esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Data  
#### 2.1.1 Eddy-Covariance Data  
We used the FLUXNET database ([[http://fluxnet.ornl.gov/](http://fluxnet.ornl.gov/)]([http://fluxnet.ornl.gov/](http://fluxnet.ornl.gov/))) to calibrate the models and to validate GPP estimates. The dataset contains annual files of half-hourly meteorological and flux data from more than 400 EC sites across Europe (CarboEurope), America (AmeriFlux and Fluxnet-Canada), and Asia (AisaFlux and ChinaFLUX), _etc._ To reduce potential errors derived from the observations, we selected the EC towers according to the following criteria: (1) the site provides four or more years of continuous data as a part of the publicly accessible standardized Level 4 or 3 database; (2) a \"site-year\" is accepted for analysis if more than 90% of the half hours in a year contain non-missing values for the meteorological data (downwelling solar radiation, precipitation, wind speed, air temperature and relative humidity), the carbon flux data (net ecosystem exchange and ecosystem respiration (\(R_{eco}\))), and the energy fluxes (net radiation (\(R_{n}\)), ground heat flux (\(G\)), latent heat flux (\(LE\)) and sensible heat flux (\(H\))); and (3) energy balance closure is evaluated for each site-year according to the ratio of the dependent flux variables (\(H+LE\)) against the independently derived available energy (\(R_{n}-G\)) for each half hour [34]. The values of the half-hourly energy balance closure ratio (\(H+LE\))/(\(R_{n}-G\)) deviate from the ideal closure (a value of 1) because random error exists, and the magnitude of the CO\({}_{2}\) uptake is less when the energy imbalance is greater [34, 35]. Thus we recorded the number of daytime half hours of which the ratio was in the range of 0.6-1.4, and then accepted a \"site-year\" when the accumulated number was greater than 60% of the total number of daytime half hours during the growing season. Uncertainties in the EC-measured GPP still exist because of the underestimation of \(R_{eco}\) at night, gap filling algorithm uncertainty, partitioning uncertainty, random uncertainty, and threshold friction velocity uncertainty [36]. We considered the supplied GPP as the \"ground truth\" [18] in this study.  
Finally, 102 site-years, which represent six biome types across two main climatic environment zones (_i.e._, plant functional types, PFTs) [37] at 21 EC sites [38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64], were selected (Table 1). Seven of these sites are in boreal regions, and fourteen sites are in temperate regions. Five sites that have Mediterranean climates were characterized as being in the temperate zone because of the absence of Mediterranean forest in the PFTs we used [37]. Though the three sites, CA-Ca1, CA-Ca2 and CA-Ca3, were located in adjacent areas, their planting years were 1949, 2000 and 1988, respectively, indicating different tree ages. This is a similar case with CA-Ojp and CA-Obs; their planting years were 1929 and 1879, respectively. We used two years of each site for model calibration, and another two consecutive years for validation.
## 3 Datasets and Methods
### Pre-processing of the Pleiades Images  
All images were not provided in the same coordinate system. Therefore, all images were first re-projected in the Universal Transverse Mercator (UTM), Zone 30 North--World Geodetic System 1984 (WGS84). Radiometric calibration and atmospheric correction were then applied to each image in order to convert the Level 1 (L-1) images, provided in digital counts (DC), to images in the surface reflectance values. Then, the surface reflectance was divided by \(\pi\) to obtain \(R_{\text{rs}}\). Atmospheric corrections were performed using the optical calibration module from OTB--Monteverdi software [49], converting all images to the top of the canopy reflectance. Both sun and viewing angles (elevation and azimuth), as well as the calibration coefficients (gain/biases and solar illuminations), were obtained from the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Acquisition Date** & **Season** & \
\begin{tabular}{c} **Acquisition Time** \\ **(UTC)** \\ \end{tabular} &
\begin{tabular}{c} **Time of Low Tide** \\ **(UTC)** \\ \end{tabular} \\ \hline
**Pleiades-1A** & 25/04/2013 & Spring & 11h15 & 12h04 \\
**Pleiades-1A** & 03/08/2016 & Summer & 11h15 & 12h45 \\
**Pleiades-1A** & 06/10/2016 & Autumn & 11h23 & 14h59 \\
**Pleiades-1B** & 24/05/2017 & Spring & 11h04 & 11h06 \\
**Pleiades-1A** & 07/10/2017 & Autumn & 11h08 & 13h27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pleiades satellite data set information.  
