esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results Analysis
### Meta-Atom Array Configurations
#### 5.2.2 \(\times\) 2 Array Analysis  
The same methodology is applied to investigate the 2 \(\times\) 2 array configuration at 0-degree, 90-degree, 180-degree and 270-degree rotations. It is visible from Figure 11a that there is a negative value from 7.84-14 GHz for permeability; a negative value from 4.56-7.79 GHz, 8.13-8.69 GHz and 8.92-10.36 GHz for permittivity; and a negative value from 4.48-6.68 GHz, 7.80-10.22 GHz and 12.22-14 GHz for the refractive index at zero-degree rotation. In addition, Figure 11b exhibits negative values from 7.74-14 GHz for permeability; negative values from 3.96-8.63 GHz and 8.74-10.13 GHz for permittivity; and negative values from 4.31-6.48 GHz, 6.98-7.52 GHz and 7.76-9.64 GHz for the refractive index at 90-degree rotation. Furthermore, in Figure 11c at 180-degree rotation, the unit cell displays negative permeability from 7.85-14 GHz; negative permittivity from 4.59-7.74 GHz and 8.94-10.60 GHz; and a negative refractive index from 4.48-6.70 GHz, 7.82-10.28 GHz and 12.27-14 GHz. Additionally, negative permeability from 7.77-14 GHz, negative permittivity from 4-10.16 GHz and a negative refractive index from 4.40-6.52 GHz and 7.77-9.69 GHz are shown in Figure 11d for 270-degree rotation of the proposed meta-atom unit cell. As the size of the structure increases, the resonance frequencies decrease, and the number of resonances also increases. The overall size of the structure is responsible for creating a larger inductive effect. With increasing inductance, the resonance frequency shifts to lower frequencies. In addition, splits and gaps are formed by the capacitive effect. As the number of unit cells varies, the number of splits and gaps between the resonators increases with the overall size of the unit cell. With the increasing number of splits and gaps, more capacitance and resonance points are formed. In addition, if the inductive and capacitive effects increase, the inductance and capacitance minimize each other and create more stop bands and resonance points. Moreover, the resonances also depend on the material's internal structure. When EM-waves propagate through a material, its electric and magnetic fields oscillate in a sinusoidal pattern, and their velocity depends on the material's electrical conductivity, which in turn depends on the material's internal structure. The relative speed of electrical signals traveling through a material varies according to how they interact with its internal structure. Moreover, from Table 4, the effective medium ratio at zero-degree and 180-degree rotation is 8.6 and 4.5 for 90-degree and 270-degree rotation.
## 4 Discussion  
The combined effects of several factors explain the improved hardness of the specimens. As noted earlier, B4C particles have the third highest hardness of known materials after diamond and boron nitride [47], and it is logical to assume that their incorporation into the aluminum matrix increases the hardness because of the parallel increase in the plastic deformation resistance [48] or indentation resistance in the case of hardness measurements [49]. The relatively low level of agglomeration or clustering observed in the SEM images and the relatively low porosity also favor an increase in hardness [48]. The initial fine particles provide better hardness in the composite than coarse particles, because such composites contain more particles at the interface and within the microstructure [50]. In addition, the deposition of small particles with the formation of the Ginner-Preston (GP) zones caused by T6 heat treatment positively affects the hardness of the specimens [25]. The highest increase in hardness observed in the S2-HT specimens is because the unreinforced specimens do not have fine particles that can precipitate. Therefore, the observed effect in the S1-HT specimens is much lower than that in the S2-HT specimens. For this reason, T6 heat treatment is mainly applied to various aluminum alloys containing hardening solid phases [51; 52; 53] or specially fabricated AMCs [54; 55; 56] rather than pure Al. The improvement in the hardness was mainly attributed to the improvement in the mechanical behavior of the specimens. Reinforcement with B4C particles reduces the ductility, causing brittleness of the specimens, but simultaneously increases the strength of the composites by increasing their resistance to tensile stresses [57; 58]. Phenomena such as grain boundary strengthening, strengthening from fine particles [59], and dislocation phenomena [17] efficiently improve composite strength. T6 heat treatment strengthens the bonding of B4C with the Al matrix, improving the mechanical behavior of the composites [60]. The mechanical behavior is favorably affected by the low porosity owing to the low proportion of casting defects [61]. The improved strength and sufficiently high ductility values of the S2 and S2-HT reinforced specimens make it possible to use them for further forming, namely calibrated rolling [27; 28], to evaluate the machinability by plastic deformation and further improve the mechanical characteristics, as prerequisites for this exist [26; 62; 63]. In summary, the experimental attempt made in this study to produce AMCs with and without HT minorly reinforced (~0.36 wt %) with fine B4C particles to improve the mechanical characteristics of pure Al, which were initially very poor, has been successful.
