esa-sceva/satcom-chunk-collection huggingface dataset:
# Miniaturized Near-Infrared Analyzer for Quantitative Detection of Trace Water in Ethylene Glycol
## 2 Materials and Methods
### Near-Infrared Spectral Acquisition  
The laboratory-built testing setup is depicted in Figure 1 and comprises a halogen light source, a cuvette holder, and a miniature Fourier-transform near-infrared spectrometer with a spectral range of 1100 to 2500 nm and a resolution of 25 cm\({}^{-1}\). Initially, the background spectrum was recorded using an empty quartz cuvette with a 10 mm path length to eliminate interference. Subsequently, the ethylene glycol aqueous solution samples were placed in the cuvette, and their absorption spectra were collected. Each sample was measured six times to ensure data reliability. Laboratory temperature and humidity were maintained at relatively constant levels throughout the experiment.  
This detection scheme simplifies experimental setup, which can be integrated into a compact and lightweight design to facilitate on-site rapid detection, significantly enhancing the convenience and flexibility of experimental operations. Throughout the spectral acquisition process, laboratory temperature and humidity were maintained at relatively constant levels.  
\begin{table}
\begin{tabular}{c c} \hline Model & C15511-01 \\ \hline Optical interferometer & Michelson interferometer \\ Photodetector & InGaAs PIN photodiode \\ Wavelength range & 1100–2500 nm \\ Spectral resolution & 5.7 nm \\ Signal-to-noise ratio (SNR) & \textgreater{}10,000 \\ Spectral acquisition modes & Absorption spectrum/Reflection spectrum \\ Dimensions & 49 mm\(\cdot\)57 mm\(\cdot\)76 mm \\ \hline \end{tabular}
\end{table}
Table 1: Information and data parameters of the C15511-01 instrument.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Number of** & **Minimum** & **Maximum** \\  & **Samples** & **Concentration (\%)** & **Concentration (\%)** \\ \hline Full Dataset & 36 & 0.002 & 1 \\ Training Set & 28 & 0.002 & 1 \\ Validation Set & 8 & 0.005 & 0.85 \\ \hline \end{tabular}
\end{table}
Table 2: Ethylene glycol water content statistics.
# Detection of Sub-Aroma Threshold Concentrations of Wine Methoxyprazines by Multidimensional GCMS
## 2 Materials and Methods  
### Reagents and Standards  
Absolute ethanol [HPLC Grade (ACS ISO UV-VIS)] was purchased from Scharlau (Biostategy, Auckland, New Zealand), sodium hydroxide and sodium chloride, AR grade, from Ajax FineChem (Thermo-Fisher Scientific, Auckland, New Zealand) and L-(+) tartaric acid, 99.5%, and alkane standard solution C\({}_{8}\)-C\({}_{20}\) from Sigma-Aldrich (Merck, Macquarie Park, Australia). Deionized water was obtained each day from a Suez Select Neptune Lab Pure Water Deionization Unit (Total Lab Systems, Auckland, New Zealand).  
Methoxypyrazine standards 2-Isobutyl-3-methoxypyrazine (IBMP, 99%), 2-Isopropyl-3-methoxypyrazine (IPMP, 97%), and 2-sec-Butyl-3-methoxypyrazine (SBMP, 99%) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Deuterated 2-Isopropyl-3-methoxy-d\({}_{3}\)-pyrazine (d\({}_{3}\)-IPMP) was purchased from CDN Isotopes (Hornsby, Australia). Deuterated 2-Isobutyl-3-methoxy-d\({}_{3}\)-pyrazine (d\({}_{3}\)-IBMP) was made in-house at Lincoln University, New Zealand [10].  
Standards of each MP were individually made up in 100% ethanol, diluted to secondary standards in 10% ethanol in deionized water, and then combined into a composite mix also made up in 10% ethanol. Deuterated analogues making up the internal standard composite were made using the same process as the standards. The composite mixes were aliquoted into 4 mL glass vials and stored at \(-20\)\({}^{\circ}\)C.  
