esa-sceva/satcom-chunk-collection huggingface dataset:
# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
## 4 Experiment and Discussion
From a quantitative calculation perspective, the energy consumption (total coverage path length) statistics of the two different coverage path methods can be calculated based on the coverage path results, according to the UAV energy model defined in (2). These statistics are summarized in Table 2. Obviously, the energy cost of the classic zigzag coverage method is 230,274 pixels; however, the proposed coverage method based on the DH model poses a total path length of 74,510 pixels, which is much smaller than that of the zigzag method, and  
Figure 10: The simulation result of the classic zigzag coverage path method displayed in 3D. Figure 9: The simulation result of the proposed CPP method based on DH model displayed in 3D. the energy-saving benefit can reach up to 67.64%. Thus, a conclusion can be drawn that the proposed coverage path planning method has significant advantages in UAV energy saving. It is noted that, by adjusting the parameters defined in the DH model, to some extent, the proposed CPP method based on the DH model can be suitable to the coverage path planning of terrains with sparsely distributed mountains and could generate the coverage path with conformal coverage effects, global shortest optimization, and safe obstacle avoidance. However, with regard to the irregular terrain features, such as continuous undulating hills and valleys with sharp elevation changes, the proposed method is not applicable. Additionally, the coverage path generated by the proposed method is represented by vertices in 3D, which can be easily converted into waypoints described by longitude, latitude, and elevation. These coverage waypoints can be further saved as a KML (Keynote markup language) file. As the KML is a universal file format, the planned coverage trajectory can be used for data exchange in almost any ground control system, which is beneficial for improving the compatibility of the proposed method based on the DH model.
# Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
## 3 Results  
### Data Inspection  
The data inspection experiment is setup in the following way. We introduce the rotation bug through randomly rotating some images by \(90^{\circ}\). We then train the two generative models, on correct images and on altered images, and compare their samples. We also train a model with DP to show that its image quality would not be sufficient to detect the error.  
Figure 1 shows the output of generative models trained on MNIST with and without image rotation. By examining the samples, developers can clearly determine that a portion of images was rotated. This way, the error can be promptly identified and fixed. On the other hand, with generative models that uphold the traditional DP guarantee (Figure 1c), it would be difficult to detect such pre-processing error, because the produced samples have very low fidelity, even though \(\varepsilon\) in this case is unjustifiably high at the order of \(10^{7}\).  
We also observe that the synthetic data quality under BDP (see Figures 1a and 2) is sufficient to detect previously unseen classes or dataset biases, such as under-represented classes. Moreover, these results are achieved with a strong privacy guarantee: under \((1,10^{-10})\)-BDP, and hence, the probability that \((1,10^{-5})\)-DP does not hold for this data is less than \(10^{-5}\).  
Figure 1: GAN output for detecting unwanted rotations on MNIST. (**a**) Trained on correct images with BDP; (**b**) Trained on altered images with BDP; (**c**) Trained on altered images with DP.  
Figure 2: Real and synthetic samples on Fashion-MNIST. (**a**) Real; (**b**) Synthetic.
## 2 Materials and Methods
### Physical and Mechanical Property Measurement of Samples  
In this study, geopolymer samples' bulk density (\(\rho_{\text{b}}\)) was the only physical property examined. On the other hand, two mechanical properties (compressive and flexural strength) of samples were investigated. For the flexural strength of samples, the 3-point flexure test arrangement was used. All mechanical testing was performed on cured samples of unreinforced metakaolin-based geopolymer and coir-fiber-reinforced metakaolin-based geopolymer (CFRMG) composites using a STYE 2000 machine (Okhard Machine Tools Ltd., Lagos, Nigeria) with a 3000 KN capacity at a loading rate of 2.4 kN/s. The tests were conducted on three samples for each formulation after the curing age of 28 days in line with ASTM C109 [59] for compressive strength and ASTM C293 [60] for flexural strength.  
Inferential statistics were used to test the set hypothesis in the study after the different property tests. One-way ANOVA was used to determine if there are any significant differences between the mean values of the examined properties of unreinforced metakaolin-based geopolymers and CFRMG composites. A probability value (\(p\)-value) of (\(p\leq 0.05\)) and confidence level of 95% was used for all analysis. If \(p\leq 0.05\), the null hypothesis was rejected (no statistically significant differences between the mean values of the physical or mechanical property under consideration).
