esa-sceva/satcom-chunk-collection huggingface dataset:
# Eco-Friendly Metadome-Antenna Innovations for Wearable Millimeter Wave Radar Sensing
###### Abstract  
A compact and low-cost meta-randomized wearable grid array antenna (MTR-GAA) for radar sensing application at 24 GHz is presented. It is based on eco-friendly aluminum-cladded Polypropylene (PP) substrate. The overall MTR-GAA size is 40 \(\times\) 40 \(\times\) 1.74 mm\({}^{3}\). Prototypes are fabricated and tested, achieving consistent agreement between simulation and measurements and meeting typical requirements for the envisioned Electronic Travel Aid (ETA) radar sensing applications to aid visually impaired people. A comparison with state-of-the-art 24 GHz wearable radar antennas is also provided to endorse the advantages of the proposed metadome-antenna ensemble for the target application.  
printed antennas; arrays; metasurfaces; metaardomes; radar sensing; Electronic Travel Aid (ETA) Systems; wearable antenna; mm-wave antenna Article
## 5 Experimental Evaluations and Results
### Data Acquisition  
The NIH Malaria dataset [54] used to evaluate the performance of the compared models which is publicly available on the National Institute of Health (NIH) ([[https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html](https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html)]([https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html](https://lhncbc.nlm.nih.gov/LHC-publications/pubs/MalariaDatasets.html)), accessed date: 18 February 2021). Researchers developed a smartphone application attached with a traditional light microscope [9] for the screening of infected and uninfected red blood cell images at the Lister Hill National Center for Biomedical Communications (LHNCBC), part of the National Library of Medicine (NLM). Thin blood smear films of 50 healthy and 150 _P. falciparum_-infected patients were prepared by using the Giemsa staining solution to enhance the visibility of parasites and were photographed at Chittagong Medical College Hospital, Bangladesh. Images of blood films are captured by the built-in smartphone camera for each microscopic field of view. These images are then manually annotated by expert slide readers at the Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand. The dataset has 27,558 cell images that are equally balanced between 13,779 parasitized and 13,779 uninfected. The cell images have variations in color distributions due to different bloodstains during the process of data acquisition. Figure 6 shows samples of parasitized and uninfected segmented red blood cell images from the malaria dataset.  
Figure 5: Visualizing Convolutional layers of CNN model.
# Additive Manufacturing of PH 13-8 Mo Family: A Review
## 3 DED-Arc of PH 13-8 Mo Family
### Material Properties  
#### 3.3.1 Tensile Properties and Hardness  
Table 5 shows the tensile properties and hardnesses for as-built and heat-treated PH 13-8 Mo processed using DED-Arc from the literature.  
Figure 5: Illustration of the as-built microstructure and aged PH 13-8 Mo produced using DED-Arc with different aging temperatures (Reprinted from Ref. [71], Copyright (2022), with permission from Elsevier).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Ref.** & \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Condition**} & \multirow{2}{*}{**Building**} & \multirow{2}{*}{**Tensile**} & \multirow{2}{*}{**Strength**} & \multirow{2}{*}{**Handsen**} & \multirow{2}{*}{**Cooling**} \\  & **(Data** & **from)** & & & & & & & \\ \cline{5-10}  & & & & & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al. [28]} & \multirow{9}{*}{**DED-Arc**} & AAC \(\times\) & \multirow{9}{*}{**T**\(\times\) 28 \(\times\) 67 \(\times\) 67 \(\times\) 67 \(\times\) 67 \(\times\) 67 \(\times\)  
[PERSON] et al. [28] investigated the as-built tensile properties and hardness of DED-Arc of PH 13-8 Mo. It was concluded that the UTS (Table 5) of the wall was similar compared to CX stainless steel produced using PBF-LB. The hardness of the as-built microstructure was 35.4 \(\pm\) 0.8 HRC. [PERSON] et al. [71] also explored the influence of different heat treatments on the mechanical properties of PH 13-8 Mo manufactured using DED-Arc. The aging procedures were 400, 450, 500, 550, and 600 \({}^{\circ}\)C for 4 h. The specimen aged at 500 \({}^{\circ}\)C had the highest UTS and hardness due to the coherent precipitation hardening. Aging temperature (600 \({}^{\circ}\)C) resulted in the lowest hardness and UTS due to the formation of reverted austenite and Cr-rich carbides. It was apparent from elongation results that there was an anisotropic ductility in the as-built PH 13-8 Mo.  
