esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs
### Increased Connectivity  
Connectivity is the presence of a single- or multiple-hop path between any source and destination nodes in the CR-WSN. Network connectivity determines the efficiency of multihop routing used in CR-WSNs. Connectivity affects the network capacity, and it depends on various factors, such as interference, signal-to-noise ratio (SNR), and energy constraints [42; 43]. To increase network efficiency, these issues should be solved in a local effort within the cluster.
# DALLoc: Deep-Learning Accelerated Visual Localization Based on Mesh Representation
###### Abstract  
Visual localization, i.e., the camera pose localization within a known three-dimensional (3D) model, is a basic component for numerous applications such as autonomous driving cars and augmented reality systems. The most widely used methods from the literature are based on local feature matching between a query image that needs to be localized and database images with known camera poses and local features. However, this method still struggles with different illumination conditions and seasonal changes. Additionally, the scene is normally presented by a sparse structure-from-motion point cloud that has corresponding local features to match. This scene representation depends heavily on different local feature types, and changing the different local feature types requires an expensive feature-matching step to generate the 3D model. Moreover, the state-of-the-art matching strategies are too resource intensive for some real-time applications. Therefore, in this paper, we introduce a novel framework called deep-learning accelerated visual localization (DLALoc) based on mesh representation. In detail, we employ a dense 3D model, i.e., mesh, to represent a scene that can provide more robust 2D-3D matches than 3D point clouds and database images. We can obtain their corresponding 3D points from the depth map rendered from the mesh. Under this scene representation, we use a pretrained multilayer perceptron combined with homotopy continuation to calculate the relative pose of the query and database images. We also use the scale consistency of 2D-3D matches to perform the efficient random sample consensus to find the best 2D inlier set for the subsequential perspective-n-point localization step. Furthermore, we evaluate the proposed visual localization pipeline experimentally on Aachen DayNight v1.1 and RobotCar Seasons datasets. The results show that the proposed approach can achieve state-of-the-art accuracy and shorten the localization time about five times.  
visual localization; multilayer perceptron (MLP); homotopy continuation; mesh-based 2023 by the authors.  
C 2023, 13, 1076, [[https://doi.org/10.3390/](https://doi.org/10.3390/)]([https://doi.org/10.3390/](https://doi.org/10.3390/))  
1
# Modeling the Response of Magnetorheological Fluid Dampers under Seismic Conditions
###### Abstract  
Magnetorheological (MR) fluid is a smart material fabricated by mixing magnetic-responsive particles with non-magnetic-responsive carrier fluids. MR fluid dampers are able to provide rapid and reversible changes to their damping coefficient. To optimize the efficiency and effectiveness of such devices, a computational model is developed and presented where the flow field is simulated using the computational fluid dynamics approach, coupled with the magnetohydrodynamics model. Three different inlet pressure profiles were designed to replicate real loading conditions are examined, namely a constant pressure, a sinusoidal pressure profile, and a pressure profile mimicking the 1994 Northbridge earthquake. When the MR fluid damper was in its off-state, a linear pressure drop between the inlet and the outlet was observed. When a uniform perpendicular external magnetic field was applied to the annular orifice of the MR damper, a significantly larger pressure drop was observed across the annular orifice for all three inlet pressure profiles. It was shown that the fluid velocity within the magnetized annular orifice decreased proportionally with respect to the strength of the applied magnetic field until saturation was reached. Therefore, it was clearly demonstrated that the present model was capable of accurately capturing the damping characteristics of MR fluid dampers.  
computational fluid dynamics; magneto-hydrodynamic model; magnetorheological fluid damper; seismic control Article
## 4 Simulation Verification
### Fading Channel
#### 4.2.2 Small-Scale Fading Caused by Multipath Effect  
The simulation presented here takes a polar code with a code length of (1024, 307) as an example. The \(E_{b}/N_{0}\) ratio is set to 2.5 dB, the maximum number of iterations is configured to 60, and the number of decoders \(L\) is set to five. Assuming there is a direct path, the power of other paths is multiplied by a random attenuation factor relative to the direct path. The delay for each path is set to be one sampling point apart. The number of paths is varied from three to seven. The bit error rate (BER) results are illustrated in the Figure 10 below:  
Figure 8: The bit error rate performance of BPLIG (\(L=5\)) compared to BPL (\(L=10\)).  