## 2 Materials and Methods
### Microalgal Growth and Chemical Analyses  
A hemocytometer (C-Chip DHC-N01, INCYTO, Cheonan-si, Korea) and phase contrast microscope (Axioskop 2 plus, ZEISS, Jena, Germany) were used to observe cell growth and calculate cell density (CD). Biomass production was quantified by the gravimetric method measuring dry cell weight (DCW). The sample was filtered through a weighed glass fiber filter (GF/C, pore size 1.2 \(\upmu\)m, 47 mm, Whatman(r), Maidstone, UK) using a vacuum pump and heated at 105 \({}^{\circ}\)C for 2 h in a dry oven. Paramylon (PM) production was quantified using the gravimetric method [9]. The lyophilized biomass was suspended in acetone and sonicated for cell disruption. After centrifugation, the pellet was resuspended in 1%(\(w/v\)) SDS, heated at 100 \({}^{\circ}\)C, and cooled to room temperature. The suspension was centrifuged and washed twice with distilled water. The final pellet was dried overnight at 60 \({}^{\circ}\)C prior to weighing. Based on the measurements, the biomass productivity, paramylon productivity, specific growth rate (SGR), doubling time, and CO2 biofixation rate were calculated using the following equations:  
\[\text{Biomass productivity (unit: g L}^{-1}\text{ d}^{-1})=\text{DCWt}/\text{t}, \tag{1}\]  
\[\text{Paramylon productivity (unit: g L}^{-1}\text{ d}^{-1})=\text{PMt}/\text{t}, \tag{2}\]  
\[\text{Specific growth rate (unit: d}^{-1})=\text{ln (CD}_{2}-\text{CD}_{1})/(\text{t}_{2}-\text{t}_{1}), \tag{3}\]  
\[\text{Doubling time (unit: d})=\text{ln 2}/\text{SGR}, \tag{4}\]  
\[\text{CO}_{2}\text{ biofixation rate (unit: g L}^{-1}\text{ d}^{-1})=\text{1.88 }\times\text{biomass productivity}, \tag{5}\]
## 6 Results and Discussion  
The design of the key exchange protocol is based on the traditional Diffie-Hellman key agreement protocol, SHA256 hash function for digital signature of the shared secret and AES encryption to provide data confidentiality.
## 4 Experiments and Results
### Results for Coverage and Packet Delivery Ratio  
In Figure 2, we can see our first simulation results for PDR and CR across the number of nodes and mobility speed factors. Each method's performance is averaged over 30 simulation seeds. Each plot compares the reference models (D-FL+RL, RL-PT, NFRL) and our proposed models A and B (the model B with three different parameter profiles).  
Key Observations
* NFRL and D-FL+RL offer relatively high Coverage/PDRs in moderate-sized networks, but they dip slightly in extremely dense conditions (e.g., \(\geq\) 240 nodes), where collisions hamper full delivery.
* RL parameter tailoring (RL-PT) remains competitive, but sometimes lags behind the fuzzy-based approaches in mid-range densities, possibly because abrupt parameter-weighting changes can destabilize the broadcast decision.  
Figure 2: (**Top row**): (**a**) Coverage vs NN, (**b**) PDR vs NN. (**Bottom row**): (**c**) Coverage vs. Mobility Speed, (**d**) PDR vs. Mobility Speed.  
* Proposed Method A (with dynamic membership ranges) consistently attains Coverage above 99% across various scenarios. Its main input parameters emphasize preserving Coverage first, then adjusting the broadcast ratio only within safe limits.
* Proposed Method B outperforms the others in dense (\(200+\) nodes) scenarios, especially with Profile 3 (optimal). By switching to parameters such as Number of Neighbors, RSSI, and Connection Time, Method B effectively reduces collisions and improves Coverage/PDRs under severe congestion.
## 2 Mathematical Model  
We denote \(\Omega=\Omega_{1}\bigcup\Omega_{2}\) the filtration domain, bounded on the outside by a closed smooth surface \(\Sigma_{1}\). \(\Sigma_{2}\)-boundary between the more permeable external subdomain \(\Omega_{1}\) and the less permeable internal subdomain \(\Omega_{2}\). An example of the computational domain is shown in Figure 1 (on the right).  