As mentioned in the previous chapter, [PERSON] et al. [72] showed that direct aging heat treatment increased the hardness and UTS of DED-Arc/PH 13-8 Mo (Table 5).
# Wood Defect Detection Based on Depth Extreme Learning Machine
## 2 Materials and Methods  
### Wood Defect Original Image Dataset  
According to the different processes and causes of solid wood board defects, they are divided into biohazard defects, growth defects and processing defects. Among them, growth defects and biohazard defects are natural defects, which have certain shape and structure characteristics, and are also an important basis for wood grade classification. Generally speaking, solid wood board growth defects and biohazard defects can be divided into: dead knots, live knots, worm holes, decay, etc. The original data set used in the experiment in this article is derived from the wood sampling image in the 948 project of the State Forestry Administration (the introduction of the laser profile and color integrated scanning technology for solid wood panels). When scanning to obtain wood images, the scanning speed of the scanner is 170 Hz-5000 Hz; Z direction resolution is 0.055 mm-0.200 mm; X direction resolution is 0.2755 mm-0.550 mm; and color pixel resolution can reach 1 mm \(\times\) 0.5 mm. The data set includes 5000 defect maps of pine, fir, and ash. The bit depth of each image is 24, and the specified size is at the 100*100 pixel level. Part of the defect image is shown in Figure 1.
## 5 Discussions
### Uncertainties  
It must be noted that some kinds of uncertainties existed in this study. The most obvious uncertainty is the mis-match of acquisition dates of data used in this study. The lidar data was collected in 2012, the field measurements were carried out in 2013, and the UAV stereo imagery was collected in 2014 and 2015. Considering that the forest structure is, in fact, derived from leaf-on stereo imagery, the time interval between lidar and UAV stereo imagery is about two years. No obvious or severe human disturbances occurred in these two year. The forest growth is slow because the growing season of this area is only about four months from late May to early September. It could be anticipated that the correlation between forest biomass from lidar and that from UAV stereo imagery might be improved or at least might not be decreased if these two datasets were acquired in the same period.  
Another uncertainty is the different point density. The point density of lidar data is about 2 - 4 points/m\({}^{2}\) while that of the UAV stereo imagery is higher than 30 points/m\({}^{2}\). The holes within forest canopies can be observed on the DSM of the lidar data, with a resolution of 0.5 m. The point cloud ofUAV stereo imagery was rasterized into the DSM with a resolution of 0.5 m to reduce the uncertainty of unmatched point densities.  
In this study, the forest AGB measurements for the developments of prediction models of UAV stereo imagery was collected on the forest AGB maps of lidar data, due to the unavailability of field measurements within the coverage of UAV stereo imagery. Although the analysis was carried out on the forest AGB, the comparison was in fact equivalently made on the vertical forest structures detected by UAV stereo imagery and those detected by lidar data. The uncertainties, exhibited on fine resolutions such as 15 m \(\times\) 15 m, as shown in Figure 9, could be attributed to their different performances of the detection of forest vertical structures. For example, the small gaps between crowns could be seen by lidar data but missed by UAV stereo imagery, as shown in Figure 5c,d. Inversely, the lidar data was easily affected by the gaps within crowns, while the UAV stereo imagery could describe crowns with good geometrical shapes by overpassing those gaps within crowns. In future studies, the UAV stereo imagery should be further evaluated against the field measurements of trees.
# Design of an Energy Efficient Future Base Station with Large-Scale Antenna System
## 2 System Model
### Large-Scale Multiple-Input Multiple-Output Model
#### 2.2.2 Throughput and Energy Efficiency  
The TP and EE are two important metrics that can be used to conduct a performance analysis.  