images' metadata files. The applied aerosol model was chosen, depending on the corresponding season of each image, following the methods in Reference [50]. The atmospheric pressure, aerosol optical thickness and ozone and water content values were obtained from NASA's Aerosol Robotic Network (AERONET) for the Arcachon station at the corresponding acquisition time of the satellite image. The atmospheric corrections in the blue band (B) present the highest residual errors. For this reason, and because the B-band does not provide useful additional information in the identification of vegetation, this band was removed from all images.  
For each image, we computed the Normalized Vegetation Index (NDVI, [51]) as:  
\[NDVI=\frac{R_{rs}(\lambda_{\textit{NIR}})-R_{rs}(\lambda_{R})}{R_{rs}(\lambda_ {\textit{NIR}})+R_{rs}(\lambda_{R})} \tag{2}\]  
Other indexes proposed in the literature for assessment of vegetation cover were tested, but they did not present significant differences relative to the NDVI. The NDVIs were also estimated for the field reflectance data, and the values obtained for each species were compared to the ones estimated from the satellite images. Based on the analysis of the NDVI maps, for each image, we performed a prior assessment of their capability of identifying the different types of habitats and species using classification methods.
## 2 Materials and Methods  
### Materials  
#### 2.1.1 Plant Material, Model Macromolecules, and Biological Test Systems  
Mountain Germander (_T. montanum_ L.) was purchased from the local supplier (Ljekovito, bilje j.d.o.o, Zadar, Croatia). The plant was collected in August 2020 (municipality of Kistanje, Sibenik-Krin County, Croatia), with the voucher specimen (ID: 75518) deposited in the Herbarium Croaticum (Faculty of Science, Zagreb, Croatia). Sorted and sieved areal parts (flowers, stems, and leaves) of the plant (<450 \(\upmu\)m) were used in all experiments.  
Bovine serum albumin was obtained from Sigma-Aldrich (Steinheim, Germany). A double-stranded, covalently closed, circular form of PhiX174 RFI DNA was purchased from the Promega Corporation (Madison, WI, USA).  
Continuous human cell lines, i.e., epithelial tongue carcinoma (CAL 27; ATCC CRL-2095), gastric adenocarcinoma (AGS, ATCC CRL-1739), epithelial colorectal adenocarcinoma (Caco-2; ATCC HTB-37), and hepatocellular carcinoma (HepG2; ATCC HB-8065), were grown in a monolayer in T-flasks (37 \({}^{\circ}\)C, controlled CO\({}_{2}\) atmosphere), in a complete RPMI culture medium (Capricorn Scientific GmbH, Ebsdorfergrund, Germany) with 10% fetal bovine serum (FBS), except for the AGS cell line, for which Ham's F-12 culture medium (Capricorn Scientific GmbH, Ebsdorfergrund, Germany) was used. Based on the number of cells determined using the Burker-Turk chamber in the suspension after the trypsinization of cultivated cells, an initial cell concentration of 10\({}^{5}\) cells mL\({}^{-1}\) was set for further experiments.  