Fluid filtration in both subdomains is described by [PERSON]'s law for unknown fields of velocity \(\mathbf{v}\) and pressure \(p\). In the external subdomain, the velocity satisfies the continuity equation. In the internal subdomain, [PERSON]'s law describes the absorption of fluid in capillaries. This model is similar to one given in paper [9]. Thus, the filtration-absorption problem can be written as a system of partial differential equations (1).  
\[\begin{cases}\mathbf{v}(x)=-\dfrac{\kappa_{i}}{\mu}\
abla p(x)\quad x\in\Omega_{ i},\,i=1,2,\\ \
abla\cdot\mathbf{v}(x)=0,\quad x\in\Omega_{1},\\ \
abla\cdot\mathbf{v}(x)=-L_{b}A(p(x)-p_{b}+\sigma\cdot\Delta\pi),\quad x\in\Omega _{2};\end{cases} \tag{1}\]  
The model parameters are \(\kappa_{i}\)--the coefficients of hydraulic conductivity in subdomains \(\Omega_{i}\), \(i=1,2\), \(\mu\)--the lymph dynamic viscosity, \(L_{b}\)--the blood vessels' absorption coefficient, \(A\)--the surface area of the blood vessels, \(p_{b}\)--the blood pressure, \(\Delta\pi\)--the mean difference in the oncotic pressure of blood and lymph, and \(\sigma\)--the oncotic reflectance.  
The outer boundary is divided into three parts: the openings of the afferent (inlet) vessels with the given lymph inflow (\(\Sigma^{q}\)), the openings of the efferent (output) vessels with the given pressure (\(\Sigma^{p}\)), and the impenetrable outer border with zero lymph flow (\(\Sigma^{0}\)), \(\Sigma_{1}=\Sigma^{0}\bigcup\Sigma^{q}\bigcup\Sigma^{p}\). The inner boundary \(\Sigma_{2}\) is homogeneous. The pressure and the normal component of the velocity vector must be continuous on this boundary.  
We assume that each of the surfaces \(\Sigma_{m}\), \(m=1,2\) is oriented so that the outer side is considered positive. Let \(\mathbf{n}=\mathbf{n}(x)\), \(x\in\Sigma_{m}\), \(m=1,2\), be the unit vector of the outer  
For simplicity, we introduce the notation:  
\[\alpha_{i}=\frac{\mu}{\kappa_{i}},\quad L=L_{b}A,\quad p_{v}=p_{b}-\sigma\cdot \Delta\pi\]  
We exclude the variable \(v\) from the system (1) by applying the \(\
abla\cdot\) operator to the first equation of system (1) and substituting the second and third equations.  
The pressure in the external subdomain \(\Omega_{1}\) satisfies the Laplace equation.  
\[\Delta p(x)=0,\quad x\in\Omega_{1}. \tag{6}\]  
In the internal subdomain \(\Omega_{2}\), the pressure satisfies the Helmholtz equation:  
\[\Delta p(x)-\lambda^{2}p(x)=\lambda^{2}p_{v},\quad x\in\Omega_{2},\quad \lambda=\sqrt{L\alpha_{2}}. \tag{7}\]  
If \(p=\bar{p}+p_{v}\), then \(\Delta\bar{p}-\lambda^{2}\,\bar{p}=0\).
## 2 Materials and Methods
### Volatile Compound Analysis  
#### 2.3.1 HS-SPME Conditions  
Fresh white ginseng roots were manually ground in liquid nitrogen using a mortar and pestle to prepare ginseng pastes. Two grams of ginseng paste sample were placed into a 20 mL headspace vial containing internal standard (2-methylbutyl 2-methylbutyrate, the final concentration of 1 \(\upmu\)g/mL in the vial) and 3 mL of distilled water. The stock solutions of the internal standard were prepared by dissolving the pure chemical in methanol to give a concentration of 10 mg/mL. An SPME fiber coated with divinylbenzene/polydimethylsiloxane (DVB/PDMS, 65 \(\upmu\)m film thickness, Supelco, Bellefonte, PA,USA) was used in combination with a PAL AOC-6000 Autosampler (Shimadzu, Columbia, MD, USA). The SPME fiber was preconditioned at 250 \({}^{\circ}\)C for 10 min to remove contaminants. The sample vial was equilibrated at 50 \({}^{\circ}\)C for 40 min in a thermostatic autosampler tray. After equilibration, the fiber was exposed for 20 min at 50 \({}^{\circ}\)C with agitation speed of 250 rpm, and then thermally desorbed for 10 min into a splitless GC injection port at 240 \({}^{\circ}\)C.