Working standards were prepared each day from the composite mix in 5 g/L tartaric acid buffer in 14% ethanol at pH 3.5. Serial dilutions (in the tartaric acid buffer) were performed to produce six standards in duplicate for calibration curve generation.  
In 20 mL amber solid-phase micro-extraction (SPME) vials (Supelco, Merck, Macquarie Park, Australia) each standard dilution was added to 4.85 mL of chilled deionized water in SPME vials, followed by 150 uL of the deuterated internal standard composite, 1 mL of 4 M sodium hydroxide and 4.5 g of sodium chloride. Vials were sealed with a magnetic cap utilizing a PTFE/Silicon gas tight seal (MicroAnalytix, Auckland, New Zealand).  
Model wine consisted of 5.0 g/L tartaric acid in 14% ethanol in water with pH adjusted to 3.5. Wines were made using standardized wine making protocols from the varieties Sauvignon blanc, Merlot (Rose), and Pinot noir.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 11 Routing in Multi-hop Single Immobile Sink for Different Objectives under Distance Uncertainties
### Important Results  
The experimental results in [6] showed the relative loss of optimality on the nominal distance measurements (\(R^{ac}\)) and the relative increase of the deterministic solution in the worst uncertainty case (\(R^{uc}\)) versus the uncertainty level for all three problems mentioned earlier in this section. It was observed that \(R^{uc}\) was greater than \(R^{ac}\) with the difference increasing versus the uncertainty level, indicating that robust solutions are more attractive at the expense of small performance losses. This is illustrated in Figure 17 for different minimum data requirements to be extracted to the sink node (\(p_{1}\) and \(p_{2}\)).  
Furthermore, simulation results showing the obtained objective function values for the robust and deterministic solutions were also provided in [6]. The robust solutions were obtained for different degrees of uncertainty, while the deterministic solutions assumed complete certainty. The results showed the average and standard deviations of the objective functions over 100 random experiments. The deterministic solution gave a higher average objective function value compared to the robust solution while the standard deviation of the uncertainty level of the robust objective value was closer to its mean values as compared to the deterministic objective values. This is clearly illustrated in Figure 18.  
Figure 17: Ratios \(R^{ac}\) and \(R^{uc}\) for the minimum energy consumption problem versus the uncertainty levels [6].
## 3 The Proposed Satellite Images DSM Generation Method
### Stereo Pairs Selection  
In the proposed method, the stereo pair selection is needed for multi-date satellite images, and this process is to ensure that the reconstruction can adopt the multi-view stereo method. The quality of the DSM generated from different image pairs in multi-date satellite images varies widely, as shown in Figure 2. The intersection angles of image pair A and image pair B are 1.3\({}^{\circ}\) and 5.9\({}^{\circ}\), respectively. More acquisition information will be introduced in Section 4.  
There are four main strategies for stereo pair selection, including ground-truth-based strategy, learning-based strategy, SIFT-based strategy, and experience-based strategy. The ground-truth-based strategy calculates the DSM of all possible pairs and lists them according to the quality evaluation results. Although this strategy yields 3D points with high accuracy, the ground-truth-based strategy is impractical, especially when DSM truth is missing (e.g., emergency cartography). The learning-based strategy uses a learning algorithm to train the meta-data of satellite images (such as intersection angle, sun azimuth angle, sun elevation angle, etc.) to obtain an image pair selection model [21]. In order to obtain the DSM with high accuracy, this strategy needs to train a large amount of image parameter data. The SIFT-based strategy sorts image pairs based on the number of matching points. This strategy is simple, but the accuracy of the generated DSM is poor [37]. The experience-based strategy sorts pairs according to their intersection angle and the acquisition date [20], which is very simple but still does not take all the factors into account.