Bacterial strains, i.e., _Escherichia coli_, _Lactobacillus plantarum_, and _Staphylococcus aureus_, were stored at \(-80\)\({}^{\circ}\)C with the addition of 10% glycerol, as the part of the Collection of Organisms of the Laboratory for Biology and Genetics of Microorganisms at the Faculty of Food and Biotechnology (University of Zagreb, Zagreb, Croatia). For the experiments, bacterial cultures were cultivated in the complete Luria-Bertani culture medium; a complete Man, Rogosa, and Sharpe broth culture medium; and a selective minimal culture medium with mannitol, respectively. The culture media mentioned were prepared according to the standard instructions.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Gene** & **SNP** & **Adjustment \({}^{\text{a}}\)** & **Additive Model** & **Dominant Model** & **Recessive Model** \\ \hline \hline
**Group** & & & **_p-Value, OR_** & _p-Value, OR_ & _p-Value, OR_ (95\% CI)_ & _p-Value, OR_ (95\% CI)_ \\ \hline Females & DACH1 & rs626277 & A & 0.332, 0.821 & 0.264, 0.66 & 0.791, 0.929 \\  & & & (0.552-1.223) & (0.318–1.369) & (0.538–1.603) \\  & & & 0.853, 0.944 & 0.837, 1.116 & 0.472, 1.344 \\  & & & (0.515–1.73) & (0.392–3.175) & (0.601–3.004) \\  & PRKAG2 & rs7805747 & A & 0.019, 0.584 & 0.001, 0.241 & 0.324, 1.307 \\  & & & (0.372–0.917) \({}^{*}\) & (0.101–0.574) \({}^{*}\) & (0.768–2.224) \\  & & & 0.382, 0.743 & 0.039, 0.299 & 3.91, 1.41 \\  & & & (0.381–1.447) & (0.095–0.939) \({}^{*}\) & (0.644–3.088) \\  & & & 0.155, 1.327 & 0.249, 1.409 & 0.242, 1.496 \\  & & & (0.899–1.96) & (0.786–2.525) & (0.762–2.938) \\  & & & 0.444, 1.272 & 0.539, 1.317 & 0.485, 1.417 \\  & & & (0.687–2.355) & (0.547–3.173) & (0.533–3.765) \\ \hline Males & DACH1 & rs626277 & A & 0.607, 0.886 & 0.471, 0.73 & 0.925, 1.031 \\  & & & (0.557–1.408) & (0.311–1.716) & (0.542–1.965) \\  & & & 0.607, 1.211 & 0.927, 1.059 & 0.627, 1.262 \\  & & & (0.584–2.508) & (0.311–3.603) & (0.494–3.224) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Multivariable logistic regression analysis of single nucleotide polymorphisms (SNP) related to chronic kidney disease. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Gene Symbol (Locus)** & **Groups** & **Wild-Type** & **Heterozygous** & **Homozygous** & **Carrier F** & **Allele F** \\ \hline DACH1 (rs626277) & & & & & & & \\ Female, n (\%) & Patient & 37 (38.9) & 45 (47.4) & 13 (13.7) & 61.4\% & 30.2\% \\  & Control & 48 (37.2) & 56 (43.4) & 25 (19.4) & 62.8\% & 41.1\% \\  & \(p\) value, OR & & 0.9, 1.0 (0.6–1.9) & 0.3, 0.7 (0.3–1.5) & 0.8, 0.9 (0.5–1.6) & 0.4, 0.9 (0.6–1.3) \\  & \(p\) value, OR & & & & & \\ Male, n (\%) & Patient & 27 (39.7) & 31 (45.6) & 10 (14.7) & 60.3\% & 37.5\% \\  & Control & 36 (40.4) & 36 (40.4) & 17 (19.1) & 59.5\% & 39.3\% \\  & \(p\) value, OR & & 0.7, 1.1 (0.6–2.3) & 0.6, 0.8 (0.3–1.98) & 0.9, 1.0 (0.5–1.96) & 0.5, 0.9 (0.6–1.5) \\ \hline PRKAG2 (rs7805747) & & & & & & \\ Female, n (\%) & Patient & 43 (45.3) & 45 (47.4) & 7 (7.4) & 54.8\% & 31.1\% \\  & Control & 67 (51.9) & 30 (23.3) & 32 (24.8) & 48.1\% & 36.4\% \\  & \(p\) value, OR & & 0.006, 2.3 (1.3–4.3) \({}^{*}\) & 0.019, 0.3 (0.1–0.8) \({}^{*}\) & 0.3, 1.3 (0.8–2.2) & 0.2, 0.8 (0.5–1.2) \\  & \(p\) value, OR & & & & \\ Male, n (\%) & Patient & 26 (38.2) & 29 (42.6) & 13 (19.1) & 61.7\% & 40.4\% \\  & Control & 49 (55.1) & 26 (29.2) & 14 (15.7) & 44.9\% & 30.3\% \\  & \(p\) value, OR & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Prevalence of the genotypes and alleles of the rs11959928 (DAB2), rs626277 (DACH1), and rs7805747 (PRKAG2) polymorphisms related to chronic kidney disease (CKD) in controls and patients.
