esa-sceva/satcom-chunk-collection huggingface dataset:
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## 3 Results and Discussion  
### Characterization of BT Dielectric Ink  
BT nanoparticles were added to a blend of urethane and acrylates (C3-D1-1032 Dielectric photopolymer, Chem3, LLC, USA) to fabricate the polymer/ceramic ink. Processability at room temperature is essential for use in E-textiles, as textile materials cannot sustain high processing temperatures [39]. The ink with the photoinitiator using ultraviolet radiation can be polymerized after UV curing, and forms a thin film on the textile substrate (Figure 3a). Moreover, the small molecules with low viscosity can help prevent the BT dielectric ink from blocking the spray nozzle (Figure 3a). Figure 3b displays a scanning electron microscopy (SEM) image of the BT nanoparticles, where it was found that the nanoparticles were a mixture of cubic and polyhedric in shape. The size distribution of the BT nanoparticles shown in Figure 3c was obtained by measuring more than 200 particles. The BT nanoparticles used in the dielectric ink exhibited an average size of 211 nm. The size distribution of the BT dielectric ink was tested by a dynamic light scattering (DLS) method. The main peak of the ink is at less than 1000 nm, indicating that the ink dispersed well only with slight aggregation (Figure 3d). This is an indication that the nanoparticle ink will not block the spray nozzle. The viscosity of the BT-UV curing ink may affect the rheological characteristics through printer nozzles, where a high-viscosity ink could lead to nozzle clogging. The BT dielectric ink exhibited Newtonian behavior with a low viscosity of 12 cps, which ensured that the ink could be easily spray coated. The ink surface tension was 37.53 mN/m, and the pendent volume was 3.43 \(\mu\)L. Ink with low surface tension can prevent nozzle dripping and guarantee decent wettability [40; 41]. Figure 3e shows the X-ray diffraction (XRD) pattern of BT dielectric ink after UV curing. The BT dielectric ink shows XRD peaks at 25.85\({}^{\circ}\) from the photopolymer. The peaks at 31.50\({}^{\circ}\) and 38.85\({}^{\circ}\) indicate that the BT nanoparticles exhibit a tetragonal crystal structure, which has a higher dielectric performance compared to cubic BT phase [42].
# Community Detection Based on Graph Representation Learning in Evolutionary Networks
## 5 Experiments
### Experimental Analysis  
The proposed algorithm (abbreviated as LCDEN) and the DWGD algorithm were used to divide the network structure of the data set containing a time stamp under different time slices, and the obtained Silhouette Coefficient and running time were compared. The experimental results and analysis are as follows.  
1. The comparative experimental results on the Superuser temporal network are shown in Figure 1. As shown in Figure 1a, we can conclude that, in the same time slice, the Silhouette Coefficient of the proposed algorithm is higher than that of the DWGDalgorithm, that is, the clustering effect is better. Figure 1b demonstrates that, in the same time slice, the running time of the proposed algorithm is less than the DWGD algorithm.
2. The comparative experimental results based on the Wiki-Talk-Temporal network (Wikipedia network dataset) are shown in Figure 2.  
Figure 2a also shows that the Silhouette Coefficient of the proposed algorithm is higher than the DWGD algorithm at the same time slice, indicating a better clustering effect of the proposed algorithm. From Figure 2b, we can see that in the time slices of S0 and S4, the running time of the proposed algorithm and the DWGD algorithm are close to each other, and, in other time slices, the running time of the proposed algorithm is less than that of the DWGD algorithm.
3. The comparative experimental results based on the Twitter dataset are shown in Figure 3.  
Figure 1: Comparison of the Silhouette Coefficient (**a**) and running time (**b**) on the Superuser temporal network.  
Figure 2: Comparison of the Silhouette Coefficient (**a**) and running time (**b**) on the Wiki-Talk-Temporal network.  
Figure 3a illustrates that the Silhouette Coefficient of the proposed algorithm is higher than that of the DWGD algorithm, indicating a better clustering effect of the proposed algorithm. Figure 3b discloses that the running time required for the proposed algorithm is less than that of the DWGD algorithm.
## 3 Methodology and Data Collection
### Data Pre-Processing
#### 3.2.4 Data Cleaning  
It was necessary to remove nonrepresentative values from the collected data as they would impair the estimation of the prediction model. This mainly includes measurements in segments undergoing certain traffic restrictions. For this purpose, the dataset of traffic  
Figure 4: Example of the course of the average hourly speed for the Nusle Bridge in Prague (_Tmcld_: TS04395T04396).  
restrictions was primarily used (see Section 3.1). For the time of existence of the traffic restriction, the data were marked as invalid and were not used for the determination of the prediction model.  
