristics of precision and high sensitivity. It can be concluded that this intensity modulation system could potentially be used to measure high frequency mechanical vibration.  
This work was funded by the Ministry of Science and Technology, Taiwan (grant number MOST 103-2221-E-151-009-MY3).  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments, and analyzed the data; [PERSON] performed the experimental works and analyzed the data; [PERSON] analyzed the data and wrote the paper.  
The authors declare no conflict of interest.
# Detection and Treatment Methods for Perfluorinated Compounds in Wastewater Treatment Plants
## 3 Results and Discussion
### PFCs Removal Efficiency Through Pilot Test
#### Removal Variation by Activated Carbon Adsorption  
As shown in Figure 6, the PFNA removal at five minutes was 69.55%, which was the highest, 98.55%, at 15 min. The PFPeA removal at five minutes was 36.49%, which rose to the lowest (90.1%) at 15 min. The PFhxS removal at five minutes was 61.8%, which rose to 93.6% at 15 min. PFOA and PFhxA showed a 64.8% and 42.73% removal at 5 min of EBCT, which increased to 95.43% and 92.12% at 15 min of EBCT, respectively.  
We found that 50% to 60% of PFCs was removed by five minutes of EBCT, and over 90% was removed after 15 min. PFNA showed the highest removal at 98.5%, and PFPeA showed the lowest at 90.1%. Previous research reported that the longer the length of the carbon ring in PFCs, the greater the increase in the adsorption force to the activated carbon. When the lengths of the carbon ring are identical, the adsorption force of the activated carbon to sulfone substituent would be stronger than that to carboxyl substituent [14]. In this study, the removal of PFNA, which has a large number of carbons, was highest. For PFhxA and PFhxS in which the number of carbons was identical, more of PFhxS, which has a sulfone substituent, would be removed.  
The activated carbon adsorption test showed relatively higher removal, and since the surface of the activated carbon is hydrophobic, it is useful for removing hydrophobic PFCs.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## Appendix A EPFD Analysis for the Orbit Plane
Figure 11 shows the variation of the satellite EPFD with the elevation angle of the NGSO sat2, and Figure 11 depicts the variation of the off-axis angle of the GSO earth station. From the downlink EPFD distributions in Figure 11, the peak EPFD of the LEO system is dominated by the EPFD generated by one of the satellites. Combining these two pictures, we can find that the off-axis angle \(\varphi\) of the GSO earth station corresponding to the large EPFD peak is always close to zero degrees. When the off-axis angle of the GSO earth station is 0, the earth station, the LEO satellite and the GSO satellite are located in a straight line. In order to keep the downlink EPFD of the system within the EPFD limit, the LEO satellite operator needs to suppress the EPFD in the in-line interference scenario. Based on the above simulation results, an interference analysis model for a single satellite are proposed in Section 2. Ensuring that the EPFD limits are met in this scenario could ensure that the LEO satellite constellation does not cause harmful interference to GSO satellite systems.
# Workload Balancing on Identical Parallel Machines: Theoretical and Computational Analysis
## 2 Problem Description  
### Preliminary Notations and Assumptions  
The problem considered in this paper is described as follows: a set of \(N\) independent jobs \(J_{1},\ J_{2},\ldots,J_{N}\) are to be scheduled on \(M\) parallel machines. All machines or resources are identical, i.e., job \(j\) requires the same processing time on any machine. Each machine can process at most one job at a time, and each job must be processed without interruption by one of the \(M\) machines. All the jobs are assumed to be available at time zero and each job \(J_{j}\) has a deterministic integer processing time \(p_{j}\). The setup times, if any, are included in the processing time.  
To avoid trivialities, it is assumed that: \(N>M\geq 2\).  
A schedule is represented by an \(M\)-partition \(S=S_{1},S_{2},\ldots,S_{M}\) of the set jobs \(J\), where each \(S_{m}\), represents the subset of jobs assigned to the machine \(m\). The objective is to minimize the workload imbalance between the different machines.  
Since the machines are identical, we can assume, without loss of generality, that the indexes of the machine are rearranged in nondecreasing order according to the completion times (or workloads) \(C_{1}=C_{min}\leq C_{2}\leq\ \ldots\leq C_{M-1}\leq C_{M}=C_{max}\) as illustrated by Figure 1.  
