esa-sceva/satcom-chunk-collection huggingface dataset:
# Radio Channel Scattering in a 28 GHz Small Cell at a Bus Stop: Characterization and Modelling
###### Abstract  
The 28 GHz band is one of the available bands in Frequency Range 2 (FR2), above 6 GHz, for fifth generation (5G) communications. The propagation characteristics at this frequency band, together with the bandwidth requirements of 5G communications, make it suitable for ultra-dense smart cell networks. In this paper, we investigate the performance of a radio channel in the presence of moving, scattering sources for a small cell at 28 GHz, located at a bus stop. To do so, measurements of the channel complex impulse response with a sweep time delay cross-correlation sounder were made and then used to examine the distribution of multipath components. Besides analyzing the delay spread caused by the channel, we also evaluate the impact on the Doppler spectrum (DS) caused by the vehicles passing near the bus stop. We show that delay components are grouped in clusters exhibiting exponential decay power. We also show that the DS varies with time as vehicles pass by, so the channel cannot be considered stationary. We propose an empirical DS model, where the model parameter should change with time to describe the non-stationary nature of the radio channel. We have also found that the DS with maximum spread is similar for channel contributions in different delay clusters.  
5G; millimeter waves; radio channel; scattering; measurements; non-stationary channel +
Footnote †: journal: Electronics  
Article
## 4 Interfacing with External Control Platforms
### System Model for Controller Design  
The state-space representation of the islanded system of Figure 6, in the abc-frame, is  
\[\left\{\begin{aligned} i_{t,abc}&=\frac{1}{R}v_{abc}+i_{L,abc}+C\frac{dv_{abc}}{dt}\\ v_{t,abc}&=L_{t}\frac{di_{t,abc}}{dt}+R_{t}i_{t,abc}+v_{ abc}\\ v_{abc}&=L\frac{di_{L,abc}}{dt}+R_{t}i_{L,abc}\end{aligned}\right. \tag{5}\]  
where \(v_{t,abc}\), \(i_{t,abc}\), \(i_{L,abc}\), and \(v_{abc}\) are \(3\times 1\) vectors comprising the individual phase quantities of Figure 6.  
The state-space model in the abc frame, _i.e._, Equation (5), is then transformed to a rotating reference frame [39]. The state-space model in the dq frame is thus given by:  
\[\left\{\begin{aligned} \dot{x}(t)&=Ax(t)+Bu(t)\\ y(t)&=C_{m}x(t)\\ e(t)&=y(t)-y_{ref}(t)\end{aligned}\right. \tag{6}\]  
where \(x=[V_{d}\;\;V_{q}\;\;I_{td}\;\;I_{tq}\;\;I_{Ld}\;\;I_{Ld}]^{\prime}\) is the vector of state variables, \(u=[V_{td}\;\;V_{tq}]^{\prime}\) is the input vector, \(y=[V_{d}\;\;V_{q}]^{\prime}\) is the output vector, \(y_{ref}\) is the reference input, e is the error in the system, and \(A\), \(B\),  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \(\mathbf{V_{dc}}\) & \(\mathbf{R_{TL}}\) & \(\mathbf{L_{TL}}\) & \(\mathbf{R}\) & \(\mathbf{C}\) & \(\mathbf{L}\) & \(\mathbf{R_{L}}\) \\ \hline
46 kV & 0.8 \(\Omega\) & 158 mH & 76 \(\Omega\) & 62 \(\mu\)F & 111.9 mH & 0.1 \(\Omega\) \\ \hline \end{tabular}
\end{table}
Table 1: Case I: System parameters.  
Figure 6: Schematic diagram of the islanded system and its controller.  
are constant matrices [40]. The open-loop system (6) includes two control inputs, _i.e._, \(m=2\), and two outputs to be controlled, _i.e._, \(r=2\). Reference signal \(y_{ref}\) belongs to the class of step signals.
## 3 The Proposed Method
### Information Difficulty Extraction Module
#### 3.1.3 Position Encoding  
Sequence processing in the attention mechanism relied on location encoding and the location information of the contained sequence elements. The position encoding of the model in this paper used an absolute position-encoding method, that is, the position encoding of the sine and cosine functions [21]. For a length \(\mathsf{d}\) at a position pos in the sequence, the value of dimension \(\mathsf{i}\) in the position-encoding vector is as follows:  
\[\mathrm{PE}_{\mathrm{pos,2i}}=\sin\left(\frac{\mathrm{pos}}{10,000^{\frac{2}{ 4}}}\right) \tag{4}\]  
\[\mathrm{PE}_{\mathrm{pos,2i+1}}=\cos\left(\frac{\mathrm{pos}}{10,000^{\frac{2} {4}}}\right) \tag{5}\]  
where \(\mathsf{i}\in(0,1,2\ldots\mathsf{d}/2)\). After generating the location code, it was combined with the embedded representation vector in an additive manner.