The simulation results demonstrate that the bit error rate (BER) of the BPL decoding based on the path selection criterion proposed in this paper is reduced by 54.4-67.8% compared to the BPL decoding of polar codes based on traditional path selection criteria.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 3 Methods
### Relevant Description  
#### 3.1.1 User-Item Interaction Graph  
In our research, we have users \(U=\{u_{1},u_{2}\ldots u_{X}\}\) and items \(I=\{i_{1},i_{2}\ldots i_{Y}\}\), where \(X\) is the number of users and \(Y\) is the number of items. We formalize the user-item interaction graph through the following formula:  
\[G=\{(u,y_{ui},i)|(u\in U,i\in I)\} \tag{1}\]  
where \(U\) denote the user set and \(I\) the item set, while \(y_{ui}\) signifies the interaction between a user and an item. The equation defining \(y_{ui}\) is presented below:  
\[y_{ui}=\begin{cases}1,\text{ if }u\text{ interacted with }i;\\ 0,\text{ otherwise.}\end{cases} \tag{2}\]  
When a user interacts (such as purchasing, liking, or browsing) with an item, \(y_{ui}=1\), otherwise, \(y_{ui}=0\). Follow the mainstream approach [13; 24], we also embed the users and item in the interaction graph as ID embeddings \(e_{u},e_{i}\in\mathbb{R}^{d}\), where \(e_{u}\) denotes the user ID embedding, \(e_{i}\) denotes the item ID embedding, and \(d\) denotes the dimension of the embedded vector.
# Line-of-Sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification
## 2 Optical Networking System Composition and Working Principle  
The principles of \"point-to-multipoint\" laser networking are shown in Figure 1. The optical antenna of the optical terminal is based on a rotating parabola, and several mirrors are spliced together to form a pointing mechanism. The rotating parabola has an optical property whereby the reflected light is parallel to the rotational symmetry axis of the paraboloid when the incident light passes through the focus. The normal line of each mirror is perpendicular to the tangent line of the paraboloid. Each mirror can be controlled to move in terms of azimuth and elevation, meaning that the antenna has certain light stabilization and deflection functions and can establish multiple optical links based on azimuth and elevation direction at the same time.  
The multiple mirrors of the optical terminal are uniformly arranged around the optical terminal, where each mirror is responsible for designing the capture range of the space. For example, the four mirrors capture a range covering 360\({}^{\circ}\). Each mirror has an azimuth capture range of 90\({}^{\circ}\) and an elevation capture range of 0-30\({}^{\circ}\). The azimuth-elevation frame can adjust the pointing angle of the optical antenna by adjusting the optical LOS attitude of the mirror, so as to realize multi-link LOS pointing to the FOU. After capture and tracking, the precise alignment of the LOS is realized. Then, communication, distance measurement, time-frequency transmission, and other functions can be carried out between space networks.  
Figure 1: Composition principle of space optical networking optical terminal.
###### Abstract  
Anaerobic digestion (AD) has long been critical technology for green energy, but the majority of the microorganisms involved are unknown and are currently not cultivable, which makes abundance tracking difficult. Developments in nanopore long-read sequencing make it a promising approach for monitoring microbial communities via metagenomic sequencing. For reliable monitoring of AD via long reads, we established a robust protocol for obtaining less fragmented, high-quality DNA, while preserving bacteria and archaea composition, for a broad range of different biogas reactors. Samples from 20 different biogas/wastewater reactors were investigated, and a median of 20.5 Gb sequencing data per nanopore flow cell was retrieved for each reactor using the developed DNA isolation protocol. The nanopore sequencing data were compared against Illumina sequencing data while using different taxonomic indices for read classifications. The Genome Taxonomy Database (GTDB) index allowed sufficient characterisation of the abundance of bacteria and archaea in biogas reactors with a dramatic improvement (1.8- to 13-fold increase) in taxonomic classification compared to the RefSeq index. Both technologies performed similarly in taxonomic read classification with a slight advantage for Illumina in regard to the total proportion of classified reads. However, nanopore sequencing data revealed a higher genus richness after classification. Metagenomic read classification via nanopore provides a promising approach to monitor the abundance of taxa present in a microbial AD community as an alternative to 16S ribosomal RNA studies or Illumina Sequencing.  
Keywords:nanopore; sequencing; DNA extraction; metagenome; abundance; GTDB; nanopore +
Footnote †: journal: Applied Sciences
# Using Coherent Hemodynamic Spectroscopy Model to Investigate Cardiac Arrest
## 2 Methodology  
### Overview of the Non-Linear CHS Model  
The non-linear Coherent Hemodynamic Spectroscopy (CHS) model [2] is a computational framework designed to quantify the temporal evolution of hemoglobin concentrations and oxygen saturation in tissue. It extends the original linear CHS model [1] by solving partial differential equations (PDEs) that account for large perturbations in physiological parameters, such as those observed during cardiac arrest (CA) and cardiopulmonary resuscitation (CPR) [1].  