## 3 Data and Method  
### Data  
_Dependent variable:_ In Thailand, a crucial dependent variable for assessing economic conditions is the average household income by province, as compiled by the National Statistical Office (NSO).8 This dataset encompasses data from 2004 to 2021, covering Thailand's 77 provinces. The NSO typically gathers this information biennially in odd-numbered years. However, an exception occurred in the early years of the dataset, with data for 2004 and 2006 collected prior to 2007. To ensure continuity and consistency in the dataset, the average household income for 2005 was imputed by averaging the incomes of 2004 and 2006. This methodology resulted in a total of eight distinct time periods, each spanning two years. The NSO defines household income broadly, including earnings from employment or self-produced goods, revenue from property, and any form of assistance received from others. This measure serves as a vital indicator for evaluating the financial health and well-being of Thai households, providing insights into regional economic disparities and trends over time. Of the 77 provinces, Bueng Kan was excluded from the analysis because it was established as a new province in 2011, resulting in incomplete data and calculations based on a different annual basis.  
Footnote 8: [[https://www.stat.uc.edu/](https://www.stat.uc.edu/)]([https://www.stat.uc.edu/](https://www.stat.uc.edu/))\(\sim\)  
_Independent variable:_ Regarding the independent variable, GPP in Thailand serves as a critical economic indicator, mirroring the concept of Gross Domestic Product (GDP) but on a provincial level. The compilation and calculation of GPP is a collaborative effort involving multiple agencies.9 The GPP encompasses various economic parameters, including returns on primary production factors such as land rent, labour compensation, interest, and profits. It accounts for total \"value added\" from all economic activities within each province. The value added, a central element in GPP, represents the net output of a province, calculated as the difference between the production value (Gross Output) and the costs of intermediate goods and services (Intermediate Cost). The NSO adopts the Chain Volume Measures (CVMs) method for calculating GPP. This method, which adjusts for inflation, offers a more accurate depiction of real changes over time through a chain-linked approach.10 The 19 economic sectors included in Thailand's GPP are categorised into 1 agricultural, 4 industrial, and 14 service sectors, as detailed in Appendix A.  
Footnote 8: [[https://www.stat.uc.edu/](https://www.stat.uc.edu/)]([https://www.stat.uc.edu/](https://www.stat.uc.edu/))\(\sim\)
## 4 Materials and Methods
### Volatile Compounds Extraction and GC-MS Analysis  
HS-SPME was used for the extraction of volatile compounds in apples. The pulys of apple fruits were ground into powder in liquid nitrogen. For each extraction, 5 g of powder was placed into a 50 mL screwcap vial containing 1.0 g of NaCl and a magnetic stirring rotor to facilitate the release of volatiles. Prior to sealing of the vials, 10 \(\mu\)L of 0.4 mg/mL 3-nonanone was added as internal standard. After the headspace vial equilibrated at 50 \({}^{\circ}\)C for 10 min on a metal heating agitation platform, the SPME fiber coated with 50/30 \(\mu\)m thickness of divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS, Supelco, Bellefonte, PA, USA) was inserted into the headspace with continuous heating and agitation (200 rpm) for 30 min to adsorb volatile compounds. Then, the fiber was introduced into the heated injector port of the chromatograph for desorption at 250 \({}^{\circ}\)C for 2.5 min.  
The volatile compounds were analyzed with a Thermo Trace GC Ultra gas chromatograph (Thermo Fisher Scientific, Waltham, MA, USA), equipped with a 60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m HP-INNOWax capillary column. Helium was circulated as the carrier gas with a flow of 1.0 mL/min in a splitless mode. The initial oven temperature was kept at 40 \({}^{\circ}\)C for 3 min and increased to 150 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min, then increased at 10 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C and held for 5 min. The temperature of ion source and transfer line were both 240 \({}^{\circ}\)C. Mass spectra were operated in electron ionization mode of 70 eV with the scan range from m/z 35 to 450.  