## 4 Conclusions  
We have presented a novel SVM-based anomaly detection algorithm for text-based protocols. In addition to using n-grams, we propose to generate additional features from message properties. A subsequent attribute reduction step then reduces the number of features to a reasonable set. In our evaluations for the SIP protocol, our algorithm achieves lower FPR and higher throughput compared to previously known algorithms.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 3 Passband Compensation of Chebyshev Sharpened Combs from [8; 10; 11]
### Coleman Chebyshev Sharpened Comb [8]
#### 3.2.1 Narrowband Compensation  
We consider \(\omega_{p}=0.22\pi/M\) and the compensator \(C_{1}\) in (3). Using PSO in MATLAB, we obtain \(C_{110}=0.9089\) resulting in the optimum passband deviation of \(\delta_{o}=0.0141\)dB. Next, \(C_{110}\) is presented in the SPT form varying the number of adders from four to seven, as shown in Table 1. It can be observed that we have approximately optimum compensation with seven adders, denoted in bold in Table 1.  
Figure 1a compares overall magnitude responses of non-compensated Coleman filter and compensated with the optimum compensator (3). The compensation slightly deteriorates the stopband, as shown in Figure 1a. Figure 1b compares passbands of the [PERSON] non-compensated filter, compensated with the optimum filter and compensated with the SPT filter from Table 1 with \(N_{1}=5\).  
\begin{table}
\begin{tabular}{c c c} \hline \(C_{\mathbf{11}}\) & \(\delta_{\mathbf{1}}\) [dB] & \(N_{\mathbf{1}}\) \\ \hline \(2^{0}\)-\(2^{-4}\) & 0.0274 & 4 \\ \(2^{0}\)-\(2^{-3}+2^{-5}\) & 0.0165 & 5 \\ \(2^{0}\)-\(2^{-3}+2^{-5}+2^{-8}\) & 0.0145 & 6 \\ \(2^{0}\)-\(2^{-3}+2^{-5}+2^{-8}\)-\(2^{-10}\) & **0.0142** & **7** \\ \hline \end{tabular}
\end{table}
Table 1: SPT compensator \(C_{1}\) coefficients, passband deviation, and the number of adders.
# An Artificial Plant Community Algorithm for Collision-Free Multi-Robot Aggregation
## 6 Benchmark Data and Experiments
### Performance Comparison  
Some state-of-the-art algorithms were employed for comparison, including the proposed APC algorithm, grey wolf optimization (GWO) [2], the genetic algorithm (GA) [3], particle swarm optimization (PSO) [4], ant colony optimization (ACO) [7], and deep learning (DL) [8]. All of these SOTA algorithms adopted the same benchmark data described in Section 6.1 and the multi-objective function in Equation (12). Each technique uses a basic version and the same population size. The APC algorithm used the same parameters as detailed above. For GWO, the population size of the gray wolves was set to \(S=80\), the upper bound of the variables was set as \(ub=[10,\,10]\), the lower bound of variables was set as \(lb=[-10,\,-10]\), and the dimension of the problem was set as \(dim=2\). For the GA, the population size was \(S=80\), the chromosome length was Lind = 20, the crossover probability was px = 0.7, and the mutation probability was pm = 0.01. For the PSO, the population size was set as S = 80,  
Figure 9: Parameter comparisons: (**a**) the trends of the four main metrics under different distances between tasks and (**b**) the trends of the four main metrics under different robot velocities. the location limitation was 0.5, the speed limitation was [\(-0.5\), 0.5], the self-learning factor was c1 = 1.5, and the social learning factor was c2 = 1.5. The ACO parameters were set as follows: pheromone importance = 1.0, importance of heuristic factors = 5.0, pheromone volatilization factor = 0.1, and S = 80 ants. For the DL models, a convolutional neural network with six convolution cores, six input channels (\(cin=6\)), and six output channels (\(cout=6\)) was used. The learning rate of the offset item was twice that of the weight. The extension edge was set to 0, the weight was initialized to Gaussian, and the value of the constant offset item was 0. Two scenarios were employed for comparison, i.e., different task numbers and different robot numbers, as detailed in Tables 7 and 8. The number of tasks was increased from \(T_{max}=1,3,5,\ldots\) to \(T_{max}=15\), and the number of robots was increased from \(R_{max}=1,3,5,\ldots\) to \(R_{max}=15\). All algorithms run with the same number of iterations and the same computation time. For all algorithms, the maximum number of iterations was set to \(Ite_{max}=200\), and the maximum computation time was 1.0 h. Five main metrics were used for the benchmark comparison: the makespan \(k_{T}^{make}\), total collision time \(k_{T}^{coll}\), total energy consumption \(e_{T}^{total}\), total travel distance \(D_{T}^{travel}\), and the solution time. As these metrics are differently scaled, the results of all metrics were normalized into the range from 0 to 1. The solution time is provided in seconds. Note that the optimal results in Tables 7 and 8 are highlighted in bold.