Based on the analysis from previous subsections, the symbol received by the \(k\)-th user is given as follows [29]:  
\[y_{k}=\sqrt{\frac{P_{tx}N_{t}}{K}}\mathbf{h}_{k}\mathbf{f}_{k}x_{k}+n_{k}+ \sqrt{\frac{P_{tx}N_{t}}{K}}\sum_{l\
eq k}\mathbf{h}_{k}\mathbf{f}_{l}x_{l} \tag{16}\]  
where \(\mathbf{h}_{i}\) is the \(1\times N_{t}\) channel vector for the \(i\)-th user and \(\mathbf{f}_{i}\) is the \(N_{t}\times 1\) precoding vector for the \(i\)-th user. The last term of (16) is inter-user interference (IUI). Then, the effective SINR for user \(k\), \(\gamma_{k}\), can be represented as follows [4]:  
\[\gamma_{k}=\frac{\frac{P_{tx}N_{t}}{K}|\mathbf{h}_{k}\mathbf{f}_{k}|^{2}}{ \frac{P_{tx}N_{t}}{K}|\sum_{l\
eq k}\mathbf{h}_{k}\mathbf{f}_{l}|^{2}+N_{0}B} \tag{17}\]  
where \(N_{0}B\) is the noise power in the given bandwidth \(B\).  
If we increase the number of TX antennas or reduce the number of users, it is obvious that we can get better channel gain and/or an effective SINR. However, we should keep in mind that, in the practical number of TX antennas, MF precoding does not remove the IUI completely.  
Based on (17), we can define TP as follows:  
\[TP=\alpha B\times\sum_{k=1}^{K}E\left[\log_{2}\left(1+\frac{\frac{P_{tx}N_{t} }{K}|\mathbf{h}_{k}\mathbf{f}_{k}|^{2}}{\frac{P_{tx}N_{t}}{K}|\sum_{l\
eq k} \mathbf{h}_{k}\mathbf{f}_{l}|^{2}+N_{0}B}\right)\right] \tag{18}\]  
where \(\alpha=\left(\frac{T_{sl}-T_{T}}{T_{sl}}\right)\times\left(\frac{T_{t}}{T_{s} }\right)\) is the scaling factor for the pilot overhead and guard interval [29].  
EE can be defined as:  
\[EE=TP/P_{total} \tag{19}\]  
We will use (19) as the metric for the performance analysis in the following sections.
## 3 Results and Discussion
### Density Distributions in the Interfacial Regions of HAp/BC Bilayer Models  
To observe the change in the interfacial behavior with respect to the crosslinking ratio, the density distribution along the \(z\) direction was measured for each HAp/BC model, and the results are shown in Figure 5. Points in the density profiles were obtained with spatial steps of 0.2 A, and the zero point of the \(z\)-axis indicates the surface of the HAp substrate. The condensed BC phases were laid approximately 3 A away from the HAp surface after relaxation. The non-bond interaction between the BCs and the HAp substrate plays a dominant role in the vicinity of the interface, but its effect became much weaker as the distance from the interface increased. One notable point is that the peak magnitudes of the density profiles increased with increasing the crosslinking ratio (from 1.25 g/cc to 1.44 g/cc). In terms of the interactions between polymer-heterogeneous material, these results are quite different from those of other studies using epoxy resins, which reported a decrease in the peak magnitude with increasing crosslinking density [12; 14; 32]. This difference can be attributed to the structure of the polymer network generated during the crosslinking reaction. For a three-dimensional network structure including epoxy thermosets, physical interlocking events induced by the crosslinking reaction occur widely and homogeneously among the polymer matrix [33], which work as spatial constraints that inhibit interfacial effects [12]. On the other hand, for linear polymer structures (i.e., PMMA used in the current study), crosslinking events occur in inhomogeneous media and cause a sparse entanglement within the microstructure. Consequently, the crosslinking reaction on linear BC chains only reduces the diffusivity by removing MMA segments, leading to stable and improved interfacial interactions with the HAp substrate.  
Another interesting aspect is that the oscillation of the BC density profile became clearer as the crosslinking reaction progressed. This was attributed to the fact that linear polymer chains adsorbed on the heterogeneous material tend to generate regular lamellar-type structures [34]. Thus, as the diffusivity decreased and a more stable interaction state was achieved, the oscillation became more pronounced.
## 3 Results and Discussion
### HS-SPME/GC-MS Analysis Results
#### 3.2.5 Acids and Alkenes  
The analysis results of the GC-MS show that only two types of acids and one alkene were detected in the coconut jam samples. The acids were mainly composed of decanoic acid, and the alkenes were mainly composed of (3E)-6-methyl-3-undecene. In general, the contributions of acids and alkenes to the aroma of the coconut jams were comparatively low, due to the presence of compounds that were not particularly odor active. Decanoic acid was present at a relatively high content in the acids, and has rancid and fatty odor characteristics [28] that may negatively affect the overall aroma of coconut jam. As seen from Table 13, the content of decanoic acid gradually decreased from 0 min to post-sterilization. The acids were formed during the early stages of the concentration process. Therefore, to reduce the adverse effects of volatile acids on the overall aroma, the concentration temperature and the agitation speed should be closely controlled in the early processing of coconut jam.