The non-linear CHS model consists of four main equations [2]: two for calculating changes in tissue microvascular oxy- and deoxyhemoglobin concentrations in tissue due to changes in capillary blood flow velocity (Equations (1) and (2)), and two for calculating changes in oxy- and deoxyhemoglobin concentrations in tissue due to changes in blood volume (Equations (3) and (4)):  
\[\Delta O_{\text{F}}(t)=ctHB\left[\varphi^{(c)}F^{(c)}(\langle S^{(c)}\rangle(t )-\langle S^{(c)}_{0}\rangle)\ +\ (\langle S^{(p)}\rangle(t)-\langle S^{(p)}_{0}\rangle)\right] \tag{1}\]  
\[\Delta D_{\text{F}}(t)=-\Delta O_{\text{F}}(t) \tag{2}\]  
\[\Delta O_{\text{V}}(t)=ctHB\left[\varphi^{(a)}S^{(a)}(t)\ v^{(a)}(t)\ +\ \varphi^{(a)}\Big{\langle}S^{(p)}\Big{\rangle}(t)\ v^{(v)}(t)\right] \tag{3}\]\[\Delta D_{V}(t)=ctHB\left[\varphi^{(a)}\left(1\ -\ S^{(a)}(t)\right)v^{(a)}(t)\ +\ \varphi^{(a)}(1\ -\ \langle S^{(v)} \rangle(t)\ v^{(v)}(t)\right] \tag{4}\]  
where \(ctHB\) represents hemoglobin concentration in the blood, \(\varphi^{(c)}\) and \(\varphi^{(a)}\) denote the volume fractions of capillary and arterial blood in the tissue, respectively, \(F^{(c)}\) stands for the Fahraeus factor, \(\langle S^{(c)}_{0}\rangle\) and \(\langle S^{(v)}_{0}\rangle\) are the \"baseline\" oxygen saturations in the capillary and venules, \(v^{(a)}(t)\) and \(v^{(v)}(t)\) signify the changes in arterial and venous blood volumes normalized to their baseline values, \(S^{(a)}(t)\) represents arterial saturation, \(\langle S^{(c)}\rangle(t)\) indicates the average capillary saturation, and \(\langle S^{(v)}\rangle(t)\) represents the average venule saturation. The capillary and venule oxygen saturations can be calculated using the following equations:  
\[c^{(c)}(t)\ \frac{\partial S^{(c)}(x,t)}{\partial x}+\frac{\partial S^{(c)}(x,t) }{\partial t}=-\alpha_{\text{O}}S^{(c)}(x,t) \tag{5}\]  
\[c^{(v)}(t)\ \frac{\partial S^{(v)}(x,t)}{\partial x}+\frac{\partial S^{(v)}(x,t) }{\partial t}=0 \tag{6}\]  
where \(c^{(c)}(t)\) and \(c^{(v)}(t)\) represent blood velocities in capillaries and venules, respectively, and \(\alpha_{\text{O}}\) is the rate of oxygen diffusion.  
Our algorithm uses analytical solutions of Equations (5) and (6) for \(S^{(c)}(x,t)\) and \(S^{(v)}(x,t)\) (see Appendix A).  
The non-linear CHS model depends on 13 physiological parameters, listed in Table 1. The complete list of mathematical notations is provided in Appendix B.
# Comprehensive Numerical Analysis of a Porous Piezoelectric Ceramic for Axial Load Energy Harvesting
## 5 Analysis of Results and Discussion
### Effect of Porosity in the Energy Harvesting Device  
The porous piezoelectric energy harvesting device is numerically examined by varying the porosities from 0 to 80%. The \(d_{31}\) piezoelectric power generator is more suitable for small forces and low vibration levels, at the same time, the \(d_{33}\)-type is most suitable for high-force vibrations. In the device, two piezoelectric bimorphs (\(d_{31}\)) and two cylinders (\(d_{33}\)) are used for the analysis. The poling of the bimorphs and cylinders are \(d_{31}\) and \(d_{33}\), respectively. The first eigenvalue of the device is increased when the porosities of the piezoelectric bimorphs and cylinders increase. The first eigenvalue is 283 Hz and 295 Hz in cases of 0% and 80% porosity, respectively, as shown in Figure 6. The output voltage of the piezoelectric bimorphs increases when the porosity increases. The output voltage decreases when the porosity of the piezoelectric cylinder increases as shown in Figure 7a. The total cumulative voltage of the energy harvesting device decreases, when the porosities of piezoelectric materials increase. The output voltages measured are equal to 2.25 V and 1.75 V for 0% and 80% porosities, respectively, see Figure 7b. The measured values of output current are equal to 2.53 \(\mu\)A and 1.74 \(\mu\)A in the cases of 0% and 80% porosity, respectively. The current intensity decreases with increasing the porosity as shown in Figure 7c.  