## 3 Results and Discussion
### Delamination-Buckling Simulation Results
#### 3.2.1 Influence of the Fracture Simulation Algorithms  
The qualitative results are illustrated in Figure 7. The final delamination length captured by the COHESIVE model is 7.5% longer than that of the MIXED model, and the deflection of the delaminated skin is 10.5% greater, respectively. However, the deformed shapes are quite similar. The comparison of the axial-load shortening curves is presented in Figure 8. As seen, the two models predicted a similar response up to the stage when delamination starts propagating (i.e., when the elements are damaged but not yet failed); this stage corresponds to an axial shortening of approximately 0.12 mm. After that stage, the MIXED model depicted a stiffer response in comparison to the COHESIVE model. The areas under the axial-load shortening curves, evaluated from the stage at which delamination starts propagating, which represent the impact resisting energies, are also compared. The comparison indicates that the specimen analyzed by the MIXED model could sustain 20%more energy than the one modeled by the COHESIVE model. Interestingly, this behavior is opposite to what was reported  
Figure 6: Close-up views of the crack propagation in the DCB, in which a magnesium skin is bonded to an epoxy/fiberglass composite, using a 0.2 mm thick layer of epoxy resin. Specimen (**a**) with the white coating used for monitoring crack propagation and (**b**) without the coating.  
by [PERSON] and [PERSON] [24], who showed that CZM has a tendency to overestimate fracture energy. The sustaining energies become closer to one another towards the end of the computation time. This is attributed to the presence of the residual stress captured by the xFEM elements. In other words, after xFEM elements fail, the stress inside the elements does not become null, which is in contrast to the behaviour exhibited by the cohesive elements. However, since the delamination continues propagating through the cohesive elements and the number of failed xFEM elements remains constant, the effect of the residual stress is reduced.  
Figure 8: Axial-load shortening curve produced by COHESIVE and MIXED models.  
Figure 7: Delamination propagation and resulting deformed shapes captured by the (**a**) COHESIVE model, (**b**) MIXED model with initial delamination at the mid-height, and (**c**) MIXED model with initial delamination at the lower interface.
## 3 Materials and Methods
### Feature Extraction  
The Principal Component Analysis (PCA) is a feature extraction technique; it is the method of determining the principal components and using them to transform the basis of the data, often simply using the foremost and discarding the remaining. The principal components are a set of p unit vectors, with the \(i\)-th vector indicating the direction of the best-fitting line. In contrast, the remaining components are perpendicular to the first \(i-1\) vectors. The first principal component can be described as a direction that maximizes the anticipated data's variance. The \(i\)-th principal component maximizes the variance of the projected data in a direction orthogonal to the first \(i-1\) principal components.  
The principal components can be proved to be eigenvectors of the data's covariance matrix. As a result, the Eigen decomposition of the data covariance matrix is frequently used to determine the principal components [49]. PCA is described as an orthogonal linear transformation that transforms data to a new coordinate system such that the data's maximum variance by certain scalar projection falls on the first coordinate termed as the first principal component, the second largest variance on the second coordinate, and so on. Hence, to obtain the principal components of the combined dataset, the maximum number of features to include in the transformed feature names is specified as 5, and the proportion of variance to retain the principal components is stated as 0.95. The principal components of the combined dataset are shown in Table 4.  
Figure 4: The target class distribution of the combined dataset.  
From Table 4, a total of 18 transformed features, referred to as principal components, with a maximum of 5 feature names, were obtained from the 13 predictors of the combined heart dataset.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 5 Electrospinning of Chitosan  
The D-glucosamine repeat unit in chitosan polymers is based of two monomers: N-acetyl-D-glucosamine and N-amino-D-glucosamine [92]. To achieve the desired properties, chemical reactions often target functional amino groups. The N-amino-D-glucosamine/ N-acetyl-D-glucosamine ratio, called the degree of deacetylation (DDA), is a key indication that distinguishes chitosan from chitin, resulting in chitosan's unique characteristics. However, during the deacetylation process, acetyl groups are eliminated, influencing the molecular weight (Mw) that must be considered when electrospinning a chitosan-based solution [93]. Several parameters, such as the voltage, flow rate, distance between electrodes, ambient temperature and humidity, electrospinninging solution properties, pH, viscosity, molecular weight, and mixing ratios of polymers and solvents play crucial roles in the final nanofibers [94; 95; 96; 97; 98].