The problem with the set of traffic restrictions is that it has only been available since 18 May 2020. Prior to this date, there was no credible information available. At the same time, an ad hoc manual check of data implies that at least some of traffic restrictions were not recorded in the traffic restrictions dataset.  
As a result, a simple additional test was implemented with respect to suspicious data values, where such values were marked as invalid in the history. The test was based on the detection of sudden decrease in the average daily speed in the given segment (by more than 20% and at least by 15 km/h) lasting at least 3 days, followed by the return to the average daily speed back to the original value. Despite the fact that not all of nonrepresentative data were detected this way, it seems that this detection, along with the official set of traffic restrictions, constitutes an acceptable solution. In the future, we hope that it will be possible to rely on the set of traffic restrictions provided officially.  
Figure 6 illustrates an example of detection of invalid values. The dark red highlight illustrates the period where the existence of a traffic restriction was set forth in the available dataset of traffic restrictions (see Section 3.1). Despite the fact that this dataset should contain all restrictions at least since 18 May 2020, this was not the case. The additional test concerning rapid decreases in average daily speed worked, and it identified the likely occurrence of traffic restriction since 7 March 2020 (light red highlight).  
All data identified either in traffic restriction or by our additional test were automatically removed from the training data. Moreover, all data from 12 March to 26 April 2020 were removed also from the training data. During this period, the traffic was highly affected by the consequences of the SARS-CoV-2 coronavirus pandemic (COVID-19) due to the measures applied generally in order to prevent the spreading of coronavirus in the Czech Republic.  
Figure 5: Example of daily average measured speed and the hours with the sun above the horizon on the Nusle Bridge in Prague (_Tmcld_: TS04395T04396).
## 3 System Model
By analogy, \(\mathbf{\mathbf{\Lambda}}_{B}\Big{(}\chi_{l}^{\left(AoD\right)}\ \Big{)}\) is defined as follows [30]:  
\[\mathbf{\mathbf{\Lambda}}_{B}(\theta\ )=\frac{1}{\sqrt{N}}\bigg{[}1,\ldots,\ e^{j\frac{2\pi}{\lambda}dn \sin\left(\chi_{l}^{\left(AoD\right)}\right)},\ldots\bigg{]}^{T}, \tag{5}\]  
where \(0\leq n\leq(N-1)\). Figure 1: RIS-assisted mmWave communication system. The mmWave-RIS optimization problem aims to jointly optimize \(\mathbf{\Phi}\) and \(\mathbf{f}\) for maximizing the achievable spectral efficiency \(\psi\) in bps/Hz at the UE. Mathematically speaking this can be expressed as follows:  
\[\{i^{*},\ j^{*}\}=\max_{i,j}\left(\psi_{\mathbf{\Phi}\psi_{j}}\right), \tag{6}\]  
where  
\[\psi_{\mathbf{\Phi}\psi_{j}}=\log_{2}\left(1+\frac{P\Big{(}\mathbf{h}_{RU}^{H}\mathbf{\Phi }_{i}\mathbf{H}_{BR}\mathbf{f}_{j}\Big{)}\Big{(}\mathbf{h}_{RU}^{H}\mathbf{\Phi}_{i}\mathbf{ H}_{BR}\mathbf{f}_{j}\Big{)}^{H}}{\sigma_{0}^{2}}\right)\]  
Herein, \(\psi_{\mathbf{\Phi}\psi_{j}}\) is the spectral efficiency at the UE resulting from using \(\mathbf{\Phi}_{i}\) and \(\mathbf{f}_{j}\), and the indices of the optimal values of \(\mathbf{\Phi}\) and \(\mathbf{f}\) are represented by \(\{i^{*},\ j^{*}\}\). Most of the existing literature assumes perfect CSI information; i.e., \(\mathbf{H}_{BR}\) and \(\mathbf{h}_{RU}\) are well known at both BS and RIS. Based on that, both \(\mathbf{\Phi}\) and \(\mathbf{f}\) can be jointly adjusted using different iterative techniques [30; 31; 32]. However, this is a strong assumption as it is too difficult to estimate \(\mathbf{H}_{BR}\) and \(\mathbf{h}_{RU}\) due to the use of massive antenna elements at both BS and RIS. Furthermore, RIS should be utterly passive without any channel estimation functionality.