Figure 6: Effect of porosity on natural frequency.  
Figure 7: _Cont._The numerical simulations are carried out for all the analysis. In the numerical simulation, all parameters and boundary conditions (i.e., damping factor, piezo capacitance, etc.) are established as per the fabricated model. For the case of energy harvesting of the piezoelectric energy harvester, the electrical output power delivered to the resistive load is measured by the equation: \(P=V^{2}/R\), where \(R\) is the resistive load and, \(V\) is the voltage across the resistive load.  
The maximum and minimum powers of 5.1 \(\upmu\)W and 3.0 \(\upmu\)W, respectively, are obtained from the device as shown in Figure 8. When the porosity increases, the output power decreases. From Figure 7a, porosity increases the \(d_{31}\) sensitivity of the piezoelectric patches but in the case of \(d_{33}\) sensitivity, it is decreased. Porous piezoelectric ceramics can be used in a variety of applications such as underwater acoustics, non-destructive testing, medical ultrasonic devices, etc. due to their high sensitivity.
# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 5 Simulation Results
### First Scenario: Step Load Change  
As the first scenario, performance of the DFIGs equipped with the SCA optimized FOPID controllers is investigated under a 0.01 p.u. step load change in area 1. The area frequency and tie-line power deviations are illustrated in Figure 5. As shown in this figure, the performance of the SCA-based FOPID controller with 10% wind penetration is compared with the SCA-based PID controller, with 10% wind penetration and the condition that there is no DFIGs in any areas. Area frequency and tie-line power oscillations are remarkably damped by the proposed SCA-based FOPID controller compared with two other controllers. The ITSE performance index, peak overshoot, peak time and settling time are shown in Table 3. The SCA-based FOPID controller has the lowest value of the ITSE index compared to the other controllers for the first scenario. Therefore, it can be concluded that the proposed controller improves the dynamic responses for the studied power system more efficiently. Furthermore, the settling time and peak overshoot are reduced by the proposed controller. Note that the peak overshoot unit for the frequency deviation is Hz; and for the tie-line power deviations is pu. In this study, settling time is the time required for an output to reach and remain within a \(\pm\)5% error band following some input stimulus. Furthermore, minimum damping ratio provides a mathematical means of expressing the level of damping in a system relative to critical damping.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Controller Type** & **Areas** & \(\mathbf{K_{P}}\) & \(\mathbf{K_{I}}\) & \(\mathbf{K_{D}}\) & \(\mathbf{\lambda}\) & \(\mathbf{\mu}\) \\ \hline FOPID-DFIG & Area 1 & 0.2104 & \(-\)0.2002 & 0.7112 & 0.4704 & 0.6387 \\ PID-DFIG & Area 1 & 0.1509 & \(-\)0.1807 & 0.7409 & - & - \\ PID-AGC & Area 1 & 0.8544 & 0.2979 & 0.5840 & - & - \\ FOPID-DFIG & Area 2 & 0.2194 & \(-\)0.2233 & 0.2901 & 0.4418 & 0.5794 \\ PID-DFIG & Area 2 & 0.1644 & \(-\)0.1907 & 0.6987 & - & - \\ PID-AGC & Area 2 & 0.4811 & 0.2977 & 0.5133 & - & - \\ \hline \end{tabular}
\end{table}
Table 2: Optimal parameters of controllers obtained by the sine-cosine algorithm (SCA).  
Figure 5: Frequency deviation and tie-line active power change in the first scenario, (**a**) frequency deviation in area 1; (**b**) frequency deviation in area 2; and (**c**) tie-line power deviation.