The different notations used in this paper are described below in Table 1.  
Figure 1: Illustration of the workload imbalance on identical parallel machines.
## 2 Computational Methods
The EMTO orbitals, consecutively, consist of the spdf partial waves (solutions of the radial Schrodinger equation for the spherical OOMT potential wells) and the spdf screened spherical waves (solutions of the Helmholtz equation for the OOMT muffin-tin zero potential). The completeness of the muffin-tin basis was discussed in detail in [56], and it was demonstrated that for metals crystallizing in close-packed lattices \(l_{max}=3\) (spdf orbitals) leads to well-converged charge density and total energy. The generalized gradient approximation (GGA) is selected for the electron exchange and correlation energy functional [43]. Integration over the Brillouin zone is implemented while using a \(25\times 25\times 25\) grid of k-points resolved according to the Monkhorst-Pack technique [58]. The moments of the density of states, needed for the kinetic energy and valence charge density, are computed by integrating the Green's function over a complex energy contour (with 2.8-3.0 Ry diameter) while using a Gaussian integration technique with 40 points on a semi-circle encircling the occupied states. The EMTO method is coupled with the coherent potential approximation (CPA) in order to treat compositional disorder [59; 60]. The ground-state properties of the chemically random alloys are obtained from EMTO-CPA computations that include the Coulomb screening potential and energy [61; 62; 63]. The screening constants are obtained from supercell calculations adopting the locally self-consistent Green's-function (LSGF) technique [64] for a 1024 atoms supercell that represents the random equiatomic alloys. The \(\alpha\) and \(\beta\) screening constants (see Refs. [61; 62] for details) are found to be \(\alpha_{U}=\alpha_{Nb}=0.724\) and \(\beta=1.411\) for U-Nb alloys; \(\alpha_{U}=0.742\), \(\alpha_{Nb}=0.694\), \(\alpha_{Ti}=0.767\), and \(\beta=1.101\) for U-Nb-Ti alloys. The EMTO-CPA technique has been previously successfully utilized in order to define thermodynamic properties of the uranium-based metallic nuclear fuels, including: U-Zr, U-Pu-Zr, U-Mo, U-Ti, and U-Nb [65; 66; 67; 68; 69]. In order to ensure the accuracy of structural energetics of the EMTO method, it is compared with the more accurate FPLMTO method for elemental uranium, as described in the results section next.
## 6 Simulation Results
### Robustness against Common Signal-Processing Effects
#### 6.3.3 Robustness to Signal Compression  
Bio-signal records are usually of long duration, which can sometimes exceed 12 h. Consequently, these records occupy important storage space. For this reason, medical service practitioners may use compression methods to archive the bio-signal recordings.  
In the current test, the robustness of our method was tested against bio-signal compression. To perform this test, a mechanomyogram (MMG) signal was selected and then compressed by the method presented in [60] for different compression ratios (CRs). Next, our scheme was applied to the decompressed signal, and the BER values were computed and are reported in Figure 19. From the results displayed in this figure, it is noticeable that the BER value increased proportionally with the increase in CR. However, the BER values remained low and close to zero. Thus, our scheme shows a significant resistance against signal compression.
# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 3 Model Architecture
### Multiscale Backcast Convolution Neural Network  
#### 3.2.1 Dilated Multi-Scale Convolutional Layer  
After the preprocessing method described in Section 3.1, the traffic flow time series of two-dimensional multivariate data, initially composed of variable dimensions and time dimensions, was transformed into three-dimensional data composed of variable dimensions, frequency dimensions, and complex dimensions. We denote this object with \(\mathcal{W}_{k}\in\mathbb{R}^{N\times k\times 2}\), where 2 in the final dimension represents imaginary and real parts. Because the frequency domain representation of the time series records amplitude from low to high frequency, the different frequency components are not independent. In order to use  
Figure 1: Preprocessing method, where \(i\) is imaginary unit.  
as few parameters as possible to explore the dependencies between different frequency bands, we designed a multiscale convolutional feedback network for the frequency domain based on a two-dimensional convolution module. Multiscale convolution comes from the Inception Network [32], which uses multiple sizes of convolution kernels to extract features of different scales for the same set of feature maps, and then combines results across scales. This method can effectively improve the utilization rate of feature maps. Building on this approach, model parameters are further reduced by using dilated convolution [33] to enhance the receptive field of the convolutional kernel without increasing the number of parameters for convolutional kernels spanning multiple scales. The multiscale convolutional layer adopted in this paper is shown in Figure 2.  