# Tabu Search Aided Multi-Track Detection Scheme for Bit-Patterned Media Recording
## 2 Proposed Detection Scheme  
The proposed detection scheme consists of a 1D equalizer, a 2D equalizer, a single-track detector, a simplified multi-track detector, a reliability test, and a TS detector as shown in Figure 1. That is, the reliability test and TS detector are additionally considered in the proposed detection scheme to the conventional simplified multi-track detection proposed in [5].  
The proposed detection scheme operates in three stages. First, the single-track detector detects the data by using the 1D equalizer with the 1D GPR target for the main-track \(\{0\}\) and side-tracks \(\{-2,-1,+1,+2\}\). Second, the reliability test is taken to detect bits with low reliability in the side-track \(\{-1,+1\}\) by comparing the distance between the readback signal and the estimated and reconstructed readback signal from the single-track detector in the first stage. If the distance is greater than the specific value, the TS detector is conducted to improve the reliability of bits with low reliability. Finally, the simplified multi-track detector detects the main-track data by using the outputs of the TS or single-track detectors on \(\{-1,+1\}\) tracks as a priori information.  
Figure 1: Block diagram of the proposed detection scheme.
## 1 Introduction  
RNA-sequencing (RNA-seq) is being used to diagnose cancer and predict the behavior of cancer cells [1], which is directly linked to the expression of certain genes. Thus, it is possible to diagnose breast cancer and predict metastasis by analyzing gene expression profiles [2]. Genes involved in metastasis can be identified by comparing RNA-seq results of confirmed metastatic and non-metastatic breast cancer samples. Genes such as _SETDB1_[3], _MALAT1_[4], _EHMT2_[5], _RAB1B-ASI_[6], _STAT3_[7], and _RAS_[8] were identified to play a role in lymph node metastasis of breast cancer. However, it is still impossible to effectively predict lymph node metastasis of breast cancer solely through gene expression analysis, although several studies have explored these particular genes. This limitation is because RNA-seq results only indicate the current state of breast cancer cells. Nevertheless, it is possible predict the behavior of breast cancer cells to some extent by analyzing the expression status of genes related to metastasis and grasping the current metastasis status of breast cancer [9], but the accuracy of such assessment is not high enough for clinical application. Hub gene was used to obtain more accuracy to represent the function of cells than single gene analysis from RNS-sequencing in the cancer studies [10]. Hub gene was defined as genes having top 10% of connectivity in individual gene module related gene function [11]. The gene modules were created by a systematic biological strategy for evaluating gene association patterns among different samples with bioinformatics tools like WGCNA or GSEA [12].  
Machine learning is a field of artificial intelligence that learns using algorithms based on real data, which is then used to design predictive models [13]. Such models are being actively investigated in different fields, including for the prediction of cancer prognosis using nuclear imaging [14]. However, prediction models building on nuclear imaging data still lack accuracy, making it difficult to apply clinically. This poor response of prediction models based only on imaging data can be explained as several factors can contribute to the cancer progression pattern. Recently, a prediction model with high accuracy was created by [PERSON] et al. using genomic sequencing data of cancer cells that have metastasized from the colorectal cancer site to the lymph nodes [15]. This previous report demonstrates that it is possible to use next generation sequencing (NGS) data as input of prediction models with high accuracy.  
In this study, a machine learning model based on RNA-seq data was developed using the random forest algorithm and hub genes to estimate the accuracy of the breast cancer metastasis prediction model. The accuracy of the prediction model with total genes, functional group genes, and hub genes was compared using various methods to identify a suitable machine learning algorithm for prediction of breast cancer metastasis.