## 4 Discussion
### Confirmatory Tests  
Confirmatory tests were carried out on the Taguchi design of experimentation to validate the authenticity of the achieved results. In this research, tool wear, surface roughness, and burr size were analyzed using a \"smaller is better\" model. The desired values of these input parameters, as determined from the input parameters in the main effects plot in Section 4, are presented in Table 11. Subsequently, validation experiments were conducted under these conditions, as presented in Table 12. It can be seen that an experiment was already carried out on one out of the eight machining parameter combinations during the initial design of the experiment (L9 orthogonal array). The rest of the conditions were run three times each. It was observed that all best and worst output response conditions yielded corroborative results. Figure 14 depicts the burr width value obtained from the optimal confirmatory test conditions, specifically at a feed rate of 0.65, a cutting velocity of 75 m/min, and an axial depth of cut set at 30 \(\upmu\)m. The burr width was measured at 154.47 \(\upmu\)m, which happened to be the lowest (optimized) burr width value, as expected. In comparison with the previous lowest value obtained during initial experimentation (at 0.45 f\({}_{\mathrm{z}}\), 25 V\({}_{\mathrm{c}}\), 60 a\({}_{\mathrm{p}}\)), 3.4% improvement was recorded. In Figure 15, the depicted data illustrate that a surface roughness of 32 \(\upmu\)m was acquired under specific machining conditions of a feed rate of 0.25, a cutting velocity of 25 m/min, and an axial depth of cut set at 90 \(\upmu\)m, which was 8.5% less than the initial best surface roughness value obtained at 0.45 f\({}_{\mathrm{z}}\), 25 V\({}_{\mathrm{c}}\), and 60 a\({}_{\mathrm{p}}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Respenses**} & \multirow{2}{*}{**Condition**} & \multicolumn{4}{c}{**Input Parameters**} \\ \cline{3-5}  & & \(\mathbf{V_{c}}\) (\(\upmu\)/min) & \(\mathbf{f_{z}}\) (\(\upmu\)/tooth) & \(\mathbf{a_{p}}\) (\(\upmu\)m) \\ \hline \multirow{2}{*}{Surface roughness (\(\upmu\)m)} & Best & 0.25 & 25 & 90 \\  & Worst & 0.65 & 75 & 30 \\ \hline \multirow{2}{*}{Burr width (\(\upmu\)m)} & Best & 0.65 & 75 & 30 \\  & Worst & 0.25 & 25 & 90 \\ \hline \multirow{2}{*}{Burr length (\(\upmu\)m)} & Best & 0.65 & 75 & 60 \\  & Worst & 0.25 & 50 & 30 \\ \hline \multirow{2}{*}{Tool wear (\(\upmu\)m)} & Best & 0.45 & 25 & 30 \\  & Worst & 0.25 & 75 & 90 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Best and worst condition for input parameters.
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 1 Introduction
In most UV-based disinfection systems, mercury lamps have been the source of radiation which is primarily of two types: medium pressure mercury (MPM) and low-pressure mercury (LPM) UV lamps that are reliable disinfection sources with high efficiency and low cost [10]. UV-C rays, which act as germicides, have the greatest effect on various microorganisms which include bacteria, viruses, protozoa, fungi, algae [14], and bacterial spores [1, 12, 15, 16, 17]. UVC germicidal lamps are used to sterilize air, disinfect surfaces, deter microorganisms from accumulating on food surfaces, and are a convenient and effective method to clean water without the usage of toxic chemicals [18]. Figure 1: UV radiation and wavelengths [19, 20, 21]. In a survey study, UV radiation was identified as one of the main novel technologies currently applied or with the potential to be commercialized in 5-10 years. The respondents (food professionals from industry, academia, and government) from North America and Europe classified UV as the third and fourth cutting-edge food processing technology, respectively. The results of the survey demonstrated that the main drivers for the commercialization of novel technologies, i.e., UV treatment, were higher quality products (94%), product safety (92%), and shelf life (91%) [22]. In January 2016 [23], the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA) delivered an opinion on UV-treated milk as a novel food submitted pursuant to Regulation (EC) No 258/97. The European Food Safety Authority (EFSA) stated that \"the novel food is cow's milk (whole, semi-skimmed or skimmed) to which treatment with ultraviolet (UV) radiation is applied after pasteurization in order to extend the shelf life of the milk and increase the vitamin D3 concentrations by conversion of 7-dehydrocholesterol to vitamin D3. The EFSA panel concluded that the novel food, UV-treated milk, is safe under the intended conditions of use as specified by the applicant.\"  
With an increasing number of studies related to the application of UV radiation in the food industry, especially milk and dairy products, this review aimed to systemize the information by describing the fundamentals of this technology, with emphasis on inactivation dose and influence on physicochemical and sensory characteristics as compared with conventional heat treatment.