# A Broadband Active Microwave Monolithically Integrated Circuit Balun in Graphene Technology
## Materials and Methods  
### Graphene Growth and Properties  
Epitaxial graphene has been grown by chemical vapor deposition (CVD) on 500 \(\upmu\)m thick and high resistivity 6H-SiC (0001) substrate. Commercial horizontal CVD hot wall Aixtron VP508 reactor with RF generator for heating was used. Before the growth, in-situ etching of the SiC surface was carried out in hydrogen atmosphere at 1600 \({}^{\circ}\)C with chamber pressure of 100 mbar. The epitaxial carbon films were deposited using propane gas as carbon precursor. The method used is based on high temperature and low argon pressure CVD by creating dynamic conditions of the laminar flow of argon which protect the SiC substrate against Si sublimation and enable mass transport of propane to the SiC surface, thus realizing graphene epitaxy as reported in [17; 18; 19].  
The growth process was followed by in-situ hydrogen intercalation at 1000 \(\circ\)C in 900 mbar Ar atmosphere. The growth parameters were optimized to achieve a bilayer graphene. The initial carrier density and the mobility were extracted from non-contact terahertz spectroscopic measurement and estimated around \(+8.3\times 10^{12}\) cm\({}^{-2}\) and 850 cm\({}^{2}\).V\({}^{-1}\)s\({}^{-1}\), respectively [20].  
Raman spectroscopy was performed using the HORIBA Jobin-Yvon lab system at a laser wavelength of 473 nm, using 1 \(\upmu\)m laser spot size and filters to deliver power less than 0.1 mW and \(\times\)100 objective lens to measure different position of the sample.
## 5 Impacts of the Microwave Absorption: Observation Results
A gain for BeiDou carrier phase signals (L2I, L6I, L7I), for GLONASS (L1C, L1P, L2C), and for GPS (L1C, L2W, L2X) show an improvement between 15% and 60% when using Eccosorb microwave-absorbing material. Figure 7: The number of identified real phase cycle-slips during continuous phase tracking for station KBG1 with Eccosorb (**a**) and for station KBG2 without Eccosorb (**b**) for the period January 2021 until the 5 th of May 2021 using state-of-the-art Trimble Alloy reference receivers.
# Optimization Design of Planar Circle Coil for Limited-Size Wireless Power Transfer System
In order to study the influence of coil parameters on PTE and TP simultaneously, the Thompson sampling efficient multi-objective optimization (TSEMO) algorithm [20] was adopted in this paper to optimize coils design. The TSEMO algorithm is a random meta-heuristic algorithm, which accelerates optimization by constructing a Gaussian process agent model and dramatically reduces the calculation cost. It has been successfully applied to global multi-objective optimization of various problems, such as CFD simulation and chemical process simulation [21; 22]. In this article, the planar circular coil is taken as the design object, and the coils are optimized around magnetic material (ferrite) and conductive material (aluminum) with a restricted primary and secondary side space size and a certain load resistance. The WPT system adopts S-S topology. When load resistance is given, the relationships between PTE, TP, and coupler parameters are analyzed to obtain coil design parameters and objectives. Then, the TSEMO algorithm is used to find the corresponding design scheme under the spatial constraints. It is highlighted that the design method can be implemented automatically due to the calculation based on MATLAB and COMSOL Multiphysics 5.6 with MATLAB. In addition, the medium around the coupler is freshwater, and its conductivity is generally 0.5 \(\times\) 10\({}^{-2}\)-5.0 \(\times\) 10\({}^{-2}\) S/m. This paper is organized as follows. Section 2 takes S-S compensation topology as an example, analyzes the influence of system parameters on PTE and TP when the load resistance is constant, then obtains the variables and goals of coils design. In Section 3, COMSOL and MATLAB are used for joint calculation, and TSEMO arithmetic is used to find the optimal coils design scheme that meets design objectives. In Section 4, the coil design scheme is simulated first to verify the correctness of the design process, and then the results of traditional design [12; 13], \(M\)/\(R\) single objective optimization [18], and TSEMO optimization are compared. Finally, the conclusion of this study is given in Section 5.