Identification of volatile compounds was based on mass spectra matching with the database of NIST/EPA/NIH Mass Spectral Library (NIST 2014), the retention times with an in-house developed retention time library based on commercial standards, and the retention indices with those of authentic compounds or literature data, which calculated under the same chromatographic conditions after the injection of a C7-C30 n-alkane series (Supelco, Bellefonte, PA, USA). Quantification was carried out by the internal standard method, as illustrated by [PERSON] et al. [48], where the concentration of each volatile compound was normalized to that of 3-nonanone.
## 2 Numerical Calculation Model
### Flluent Setting  
FLUENT software, a fully implicit separation solver suitable for incompressible and low-velocity flows, was used for numerical simulation and an unsteady shear stress transport k-\(\omega\) turbulence model [26; 27], suitable for flow around bluff bodies, was selected. The SIMPLE algorithm calculates pressure-velocity coupling. The momentum equation, turbulent kinetic energy, and transport dissipation rate equations adopted the second-order upwind discrete scheme, and the residual convergence accuracy was taken as 1.0 \(\times\) 10\({}^{-6}\).  
Considering the complex variation of wind characteristics in a mountainous valley, first, the steady model was used to solve and the flow field was initialized. After the calculation was stable, the transient model was used for solving [28]. According to the requirements of Courant-Friedrichs-Levy (CFL) parameters in FLUENT [29], the transient solution time should ensure that each iteration can be within a grid range and eliminate cross-grid errors, as shown in Equation (1). The CFL is computed by the solver and used to ensure the success of the simulation. The time step was set to 0.8 s, and the total time was 6000 s. After trial calculations, wind speed and air volume converged when the working condition reached 4500 s, so these values at the monitoring point were obtained by averaging the wind speed time series of the last 1000 s.  
\[CFL=\frac{U\Delta t}{\Delta L}<1 \tag{1}\]  
where \(U\) is the inlet wind speed (m/s); \(\Delta t\) is the time step (s); and \(\Delta L\) is the minimum grid length (m).
# I-Shaped Metamaterial Using SRR for Multi-Band Wireless Communication
## 2 MeTM Unit Cell Geometry and Design  
The geometrical configuration of the proposed ISMeTM unit cell and its simulated model are illustrated in Figure 1. A perfect electric conductor (PEC) is used for the metallic part and it is designed on the FR-4 dielectric substrate. The dielectric constant and loss tangent \((tan\delta)\) of the FR-4 substrate material are 4.3 and 0.025, respectively. The thickness of the substrate and the thickness of the annealed copper used for the SRR are 1.6 mm and 0.035 mm, respectively. The primary function of splits in the ring resonators is to ensure that the inductance and capacitance interact with one another to determine the operating frequency. The unit cell's total optimum size is 10 \(\times\) 10 \(\times\) 1.6 mm\({}^{3}\) (sw \(\times\) 1s \(\times\) s\({}_{\text{h}}\)). The parameters of the ISMeTM unit cell are presented in Table 1. The configuration and the comprehensive analysis of the MeTM properties is carried out using the Finite Integration Technique (FIT)-based electromagnetic CST simulator.
## 2 Method
### Mathematical, Index-Based Health Management System
#### 2.1.1 Mahalanobis-Taguchi System (MTS)
The abnormal data set is symbolized as P; \(p_{ij}\) is the _ith_ observation of the \(j\)th feature, where \(i=1,2,\ldots,\ m\), and \(j=1,2,\ldots,\ n\). It is normalized by applying the mean and standard deviation of the healthy data set, while the MDs are estimated using the feature information and the coefficient matrix. MDs which correspond to the ACLR will be out of the MS if the MS is appropriately constructed. In other words, the MDs associated with abnormal conditions will have higher values [36; 37]. Step III: Identification of useful features  
The useful features are selected via the OAs and S/N ratios. In MTS, OAs are used to recognize the significant features by decreasing the number feature combinations which were initially established. The number of features determines the true number of columns in the OA. Two levels of factors are used: Level-1 indicates that the feature is included, while Level-2 means that it is not. To measure the accuracy of the MS predictions, S/N ratios are used. These are calculated using only the ACLR conditions. The equation for determining the S/N ratio (\(\eta_{i}\)) corresponding to the \(i\)th run of the OA is  
\[\eta_{i}=-10\log\left(\frac{1}{t}\sum_{j=1}^{t}\frac{1}{MD_{j}}\right) \tag{8}\]  
where \(i\)th is the number of ACLR conditions and \(MD_{j}\) is the MD of the \(j\)th ACLR condition. By evaluating the gain in the S/N ratios, the useful features are identified. By using Equation (9), the gain of each feature is computed. Features with positive gains are considered useful. \[Gain=\ \overline{S/Nratio_{Level-1}}-\ \overline{S/Nratio_{Level-2}} \tag{9}\]  
Step IV: Future diagnosis  
The MS is rebuilt using the attributes recorded in Step III, and the MDs of the monitored products are calculated. The subjects are healthy if the MDs are inside the MS. If the MDs are outside the MS, then the subjects reveal abnormal (ACLR) behavior. A greater MD indicates a greater deviation between healthy and ACLR patients [13].