In the figure, dilation represents the size of the dilation. Each multiscale convolutional layer contains three convolutional kernels of different sizes. Size variation is generated by the size of the dilation, not by the size of the convolutional kernel. By increasing dilation size, the receptive field of the convolutional kernel can be expanded without altering the number of parameters associated with the convolutional kernel. The three convolutional layers use padding operations to keep the feature map size of the input and output unchanged. After that, the features are normalized using the BatchNorm layer. By using LeakyRelu as the activation function, and by downsampling the feature map using maximum pooling, we obtain feature maps for the three different receptive fields. It should be noted that the dimensions of the three sets of feature maps are the same.
## 4 Mathematical Model
### Consideration of the Infrared Reflective Lining  
One of the tasks of this paper is the study of the radiation heat-flux reduction using the infrared reflective lining. In this paper we use a simple model based on the decomposition of the total heat flux into the radiation flux and the heat conduction flux: \(Q=q_{rad}+q_{cond}\). The radiation flux \(q_{rad}\) is calculated using the analytical solution for the hot screens (see [22], pp. 496) between the air layer and batting, neglecting the radiation absorption in the undershift and batting space:  
\[q_{rad}=\zeta\sigma(T_{skin}^{4}-T_{g}^{4})\]  
where \(\sigma\) is the Boltzmann constant, \(T_{skin}\) and \(T_{g}\) are, respectively, the temperatures of the skin and outer garment boundary, and \(\zeta\) is the total radiation resistance  
\[\zeta=\frac{1}{\frac{1}{\zeta_{1}}+\frac{1}{\zeta_{2}}+\frac{1}{\zeta_{3}}+ \frac{1}{\zeta_{4}}-2.0+\beta} \tag{10}\]  
where \(\zeta_{1}\) is the emissivity of the human body (see Figure 5), \(\zeta_{2}\) is the emissivity of the left side of the lining, \(\zeta_{3}\) is the emissivity of the right side of the lining, \(\zeta_{4}\) is the emissivity of the left side of the batting lining, and \(\beta=3.1\) is the empiric constant chosen from the condition that the skin temperature equals to 31 \({}^{\circ}\)C for the polyester batting of 3 cm thickness at the ambient temperature of \(-20\,^{\circ}\)C. In the simulations presented in Section 6.1 the emissivity \(\zeta_{2}\) is varied, whereas other parameters are assumed to be constant \(\zeta_{1}=\zeta_{3}=\zeta_{4}=0.9\). According to this decomposition, the Neumann boundary condition at the human body for \(\dot{q}_{cond}\) is reformulated as  
\[\dot{q}_{cond}=\dot{Q}-\dot{q}_{rad}\]  
where \(\dot{Q}\) is given. At the boundary between the garment and environment, the boundary condition reads:  
\[\dot{q}_{rad}-k_{ef}\frac{\partial T}{\partial x}=h_{t}\]  
The heat-exchange coefficient \(h_{t}\) can be taken for the turbulent flow over a plane surface with account for the radiation.
## 4 Automated Online Traffic Collection, Sample Construction, and Correlation
### Cross-Platform Traffic Collection and Sample Construction  
The three leading OS platforms are Windows, Linux, and Android. Therefore, A3C must take into account the specificity of these three operating systems to realize cross-platform traffic collection and sample construction.  
To solve the problem of sample purity, we delve into the mechanisms by which operating system processes generate network traffic. Processes have their own unique PID, and each process independently identifies its application traffic by the port number per unit of time. Therefore, we propose a cross-platform traffic collection and sample construction method based on the \"process-PID-port-flow\" graph.  
Moreover, no matter what the OS is, the application generates traffic in two behaviors: user-relevant behaviors and user-irrelevant behaviors. User-relevant behaviors represent how users interact with the application, and the application generates traffic during the interaction. User-irrelevant behaviors are spontaneous of the application, which produces network traffic.