## 2 Materials and Methods  
### Trees under Study  
The study was carried out in 2019 in San Enrique dehesa (Almonte, Huelva, Spain, 37\({}^{\circ}\)15\({}^{\prime}\)43.73\({}^{\prime\prime}\) N, 6\({}^{\circ}\)28\({}^{\prime}\)34.65\({}^{\prime\prime}\) O; 80 m asl), where _Q. suber_ is the main tree species. Previous research on the incidence of the xylophagous cerambycid _Cerambyx welensii_ Kuster in this dehesa allowed us to determine the foliar monoterpene emission profile of certain trees [22]. Specifically, we randomly selected 26 cork oaks: half with a limonene chemotype (limonene accounting for \(>30\%\) of all monoterpene emissions [23]) and the other half with a pinene chemotype (\(\alpha\)-pinene-, \(\beta\)-pinene-, and sabinene-enriched emissions, while limonene accounted for \(<30\%\)) (Figure 1). Those emission profiles were confirmed by gas chromatography-mass spectrometry (GC-MS) analysis of headspace volatiles collected in situ from each studied sprig, following the procedure described in [22]. Briefly, monoterpenes were sampled using an \"aeration system\", as proposed in [24]. That system comprised a measurement chamber (polyester oven bag, Albal, Cofresco, Madrid, Spain), provided with a Teflon sampling line connected to a diaphragm pump (SP 200 EC-LC; Schwarzer Precision, Essen, Germany). Charcoal-filtered air entering the chamber was drawn through a glass sorbent tube (403 Orbo Tenax TA tube, 150 mg, 60/80 mesh; Sigma-Aldrich, Madrid, Spain) for 5 min at a flow rate of 120 mL min\({}^{-1}\). All sample tubes were sealed immediately after collection and stored at 4 \({}^{\circ}\)C and at \(-28\)\({}^{\circ}\)C in the laboratory until analyses (in the following 24 h). Blank controls (empty measurement chambers, not enclosing sprigs) were collected simultaneously using the same sampling system.  
Samples were analysed with a GC-MS system (GC type 6890N, MSD 5973; Agilent, Santa Clara, USA), employing an HP-5 MS column (0.25 mm\(\times\) 30 m\(\times\) 0.25 \(\upmu\)m), helium as the carrier gas (1 mL min\({}^{-1}\)), and the following oven program: the starting temperature (46 \({}^{\circ}\)C) was increased to 70 \({}^{\circ}\)C (at a rate of 30 \({}^{\circ}\)C min\({}^{-1}\)), held steady (4 min), and then increased to 80 \({}^{\circ}\)C (at 5 \({}^{\circ}\)C min\({}^{-1}\)), to 90 \({}^{\circ}\)C (at 4.5 \({}^{\circ}\)C min\({}^{-1}\)), and to 300 \({}^{\circ}\)C (at 50 \({}^{\circ}\)C min\({}^{-1}\)). Peaks were identified by comparison with pure standards (Sigma-Aldrich; Madrid, Spain; purity \(\geq 94\%\)) and with mass spectra in the NIST 02 library (MSD Chemstation Build 75 software, G2070 BA version). For quantitative analysis, calibration curves were determined for \(\alpha\)-pinene, \(\beta\)-pinene, sabinene, limonene, and myrcene.