# Unsupervised Scene Image Text Segmentation Based on Improved CycleGAN
## 3 Scene Text Segmentation Image Generation
### Loss Functions
#### 3.3.2 Cycle-Consistent Loss  
CycleGAN adds cycle-consistent loss to learn the mapping of \(G_{B}\) and \(G_{A}\). \(G_{A}\) is used to transform the image \(a\) in domain \(A\) into domain \(B\) space and then restore it to \(a\). Images in domain \(A\) may all be converted to the same image in domain \(B\). To avoid this situation, calculate the loss between \(a\) and \(G_{B}(G_{A}(a))\). Similarly, all images in the \(B\) domain may be converted to the same image in the \(A\) domain. Again, the loss between \(b\) and \(G_{A}(G_{B}(b))\) needs to be calculated to avoid this. The cycle-consistent loss function was added to make the training process of CycleGAN more stable. Add restrictions to preserve the characteristics of the input image and reduce the mapping space. The loss function for cycle-consistent is defined as follows:  
\[L_{cyc}(G_{A},G_{B})=E_{a\sim P_{\text{data}\,(a)}}\left[\left\|G_{B}(G_{A}(a)) -a\right\|_{1}\right]+E_{b\sim P_{\text{data}\,(b)}}\left[\left\|G_{A}(G_{B}( b))-b\right\|_{1}\right] \tag{5}\]
## 2 Materials and Methods
### Retention of \(\beta\)-Blockers on CSPs  
Often in chiral liquid chromatography, the retention of -blockers and separation selectivity of enantiomers can be connected to a certain type of CSP. Typically, this occurs if one of the three interactions required for chiral recognition of enantiomers is dominant. For this reason, the dependences of retention factors (\(k\)) on hydrophobicity measure as logP were checked for E-CSP as a standard stationary phase for comparison. As expected, no correlation was observed in polar organic mode when 100% organic solvent containing mobile phase was used in combination with a relatively hydrophilic stationary phase. EAg-CSP with removed saccharide residues from eremomycin should be the most hydrophobic among studied CSPs. However, no visible correlation between \(k\)' and logP was observed.  
All studied  
Figure 4: Separation of labelatol enantiomers on CSPs with immobilised eremomycin (E-CSP) deseremosaminyl eremomycin (DEE-CSP) and eremomycin aglycons (EAg-CSP) chiral columns (250 \(\times\) 4.0 mm I.D.): eluent: 50% methanol—50% acetonitrile containing 0.1% TEAA: flow rate 0.7 mL/min, temperature 25 \({}^{\circ}\)C: injection volume 10 L, UV detection at 210 nm. The numbers indicate peak areas.  
of peaks are observed for E-CSP in less than 20 min (see Figure 4). However, according to the chromatographic theory, the retention time impacts strongly on the resolution of chromatographic peaks proportionally \(k^{{}_{2}}\)/(\(k^{{}_{2}}\) +1), so, in this work, only the use of a weaker eluent 50% methanol-50% acetonitrile containing 0.1% TEAA allowed better resolution of -blockers peaks.
residues provide effective shielding of selector sites, which cause strong adsorption but non-emantioselective interactions with -blockers. One more option relates to the possibility of obtaining an optimal configuration for carbohydrate residues, which can be available for hydrogen bonding interactions in chiral recognition (Figure 2).
## 6 Conclusions  
Aiming at the mixing flow pattern of a WTTB, a regional mixing flow pattern and an isolated partition iterative algorithm are proposed, the thermoelastic dynamics lubrication performance of the WTTB is analyzed, and a method for the control of flow pattern is created. Our conclusions are as follows:  
1. The lubrication performances obtained under the four flow pattern modes were quite different, and the values of the mixing flow pattern were between those of the laminar and turbulent flow patterns. This shows that it is very important to choose a suitable flow pattern mode for the design of the WTTB.