## 2 Materials and Methods  
The Ag-coated SiO\({}_{2}\) nanoparticles were fabricated using a modified St\(\ddot{\text{o}}\)ber method [14; 15]. A solution of 26.8 mL of ammonium hydroxide (NH4 OH) in 200 mL of ethanol was stirred for 30 min at 30 \({}^{\circ}\)C in a three-necked round bottomed flask. To grow the monodispersed SiO\({}_{2}\) nanoparticles, 4 mL of tetraethyl orthosilicate (TEOS) was quickly added to the mixture and stirred for 10 h. In order to get amino functionalized SiO\({}_{2}\) nanoparticles, 0.5 mL of amino-propyl-tri-methoxy silane (APTMS) solution was added to the mixture and stirred for another 8 h. Subsequently, the solution was centrifuged for one hour at 5000 rpm and washed twice with ethanol. After washing the nanoparticles were redispersed in 200 mL of ethanol. A basic solution of silver salt containing potassium was prepared separately by adding 0.05 g of potassium carbonate (K\({}_{2}\)CO\({}_{3}\)) to 200 mL of water, stirred for 15 min to dissolve the K\({}_{2}\)CO\({}_{3}\) completely, and 2 mL of 1 wt% AgNO\({}_{3}\) silver-based solution was added.  
In the final step, varying amounts of the amine-functionalized SiO\({}_{2}\) solution (1, 2, and 3 mL) were mixed with 8 mL of the K-silver and K\({}_{2}\)CO\({}_{3}\) solutions to obtain three samples (S1, S2 and S3), each having different Ag shell thicknesses. Solutions were stirred for 10 min. Subsequently, the color of the solutions changed to dark blue when 0.02 mL of formaldehyde was added. The solution was then centrifuged and washed in milli-Q-water for several times to remove unwanted and unreacted chemicals.  
The prepared nanoparticles were then dispersed in ethanol with a suitable concentration to prepare the samples for photo-luminance and transmission electron microscopy. A transmission electron microscope (TEM, Model-JEM2100, 200 kV, Japan Electronics Corporation, Tokyo, Japan) was used to obtain information about the sizes, morphology, and thicknesses of the Ag shells. The TEM samples were prepared by the drop coating method, on a copper grid with a carbon film, and then dried at 150 \({}^{\circ}\)C overnight. Finally, the samples were classified on the basis of the measured thicknesses of their Ag shells.  
The shell thickness of the observed rough surfaces of the nanoparticles was estimated as follows. First, the thickness of the Ag shells was controlled by the stoichiometry that was used during the experiments, i.e., by the weight of the AgNO\({}_{3}\) added to the solution for the shell growth. Second, many nanoparticles were selected throughout the solution and the shell thickness was measured at different locations around each of the chosen ones to obtain an average value. Then, by correlating the outcomes of these two approaches, the reported values of shell thickness were determined.