## 4 Results and Discussion
### Effect of Leading Time on Compressive Strength
The mineralization reaction conditions directly determine the degree of mineralization reaction between fly ash and carbon dioxide and the amount of mineralization reaction products, so the mineralization reaction conditions have an important influence on the internal pore structure and mechanical strength of the mineralized fly ash specimens. Experimental studies have shown that immersion treatment, hot steam treatment, ultrasonic treatment, and stirring treatment will have a positive effect on the internal structure and strength of mineralized fly ash specimens, with immersion treatment being the most significant. Both the improvement in the strength of the material and the optimization of the microstructure are similar to the results of other scholars, such as [PERSON]. During the mineralization reaction between fly ash and carbon dioxide, the concentration of leached dissolved alkaline ions in fly ash is positively correlated with the mineralization reaction products, and the leaching and dissolution rate of alkaline ions directly affects the mineralization reaction rate. The multi-scale effect of micro- and nanopores in fly ash is the main influencing factor restricting the diffusion rate of alkaline ions in fly ash, and the alkaline-ion leaching rate in fly ash is affected by pore size at the microscopic level, while showing a dynamic change with time at the macroscopic level. The change in the concentration of alkaline ions affects the mineralization efficiency and then the structure and mechanical properties of the material. Through experimental research, it has been found that because the alkaline-ion leaching rate in the early stage of fly ash is greater than the rate of calcium silicate formation and consumption, and the alkaline ion concentration increases first and then decreases with the soaking time, the leaching ion concentration increases with time, but the leaching rate in the later stage of the reaction is smaller than the calcium silicate formation and consumption rate, and the ion concentration gradually decreases with the increase in time. Combined with the analysis of ion concentration, the overall trend of mineralization degree with soaking time showed a trend of first increasing, then decreasing slightly, and finally flattening. As the mineralization reaction product continues to form and coat the surface of the original particles, a layered layer of hydration products is formed. The longer the immersion time, the denser the  
Figure 12: The relationship between mineralization degree and the UCS of specimens. generated carbonate becomes, and the tightly packed carbonate fills the pores, improving the compressive strength of the specimen. After the soaking treatment, the structure of the mineralized fly ash specimen can be denser and more stable, the mechanical properties are better, and the strength of the specimen is nearly six times higher than that of the specimen without immersion treatment.
## 4 Results and Discussion
### RAOs in Waves with and without Wind  
RAOs are calculated based on time-domain simulations with white-noise wave excitations, as described in Table 8. The load cases considered here correspond to LCs 2.6 and 3.7 in the OC4, Phase II Project. Four sets of computations are carried out using banded white-noise spectrums with four different seed numbers. The simulations are run for one hour but discarding the first 200 seconds which may include some unsteady transient effects. Auto-spectral density of the input (wave elevation) and cross-spectral density between the input and output (system responses) are computed based on Fast Fourier Transform of the averaged time-domain signals from the four sets of FAST simulations. The ratio of the cross-spectral density and the auto-spectral density gives rise to the RAO [36] which is also named as the frequency-response function  
\[RAO(\omega)=\frac{S_{yy}(\omega)}{S_{xx}(\omega)}, \tag{7}\]  
where the numerator and the denominator on the right-hand side of Equation (7) correspond to the cross-spectral and auto-spectral densities of the input and output signals.  
As seen in Figure 6, since the waves are excited only within the frequency band between 0.05 and 0.25 Hz, analysis results outside the band are meaningless hence all RAOs are only shown within the excited band [37]. Figures 7 and 8 show that in general the wind has little effect on the surge, heave and pitch platform motions, except the lower-frequency region of the pitch RAO, where the existence of wind increases the responses of both the MPM and the SPM systems. The wind influences more significantly on the sway, roll and yaw platform motions, especially the response of yaw in the SPM system. This is easy to understand because there exists no yaw stiffness in the SPM system and the platform can freely change its yaw displacement in the presence of wind. Comparison between Figures 7 and 8 show that generally the RAOs computed by MoorDyn are slightly smaller than those by MAP++, since MoorDyn includes line damping and drag.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Load** & **Description** & **Enabled DOFs** & **Simulation Length** & **Wind Condition** & **Wave Condition** \\ \hline \multirow{3}{*}{V} & RAO & Support & & & & Banded white noise, \\  & estimation, & Support & & & PSD = 1 m\({}^{2}\)/Hz for \\  & no wind & & & & 0.05–0.25 Hz \\  & RAO & Support & & & Banded white noise, \\  & estimation, & Support & & & \\  & with wind & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Properties of load cases in the RAOs (Response Amplitude Operators) computation; PSD: Power Spectral Density.  
Figure 6: Power spectral density of the incident wave elevations with four different wave seeds for the white-noise spectrum.  
Figure 7: Platform motion RAOs computed by MAP++ for the DeepCwind MPM and SPM systems with and without wind: **(a)** in surge; **(b)** in sway; **(c)** in heave; **(d)** in roll; **(e)** in pitch and **(f)** in yaw.