2. With a change in the speed or load, the area of the laminar and the turbulent flows at the bearing interface will change correspondingly, but the single flow pattern cannot reflect the transition phenomenon. The regional mixing flow pattern can automatically degenerate into a single flow pattern with the change in the operation conditions, which makes it more suitable for the WTTB under variable operation conditions.
3. The calculation example shows that both the circumferential bulge and radial concave pad surfaces are beneficial for increasing the turbulent area of the pad and the film thickness, thereby improving the load-carrying capacity. Additionally, it is feasible to adjust the flow pattern by changing the pad shape. The method proposed in this paper can be used as a reference for bearing design procedures.
# Error Correction of Water Vapor Radiometers for VLBI Observations in Deep-Space Networks
## 4 Conclusions  
In this paper, we developed a water vapor radiometer for VLBI observations. We describe the details of the design of the water vapor radiometer, the sources of measurement errors, and the correction methods. By using the four error correction methods, the long-term measurement accuracy of the brightness temperature reaches 0.2 K, whereas the measurement accuracy of the wet delay is 15 mm (tilted path 20 mm). The water vapor radiometer satisfies the expected requirements of our project, but there is still a big gap compared with the experimental results of JPL. The AWVR brightness temperature measurement accuracy developed by JPL can reach 0.1 K, and the wet delay measurement accuracy can reach 1 mm. This excellent performance is mainly due to the design of the integrated receiving system, excellent thermostatic control system, and larger antenna (1.8 m), which can obtain a higher system sensitivity and can reduce the influence of the sidelobe. At the same time, the choice of measurement frequency is also an important factor. We use fewer measurement channels. In designing the next generation water vapor radiometer, more radiometer measurement channels will be added in order to obtain sufficient atmospheric radiation information. Future work will focus on the further improvement of hardware and an exploration of error calibration methods. We also observed that the measurement results are poorer in the case of more rain or clouds, which is mainly due to the influence of liquid water [11; 12; 13]. This work can be extended to select the optimal frequency and to further optimize the algorithm performance.  
**Author Contributions:** Conceptualization, Z.Z.; Funding acquisition, Q.K.; Investigation, J.G.; Methodology, H.C. and Q.Z.; Project administration, Q.Z.; Software, H.C.; Supervision, J.G.; Validation, Q.K.; Writing--original draft, H.C.; Writing--review & editing, Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data set available on request to corresponding authors.  
**Acknowledgments:** Thanks to Shanghai Astronomical Observatory, Chinese Academy of Sciences for their supports and suggestions in improving the stability of the water vapor radiometer.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 2 mmWave-Integrated Cellular Networks  
### Control/User Plane Splitting for HetNet  
In CP/UP splitting [14; 23; 42], also referred to as \"phantom cell\" or \"soft cell\", the CP and UP are handled separately. In a CP/UP-split HetNet, the mobile stations (MS), also referred to as user equipment (UE), are capable of maintaining a logical link with both the macrocell and multiple small cells. This capability, called dual connectivity (DC), allows the CP and UP to be handled individually and flexibly. The single CP also enables efficient, centralized radio resource control (RRC), for example mobility control or beamforming (BF). DC affects UE peak power consumption because the UE has to activate multiple RF devices at the same time. However, the higher throughput makes the download time shorter, allowing the device to go to the sleep state soon after the download has been finished. In [43; 44], it is indicated that average UE power consumption with two frequency carrier aggregation does not exceed that with single frequency carrier. Furthermore, a 99.7% reduction of UE power consumption could be achieved via a combination of traffic offloading and dynamic on/off [45].  
To achieve CP/UP splitting, the macrocell and small cells are connected via a backhaul interface used for exchanging control information and user data and are controlled by the same RRC entity as shown in Figure 1. The backhaul, as the intermediate link between the cells and the centralized network, is therefore an essential part of the CP/UP-split HetNet. The use of mmWave technologies in the backhaul is discussed in Section 3.  