## 2 Results
### DksA Is Not Required during the Infection Process  
Because (p)ppGpp seemed important during host infection and DksA is involved in a part of the (p)ppGpp transcriptional response in other species, we tested the ability of \(\Delta dksA\) to infect and proliferate inside RAW 264.7 macrophages. No difference in CFUs was observed between WT and \(\Delta dksA\) strains (Supplementary Figure S1), meaning that DksA is not crucially involved in the infection process and that the phenotype observed for (p)ppGpp-deprived mutants (\(\Delta rsh\) and _pBBRi-mesh1b_) is probably not mediated by DksA.
# Effect of Admixtures on Selected Properties of Fly Ash-Based Geopolymer Composites
## 5 Conclusions  
The results of the analysis of geopolymer mortars indicate that their properties can be improved by using suitable admixtures. The effect of varying activator content in the geopolymer composite should also be considered:  
1. The improvement in properties was achieved by the addition of the liquifying admixture based on stabilized polycarboxylates combined with a low activator content. The most favorable results were due to a more compact structure, which is confirmed by the highest bulk density as well as the lowest water absorption;
2. The increased volume and highly porous structure when the plasticizing admixture is used in the geopolymer composite have the same effect as when it is used in cement concrete. However, its additive activating properties have not been noted;
3. No positive effect was found for plasticizing admixtures or those based on modified polynaphthalenes.  
The evaluation of the effect of the variable polycarboxylate-based admixture content to obtain optimal properties of geopolymer composites will be the next step in the research program. On the basis of the preliminary studies presented in this article, it  
Figure 7: Bulk density results for all the series.  
will be possible to plan the next part of the study, in which the microscopic structure of geopolymer composites should be compared in relation to their composition, especially their admixture content.  
The study was performed as part of a research project conducted at the Bialystok University of Technology and was funded by the Polish Ministry of Education and Science.  
Not applicable.  
The author declares no conflict of interest.
# Dirac-Based Quantum Admittance of 2D Nanomaterials at Radio Frequencies
## 1 Introduction  
The drive toward circuit miniaturization and the advent of new materials, such as graphene, has led to a situation where the quantum current flows on mesoscopic and nanoscopic conductors. The quantum current and conductivity in nanostructures present novel and interesting aspects that have been the subject of much interest in recent years. In evaluating this kind of situation with a finite electron mass, the Schrodinger equation from quantum mechanics has been used in the framework of a free electron model, where electrons are described by scalar wave functions. This description is, however, incomplete when the polarization and magnetization aspects become important (e.g., in the common situation of applied electromagnetic fields). In such a case, the use of a four-component Dirac spinor description becomes necessary. In a conductor in particular, the scalar ratio between the first pair of components and the second pair plays the role of characteristic impedance of a transmission line, where the ratio of source impedance to load impedance is essential for the correct definition of transmittivity. In a previous work [1], we derived from the Dirac equation with a finite effective mass the quantum current in the presence of a d.c. electric field parallel to the surface of a 2D nanomaterial and, consequently, the d.c. conductivity. In the literature, a large number of works address the problem of charge transport in low-dimensional structures and related applications [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. In this work, we consider the action of an R.F. electric field in the same idealized 2D geometry. In the case of a device with dimensions much smaller than the electromagnetic wavelength, the electromagnetic boundary conditions of the field can be neglected, and the tensor character of the admittance can be approximated as scalar since the cross-impedance effect is low. Real loss mechanisms, such as intra-band transitions and interaction with the substrate, are subsumed by a global, finite mean free path \(L_{F}\). Our aim is to extend the calculation of the scalar quantum conductivity previously performed in the d.c. case to the case of the R.F. admittance. The amplitude of the driving R.F. field is assumed initially to not be depleted by losses and be unaffected by the current on the surface (low loss approach). Once the current has been evaluated, losses are introduced in the potential, leading to the complex R.F. admittance at the signal frequency. The intensity of the R.F. signal is assumed to be moderate enough to not affect the electron density or Fermi distribution, and its action is assumed to start at x = 0 and t = 0.