## 5 Conclusions  
A comprehensive numerical study of hot-form carbon steel square and circular hollow braces under axial cyclic loading was carried out and presented in this paper. A series of 88 braces with varying width/diameter-to-thickness ratios and slenderness ratios were modeled to analyze cyclic behavior and compressive resistance degradation. The following conclusions can be drawn:  
* The overall cyclic response of both square and circular braces was comparatively affected by diameter/width-to-thickness ratios and slenderness ratios.
* Both square and circular shaped hollow braces, with smaller slenderness and width/diameter-to-thickness ratios, have better hysteretic performance. However, circular-shaped braces generally showed a superior performance than square-shaped braces.  
Figure 14: Comparison of normalized compressive resistances: (**a**) at loading displacement of 2\(\Delta\)y; (**b**) at loading displacement of 4\(\Delta\)y.  
For all width/diameter-to-thickness ratios, the ultimate magnitude of energy dissipation reduced with the increase in the slenderness ratio. Thus, braces with smaller slenderness and width/diameter-to-thickness ratios have enhanced energy dissipation abilities.
* The compressive-tensile resistances significantly degraded with increasing cycles after the occurrence of the global buckling of the braces. The global buckling of slender specimens occurred earlier and compressive-tensile resistances were reduced more promptly compared to stocky members. Finally, the conclusion can be drawn that the width/diameter-to-thickness ratios have a paramount influence on the compressive-tensile resistance degradation of steel braces under axial cyclic loading. By comparison, the slenderness ratio was found to be less significant.  
Conceptualization, H.H.; Data curation, A.S.; Funding acquisition, X.G.; Investigation, A.S.; Methodology, H.H.; Supervision, X.G.; Writing--original draft, H.H.; Writing--review & editing, X.G. All authors have read and agreed to the published version of the manuscript.  
This research received funding from the Beijing Natural Science Foundation Key Project, (8141001) and the National Natural Science Foundation Project of China, (51378038).  
Not applicable.  
Data Availability Statement: Data available within the article.  
The authors declare no conflict of interest.
# Reducing GPS Error for Smart Collars Based on Animal's Behavior
## 3 Experiment and Its Results
### Data Collection  
We collected data for individual goat behavior such as running, walking, and resting (standing, eating, laying). For seven days, we collected data for three conditions every day (as illustrated in Table 3). The experimental goat ran and walked by the tagged line exactly. For the resting condition, the goat kept in the center of the experiment area. All data was collected from the virtual fence application by the database. The data was saved to the database as filtered and unfiltered data. Unlike earlier studies, our total number of filtered and unfiltered data was the same because our GPS error reducing method never erased error locations. Instead of erasing it, it created a new geolocation.  
Figure 4: The experiment ground. (a) Konkuk University playground with 2.6-ha area. (b) Line tag marked zone for the experiment.
## 3 Results
### Topographic Model
#### 3.1.1 Orthophoto as Bridge  
As outlined in the methodology workflow, the starting point regarded the generation of an orthophoto for the entire study area, representing the topographic and the water surfaces. The orthophoto was produced after following the photogrammetric workflow of Agisoft Metashape 2.1.4 starting by computing the relative orientation of the 128 acquired frames covering an area of 0.151 km\({}^{2}\). The coordinates of 18 GCPs expressed in ETRF2000-2008.0 reference frame with UTM zone 32N projection (EPSG: 6707) were inserted, achieving an average error of 3 cm. The outputs included a colored point cloud, a 3D model, and a DSM with 10.4 cm/pix. The DSM was processed to obtain a flat surface water representing the zero level reference (Figure 5).  
Figure 4: Study area location: The red dot indicates the study area, Sestri Levante, located in the east cost of Liguria region (Italy).  
Using the processed DSM, an orthophoto with 2.6 cm/pix (Figure 6) was the final product of the photogrammetric processing.  
The orthophoto generated from all the UAV-captured frames serves multiple purposes: some frames contribute to realize the comprehensive model, while others facilitate the detection of the snorkeler and the estimation of his position. In this context, the unified orthophoto covering the entire study area was segmented into individual orthorectified images, resulting in a corrected and georeferenced image for each UAV frame (Figure 7). The orthorectified images capturing the snorkeler can be used to estimate his position within the same terrestrial reference frame, i.e., ETRF2000-2008.0 with U  
Figure 5: Original DSM with surface water distortion (**a**) and modified DSM with flat water surface equivalent to zero (**b**).  
Figure 6: The 3D model, and orthophoto.