The DC and CP/UP splitting feature was introduced in Release 12 of the 3 GPP LTE standard [42]. In Release 13, it was extended to support DC between the LTE and an IEEE 802.11 wireless local area network (WLAN) [46; 47]. The detailed architecture of LTE-WLAN CP/UP splitting in Release 13 is discussed in Section 2.3. The CP/UP splitting concept must be a key enabler of HetNet.
## 3 Results
### Key Metabolic Pathway Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Pathway** & **Hits** & **Expect** & _p-Value_ & **Holm P** & **FDR** \\ \hline \multirow{4}{*}{CON vs. HY + IR} & Biosynthesis of unsaturated fatty acids & 3 & 0.141 & 0.000226 & 0.019 & 0.019 \\  & Steroid biosynthesis & 2 & 0.164 & 0.0102 & 0.848 & 0.356 \\  & Fatty acid biosynthesis & 2 & 0.184 & 0.0127 & 1 & 0.356 \\  & Fatty acid elongation & 1 & 0.148 & 0.14 & 1 & 1 \\  & Fatty acid degradation & 1 & 0.152 & 0.143 & 1 & 1 \\ \hline \multirow{4}{*}{Sup-HY + IR} & Biosynthesis of unsaturated fatty acids & 2 & 0.0703 & 0.00158 & 0.133 & 0.133 \\  & Fatty acid elongation & 1 & 0.0742 & 0.0724 & 1 & 1 \\ \cline{1-1}  & Fatty acid degradation & 1 & 0.0762 & 0.0743 & 1 & 1 \\ \cline{1-1}  & Steroid biosynthesis & 1 & 0.0820 & 0.0799 & 1 & 1 \\ \cline{1-1}  & Fatty acid biosynthesis & 1 & 0.0918 & 0.0891 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Although the differential metabolites in the Sup-HY and Sup-HY + IR groups were enriched in two and five metabolic pathways, respectively, their impact on these pathways was not significant. Notably, the pathways of fatty acid elongation and fatty acid degradation emerged as characteristic metabolic pathways in the _B. dorsalis_ larvae following IR treatment (IR-alone, HY + IR, and Sup-HY + IR). Furthermore, IR treatments under both the hypoxic and super-hypoxic conditions influenced five metabolic pathways, the biosynthesis of unsaturated fatty acids, fatty acid biosynthesis, fatty acid elongation, fatty acid degradation, and steroid biosynthesis. The findings seem to correlate with the developmental results of the six groups of _B. dorsalis_ larvae described in Section 3.1. Hence, these five metabolic pathways might play an important role in influencing the sensitivity of _B. dorsalis_ larvae to IR.
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## 3 Results and Discussion
### Related Work  
Table 3 shows the comparison between the proposed antenna and recent work reported in the literature, including antenna material, size, bandwidth, and gain. It can be seen from Table 3 that our proposed antenna has a relatively high gain. Knit fabric is the most widely used fabric in our daily clothing due to its superior flexibility and breathability, but its poor dielectric properties limit its applications in wearable antennas. In this work, the dielectric constant of knit fabric was enhanced by the printing of BT dielectric ink. Instead of using densely woven fabric, filter paper, and PDMS (Polydimethylsiloxane), as shown in Table 3, we employed a combination textile substrate (six layers of knit fabric and two layers of nonwoven fabric) that shows great flexibility and is more suitable for wearable applications. The success of using BT dielectric ink to modify the dielectric properties of porous materials could expand the options of textile materials as antenna substrates.  
Figure 7: (**a**) Experimental setup for the textile antenna with the combination substrate (two layers of nonwoven fabric and six layers of knit fabric spray coated with BT dielectric ink) for 3D radiation pattern measurements in an anechoic chamber. (**b**) Maximum gain plot at \(\varphi=0^{\circ}\). (**c**) Two-dimensional radiation pattern of the textile antenna at 2.3 GHz and \(\varphi=0^{\circ}\). (**d**) Three-dimensional radiation pattern of the textile antenna plotted in dB at 2.3 GHz.