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 3 Results
### Lightning Analysis
#### 3.2.2 Trends versus Precipitation Rates
In all figures, the shaded areas indicate the 99% confidence interval in each 1 mm h\({}^{-1}\) bin. The GLM analysis shows a similar pattern. For CYGNSS (Figure 5c), the gap between winds without and without GLM lightning narrows relative to WWLLN, though this is not true for ASCAT (Figure 5d). This narrowing for CYGNSS could reflect more accurate separation between the \"with lightning\" and \"without lightning\" categories, since GLM detects total lightning rather than primarily CGs like WWLLN. For ISS LIS and CYGNSS (Figure 5e), the gap between the two wind categories narrows to the point of overlap above ~1 mm h\({}^{-1}\). For ASCAT and ISS LIS (Figure 5f), a similar pattern to GLM and WWLLN is seen, but the two curves are closer and start to overlap at 5 mm h\({}^{-1}\). It is possible that the reduced samples for ISS LIS may be contributing to the greater volatility in the curves at higher rain rates. Overall, Figure 5 confirms and expands upon the basic statistics shown in Tables 2-4. For CYGNSS, differences between winds associated with lightning and those not are reduced for total lightning sensors like GLM and ISS LIS. Moreover, the difference between the two wind categories (maximized around +0.5 m s\({}^{-1}\) for WWLLN, but much smaller for GLM and especially ISS LIS), is significantly smaller than the overall increase in wind speeds above background when precipitation is present (+1-2 m s\({}^{-1}\), depending on precipitation rate). Winds associated with lightning are slightly stronger when precipitation rate is low (<1 m s\({}^{-1}\)), but that changes as precipitation rate increases and winds associated with WWLLN and GLM lightning are slightly weaker than winds not associated with lightning. Similar to Figure 3, ASCAT winds increase more rapidly than CYGNSS as rain rate increases (Figure 5b,d,f). However, for all lightning sensors below at least 5 mm h\({}^{-1}\) rain rate, ASCAT winds are stronger when lightning is present (by up to 0.5 m s\({}^{-1}\)). Recall, however, the fewer samples available for ASCAT in the analysis.
###### Abstract  
The continuous phase modulation (CPM) technique is an excellent solution for underwater acoustic (UWA) channels with limited bandwidth and high propagation attenuation. However, the severe intersymbol interference is a big problem for the algorithm applying in shallow water. To solve this problem, an algorithm for prefiltered single-carrier frequency-domain equalization (PF-SCFDE) is presented in this paper. The regular whitening filter is replaced by a prefilter in the proposed algorithm. The output information sequence of this prefilter contains the forward information. To improve the performance, the output of the equalizer, combined with the forward information, is used to make the maximum likelihood estimation. The simulation results with minimum-shift keying and Gaussian-filtered minimum-shift keying signals over shallow water acoustic channels with low root mean square delay spread demonstrate that PF-SCFDE outperformed the traditional single-carrier frequency-domain equalization (SCFDE) by approximately 1 dB under a bit error rate (BER) of \(10^{-4}\). A shallow sea trial has demonstrated the effectiveness of PF-SCFDE; PF-SCFDE had a reduction in BER of 18.35% as compared to the traditional SCFDE.  
UWA communication; binary continuous phase modulation; single-carrier frequency-domain equalization; prefilter +
Footnote †: journal: _Sensors_  
**2022**, 22, 3821. [[https://doi.org/10.3390/s22103821](https://doi.org/10.3390/s22103821)]([https://doi.org/10.3390/s22103821](https://doi.org/10.3390/s22103821))
# Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials
## 1 Introduction  
With the rapid development of nanotechnology, various but similar definitions of nanomaterials have been proposed [1; 2; 3]. From the current available definitions of nanomaterials, summarized by [PERSON] et al., most of them define nanomaterials based on the size parameter [4]. In this article, we used the definition from the EC Scientific Committee on Emerging and Newly Identified Health Risks. A manufactured nanomaterial is a material which is intentionally generated such that it is composed of discrete structural and functional parts, either at the surface or internally, with one or more dimensions at the order of 100 nanometers (nm) or less [5], exhibiting distinct and superior physicochemical and biological properties compared to their conventional equivalents [6]. The improved nanoscale properties such as hardness, electrical conductivity, magnetic characteristics, chemical reactivity, and toxicity are derived from a number of parameters such as shape, surface chemistry, size, and specific surface area [4; 7; 8; 9]. So far, engineered nanomaterials have been proposed for a wide array of industrial applications such as paints, coatings, electronics, energy, power, cosmetics, and pharmaceuticals [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. According to a recent report published by Grand View Research, Inc., the size of the global nanomaterials market is estimated to reach USD 22.9 billion by 2027 with a compound annual growth rate of 13.1% [23].  