## 1 Near-Infrared Hyperspectral Imaging (NIR-HSI) for
###### Abstract  
Anthocyanins are an important micro-component that contributes to the quality factors and health benefits of black rice. Anthocyanins concentration and compositions differ among rice seeds depending on the varieties, growth conditions, and maturity level at harvesting. Chemical composition-based seeds inspection on a real-time, non-destructive, and accurate basis is essential to establish industries to optimize the cost and quality of the product. Therefore, this research aimed to evaluate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) to predict the content of anthocyanins in black rice seeds, which will open up the possibility to develop a sorting machine based on rice micro-components. Images of thirty-two samples of black rice seeds, harvested in 2019 and 2020, were captured using the NIR-HSI system with a wavelength of 895-2504 nm. The spectral data extracted from the image were then synchronized with the rice anthocyanins reference value analyzed using high-performance liquid chromatography (HPLC). For comparison, the seed samples were ground into powder, which was also captured using the same NIR-HSI system to obtain the data and was then analyzed using the same method. The model performance of partial least square regression (PLSR) of the seed sample developed based on harvesting time, and mixed data revealed the model consistency with \(R^{2}\) over 0.85 for calibration datasets. The best prediction models for 2019, 2020, and mixed data were obtained by applying standard normal variate (SNV) pre-processing, indicated by the highest coefficient of determination (\(R^{2}\)) of 0.85, 0.95, 0.90, and the lowest standard error of prediction (SEP) of 0.11, 0.17, and 0.16 mg/g, respectively. The obtained \(R^{2}\) and SEP values of the seed model were comparable to the result of powder of 0.92-0.95 and 0.09-0.15 mg/g, respectively. Additionally, the obtained beta coefficients from the developed model were used to generate seed chemical images for predicting anthocyanins in rice seed. The root mean square error (RMSE) value for seed prediction evaluation showed an acceptable result of 0.21 mg/g. This result exhibits the potential of NIR-HSI to be applied in a seed sorting machine based on the anthocyanins content.  
micro-component; black rice; seed quality; near-infrared; hyperspectral imaging 2020 May 2021 25 May 2
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods
### Biomedical Devices
#### 2.1.2 Planar Loop Antennas  
A novel rectangular loop implantable antenna design featuring three concentric loops was introduced, utilizing an I-shaped ground to significantly enhance tuning across operational bands [34]. Polydimethylsiloxane (PDMS) was chosen as the material for both the substrate and superstrate due to its favorable properties for implantable medical devices. The performance and safety of the overall antenna structure were evaluated using realistic human scalp phantom models and a homogeneous skin box to closely mimic the conditions of implantation. The simulation studies of this antenna design indicated that it operates effectively around 5.8 GHz. Concerning safety, the specific absorption rate (SAR), which measures the rate at which energy is absorbed by the human body when exposed to an RF field, was found to be 0.28 W/kg for the skin box and 0.26 W/kg for human scalp phantoms. The safety levels based on Institute of Electrical and Electronics Engineers (IEEE) and Federal Communications Commission (FCC) standards [35] are 1.6 W/kg and 2 W/kg when 1 g and 10 g of head tissue is exposed to EM radiation. It is important to note that these limits are valid for the RF frequency range of 100 kHz-300 GHz. This demonstrates the antenna's suitability for safe use within the specified operational parameters (1 mW input power). The transmitting system is powered by a battery source, which limits the operational period, particularly for cranial implants. Additionally, while selecting a high operating frequency can reduce the size of the system compared to other designs, it also results in a decreased gain of \(-32\) dB. This reduction in gain confines the system's application primarily to shallow implants, where such low gain is less critical.  
To further simplify the RF transceiver circuitry and eliminate the need for Baluns (balance to unbalance converters) required by single-ended antennas, differential loop antennas have been introduced as a solution [18; 36]. These antennas feature an inductively coupled feed structure alongside dual-band meandered radiators, achieving a \(-10\) dB impedance fractional bandwidth of 10.2% with the circular-shaped foldable circuitry frequently used in commercial ingestible capsules. Utilizing a realistic phantom for testing at 2.45 GHz, the antenna demonstrated a gain of \(-27.9\) dB, indicating potential suitability for applications in wireless ingestible capsule technology. However, the frequency of operation was adjusted due to the structural complexity, which may be attributed to the flexibility of the foam. Thisraises concerns about the behavior of the system when inserted inside the human body. Furthermore, such antennas present high reactive impedance due to the inductive coupling, making the matching between the antenna and chip a complex procedure.