## Results
### Canopy Architecture and Volatile Compounds
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Volatile Compounds** & **Compound Group** & **Aroma Associated** & **References** \\ \hline _Phenethyl acetate_ & _Ester_ & _Honey/Ioral/yeasty/cocoa_ & [28, 29] \\ _2-Phenethyl-2-buttenal_ & _Aldehyde_ & _Sweet/cocoa/nutty/beauty_ & [28, 29] \\ _Isoamyl acetate_ & _Ester_ & _Banana/fruity_ & [29, 30] \\ _Tetramethylpyrazine_ & _Pyrazine_ & _Roasted/cocoa/coffee/nutty/earthty_ & [7, 29, 31] \\ _Phenylacetaldehyde_ & _Aldehyde_ & _Honey/rose/cocoa_ & [29, 31] \\ _2,3-Butanediol_ & _Alcohol_ & _Cocoa butter/creamy_ & [29, 30] \\ Isovaleraldehyde & Aldehyde & Chocolate/nutty/cocoa & [28, 29] \\ 2-Pentanone & Ketone & Fruity & [29, 32] \\ Diacetyl/2,3-Butanedione & Ketone & Butter/creamy/caramel & [28, 29] \\ Ethyl acetate & Ester & Pineapple/fruity/green & [28, 29] \\ Acetoin & Ketone & Butter/creamy/dairy/fatty & [28, 29] \\ 2-Heptanol & Alcohol & Citrus/fruity & [28, 29] \\ 3-Methyl-2-butanol & Alcohol & Cocoa/alcoholic/musty & [29] \\ Isoamyl alcohol & Alcohol & Malay/banana/pungent & [29, 32] \\ 2,3-Dimethylpyrazine & Pyrazine & Caramel/cocoa/coffee/nut & [29, 33] \\ 2,3-Dimethyl-5-ethylpyrazine & Pyrazine & Burnt/roasted/cocoa & [29] \\ Benzaldehyde & Aldehyde & Cherry/almond/bitter & [29, 33] \\ Ethyl Phenylacetate & Esters & Honey/rose/dark & [29] \\ Benzyl alcohol & Alcohol & Sweet/floral/rose/phenolic & [28] \\ Phenylethyl alcohol & Alcohol & Honey/bready/floral & [29] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Major volatile compounds found in the fermented cocoa beans, and the associated aromas. Figure 5: Peak areas of the volatile compounds found in the fermented cocoa bean samples. All values are reported in scientific notation 10\({}^{3}\). Sample codes: B1 = Block 1, B2 = Block 2, Cf = number of the tree, and A or B = position of tree (row).
# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 2 Materials and Methods
### Model Development and Application  
Comparably to its ancestor LISAM [13], the suitability spatial assessment model (SUSAM), developed in this study, is based on S-MCDA [14]. S-MCDA is a group of widely used techniques combining geographic information systems (GIS) and multicriteria decision aiding (MCDA). These techniques allow integrating spatial indicators (map criteria), preferences, and related uncertainties (value judgments) expressed by decision makers or stakeholders. The aim is to obtain information for decision making in an iterative, transparent, and consistent manner [48; 49]. The analytic hierarchy process (AHP) [50; 51] is a widely used approach within S-MCDA [52; 53; 54; 55]. It is based on breaking down the decision-making model into a hierarchical tree. The main goal (in this case the \"landscape suitability\") is at the top, and criteria (groups of services) and sub-criteria (services) are in the descending order within the hierarchy. According to a hierarchical scheme, the structure of SUSAM, derived from LISAM, allows the formulation of comparative judgments of criteria and sub-criteria. The judgements are expressed with iterative pairwise comparisons (PCs) between criteria or sub-criteria, within a reciprocal matrix (the pairwise comparison matrix--PCM) in which evaluations of the different factors are entered using the judgments from the decision makers). PCM is a method developed within the AHP and widely applied in similar studies [13; 15; 54]. Scaled indicators of services are progressively aggregated through weighted linear combinations to obtain the final suitability spatial assessment, using the weights of each service or group of services calculated at each hierarchical level. Since the spatialization of suitability is aimed at defining areas providing services with different characteristics, in SUSAM, relevant scenarios were designed to apply and test the model. Each scenario is designed to represent the interests of a defined stakeholder group or the focus of a different decision. In each scenario, the relevance of a group of services with common characteristics is maximized to identify the areas with high intensity and density of the most relevant services. SUSAM was developed in seven steps (Figure 2): (1) definition of a hierarchical classification including ES and US; (2a) definition of verisimilar scenarios and (2b) service selection and ranking; (3) attribution of relative weight to each service based on AHP. Then, steps dedicated to the spatialization of services in the area were performed: (4) spatial
## 3 Results
### Olfactory Function  
The olfactory function parameters' descriptive statistics and the K-MoCA correlation results are presented on the left side of Table 7. The factor loadings respective to the parameters that make up olfactory PCs, and their K-MoCA correlation results are presented on the right side of Table 7. Among the 104 participants, 3 refused to complete the olfactory identification test. The results of said test shown in Table 7 refer to the 101 participants who completed it. The olfaction threshold and olfactory identification score were not correlated with the K-MoCA score. The only variable with a significant correlation to the cognitive score was the olfactory discrimination score.  