The rapid development of nanotechnology generates numerous nanomaterials with different properties and functions. To promote better development of nanotechnology, some basic and common terms and concepts have been proposed. For example, regardingthe synthesis of nanomaterials, there are two basic approaches to synthesize materials with nanoscale features and attributes [24]. One is known as top-down fabrication, where small features are created based on large substrates using methods such as lithography, chemical ablation, laser ablation, and electrochemical carbonization [25; 26; 27; 28]. Another one is called bottom-up fabrication, which assembles small building-block units into larger nanostructures. The bottom-up route includes self-assembly, microwave irradiation, hydrothermal/solvothermal treatment, and so on [29; 30; 31]. The synthesized nanomaterials, depending on their composition, can be divided into carbon-based nanomaterials, metal nanomaterials, semiconductor nanomaterials, metal oxide nanomaterials, polymer nanomaterials, lipid-based nanomaterials, and others, as shown in Figure 1. Nanomaterials can also be classified by their dimensionalities, including zero dimensional (0D: zero dimension \(>100\) nm) nanomaterials such as nanoparticles, one-dimensional (1D: one dimension \(>100\) nm) nanomaterials such as nanotubes, two-dimensional (2D: two dimensions \(>100\) nm) nanomaterials like graphene, and three-dimensional (3D: all three dimensions \(>100\) nm) nanomaterials, e.g., nanocomposites [32].
# An Efficient Precoding Algorithm for mmWave Massive MIMO Systems
## 5 Conclusions and Future Recommendations  
In this paper, we proposed a matrix-inversion bypass (MIB) bird swarm algorithm (BSA) based OMP algorithm (BSAMIBOMP) to eliminate the matrix inversion operation and known candidate matrix requirement so that the number of computations is reduced. The algorithm uses the characteristics of BSA with a global search optimal value to search for the largest array response vector multiplied by the residual matrix and uses the Banachiewicz-Schur block matrix generalized inverse to transform the high-dimensional matrix into a low-dimensional matrix, avoiding matrix inversion and reducing the amount of calculation. Compared with the existing OMP-based hybrid precoding [15], SDRAItMin precoding [16], and analog precoding [17], the proposed algorithm achieves better performance in terms of system spectral efficiency and bit error rate without known candidate matrix and matrix inversion. For future directions, [this study can be further extended by considering the energy efficiency of the proposed algorithm and comparing it with other state-of-the-art algorithms under different important parameters and constraints. Moreover, the future work can also focus on analyzing the hardware impairments versus different parameters for the proposed algorithm and comparing it to existing competing alternatives.  
Conceptualization, I.K., S.H. and N.A.; Data curation, Y.K., M.I.K. and F.A.; Formal analysis, J.R. and A.S.; Funding acquisition I.K. and A.S.; Investigation, I.K. and Y.K.; Methodology, M.I.K., I.K., J.R. and N.A.; Project administration, A.S. and I.K.; Software, S.H., I.K. and N.A.; Writing--original draft, I.K. and S.H.; Writing--review & editing, I.K., S.H. and N.A.  
This work was supported by Universiti Putra Malaysia under the grant NOMA-MIMO: Optimizing 5G Wireless Communication Performance Based on Hybrid NOMA with Partial Feedback for Multiuser MIMO (GP-IPS/2018/9663000, Vot No: 9663000).  
The authors declared that they have no conflict of interest.
## 5 Results
### Model Accuracy
Among the models, HFL reaches the highest accuracy with the lowest privacy leakage, while FLBM-IoT has the highest privacy leakage with similar accuracy to the other models. Slightly lower in accuracy but not too high in terms of privacy leakage, PPFLB is a middle ground. In the evaluation process, experiments were conducted in real-world scenarios such as disease prediction and patient risk profiling to test the proposed HFL model. The simulation results that we obtained indicate that the HFL model can be implemented in multiple healthcare organizations while maintaining privacy. These characteristics make the HFL framework the most suitable solution to apply in large-scale healthcare networks because of its scalability, computational efficiency, and privacy preservation.