Due to the missing values, the olfactory identification score was omitted from the PCA. The olfaction threshold and discrimination score were combined into one component, which did not correlate with the cognitive score.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **MEAN (SD)** & **R2 (_p_-Value)** & **PC** & **Loading** & **R2 (_p_-Value)** \\ \hline OSI EO & 0.5 (0.2) & 0.016 (0.095) & Eyes open & 0.929 & \\ MLSI EO & 0.2 (0.1) & 0.013 (0.135) & postural & 0.850 & 0.006 (0.252) \\ APSI EO & 0.4 (0.2) & 0.014 (0.135) & stability & 0.803 & \\ \hline FRI & 1.1 (0.4) & 0.011 (0.146) & - & - & - \\ \hline APSI EC & 1.2 (0.6) & 0.002 (0.570) & Eyes closed & 0.926 & \\ OSI EC & 1.5 (0.7) & 0.002 (0.500) & postural & 0.826 & 0.001 (0.674) \\ MLSI EC & 0.6 (0.4) & 0.001 (0.662) & stability & 0.762 & \\ \hline RT EC (s) & 8.1 (13.5) & 0.014 (0.100) & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & 0.799 & \\ RT EO (s) & 40.2 (24.3) & 0.006 (0.310) & & 0.699 & \\ LOS time (s) * & 55.4 (14.1) & 0.048 (0.001) & time * & \(-\)0.587 & \\ \hline \hline \end{tabular} SD, Standard Deviation; RT, Romberg Tandem; EO, Eyes Open; EC, Eyes Closed; OSI, Overall Stability Index; MLSI, MedioLateral Stability Index; APSI, AnteroPosterior Stability Index; FRI, Fall Risk Index; LOS, Limits Of Stability; PS, Postural Stability; PC, Principal Component; * significant correlation with the K-MoCA score.  
\end{table}
Table 6: Participants’ postural stability characteristics.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **MEAN (SD)** & **R2 (_p_-Value)** & **PC** & **Loading** & **R2 (_p_-Value)** \\ \hline Olfaction threshold & 3.8 (2.0) & \textless{}0.001 (0.964) & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & 0.799 & \\ Olfactory discrimination * & 12.5 (3.3) & 0.033 (0.011) & & 0.799 & \\ \hline Olfactory identification \(\dagger\) & 8.2 (2.3) & 0.003 (0.447) & - & - & - \\ \hline \hline \end{tabular} SD, Standard Deviation; PC, Principal Component; * significant correlation with the K-MoCA score; \(\dagger\) results refer to n = 101.  
\end{table}
Table 7: Participants’ olfactory function characteristics.
# Solid-State Rotary Friction-Welded Tungsten and Mild Steel Joints
## 4 Conclusions  
The microstructure, hardness and tensile strength of joints between tungsten and S355 mild steel after high-speed rotary friction welding were investigated, with a focus on changes in physical properties, phase and chemical compositions at the interface. The following main results were obtained:  
* Friction welding of tungsten to S355 steel was successfully carried out directly and without any heat treatment under a shielding gas atmosphere. The maximum friction-welded joint strength of 330 MPa is about 30% of the tungsten base material. The average value of the strength of the joint was significantly lower than the nominal values characteristic for tungsten.
* In the structure of the joint on the tungsten side, no instances of changes that could be responsible for this weakening were observed. The reason may be the high level of residual stresses inherent in tungsten and iron alloy joints.
* The microstructure of friction welds consisted of fine equiaxed grains due to dynamic recrystallization. Moreover, plastic deformation in the direction of the flash is visible mainly on the steel side.
* EDS-SEM maps and scan line analyses across the interface did not confirm the diffusion of tungsten to the iron alloy side. However, the EDS-SEM point analysis indicated a slight amount of tungsten in the Fe matrix close to the interface.
* On the basis of EDS and fracture observation, it can be concluded that the nature of friction welding of dissimilar joint is non-equilibrium based on deep plastic deformation without visible diffusive processes in the interface zone.
* The phase boundary of W-steel is not the weakest element of the joint; fractures during the tensile test propagated through tungsten grains close to the interface.
* The tungsten was weakened by the welding process close to the W-steel interface.  
Figure 14: Macrograph in fracture area of (**a**) the friction-welded joint (tungsten + S355), non-etched; (**b**,**d**) micrograph of the fracture at the edge and axis (**c**) of the bars with higher magnification.  
**Author Contributions:** Conceptualization, B.S. and T.C.; methodology, T.C.; software, M.B. ([PERSON]); validation, M.B. ([PERSON]), P.K. and M.B. ([PERSON]); formal analysis, B.S.; investigation, T.C. and M.K.; resources, P.K.; data curation, M.B. ([PERSON]); writing--original draft preparation, T.C., B.S. and M.K.; writing--review and editing, T.C. and M.K.; supervision, T.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.