## 3 Results and Discussion
### Non-Equilibrium MD Simulations
#### 3.2.2 Effects of Thermal Motion on Solid/Fluid Boundary  
To gain a better understanding of solid/fluid boundary estimated by the previous section, the temperature effects of solid/fluid boundary is studied. The solid/fluid interface involves thermal motions, the structure of solid, potential energy between solid and fluid atoms. In this study, the quantum mechanical temperature effect on the potential energy surface does be not considered, which may have an impact on solid/fluid boundary. An identical LJ potential is used in all temperature range tested.  
The solid/fluid boundary location is calculated with LJ potential, the lattice constant of FCC (Face Centered Cubic) crystal by ignoring thermal motion. It is known that monoatomic fluid molecules adjacent to the FCC (001) surface forms another layer of the FCC-like structure to minimize the energy [46]. If the thermal energy is very small, the fluid atoms may form a perfect FCC structure near the surface as shown in Figure 7a. The solid/fluid boundary is represented as the red line that has a three-dimensional corrugation pattern (See Figure S2). For simplicity, the solid/fluid boundary is assumed as a plane in this work. Then, the averaged location of solid/fluid boundary can be described as follows:  
\[\delta_{\text{b,T-0K}}\equiv\frac{1}{2}\sqrt{r_{\text{m}}^{2}-\frac{a^{2}}{4}} \tag{6}\]  
where \(a\) is lattice constant and \(r_{\text{m}}\) is the location of a minimum 12-6 LJ potential between solid atom and fluid atom (i.e., \(r_{\text{m}}=2^{1/6}\sigma_{\text{sf}}\)).  
Figure 7: (**a**) A geometrical representation of solid/fluid boundary (red curve) near the absolute zero-temperature. Where \(r_{\text{m}}\) is the location of the minimum LJ potential between solid and fluid (i.e., \(r_{\text{m}}=2^{\frac{1}{6}}\sigma_{\text{sf}}\)) and \(a\) is the lattice constant. (**b**) Determined Solid/fluid boundary with respect to the temperature for the Ag/He interface. The constant value of the linear fitting chosen as \(3.6\times 10^{-4}\) for best fitting of first 4 data.  
As the temperature increases the solid/fluid boundary shifts toward the fluid regime. A similar shifting is observed during the study of the temperature dependence of ionic structure [47]. It is noted that the minimum temperature tested via MD simulation is 35 K because below this temperature, He atoms are solidified at the Ag interface and thus the method used in this paper is no longer works well. At below 100 K, the solid/fluid boundary shows a linear behavior with respect to the temperature and the linear extrapolation of those data show a good agreement with Equation (6) at \(T\)\(\sim\)0 K. At a temperature larger than 100 K, the behavior is non-linear, and it seems saturated after a certain temperature. Additional research is needed to elucidate the cause of this saturation.
###### Abstract  
Solar-induced chlorophyll fluorescence, or SIF, is a part of the natural process of photosynthesis. SIF can be measured from space by instruments such as the Orbiting Carbon Observatory-2 (OCO-2), making it a useful proxy for monitoring gross primary production (GPP), which is a critical component of Earth's carbon cycle. The complex physical relationship between SIF and GPP is frequently studied using OCO-2 observations of SIF since they offer the finest spatial resolution available. However, measurement error (noise) and large gaps in spatial coverage limit the use of OCO-2 SIF to highly aggregated scales. To study the relationship between SIF and GPP across varying spatial scales, de-noised and gap-filled (i.e., Level 3) SIF data products are needed. Using a geostatistical methodology called cokriging, which includes kriging as a special case, we develop coSIF: a Level 3 SIF data product at a 0.05-degree resolution. As a natural secondary variable for cokriging, OCO-2 observes column-averaged atmospheric carbon dioxide concentrations (XCO\({}_{2}\)) simultaneously with SIF. There is a suggested lagged spatio-temporal dependence between SIF and XCO\({}_{2}\), which we characterize through spatial covariance and cross-covariance functions. Our approach is highly parallelizable and accounts for non-stationary measurement errors in the observations. Importantly, each datum in the resulting coSIF data product is accompanied by a measure of uncertainty. Extant approaches do not provide formal uncertainty quantification, nor do they leverage the cross-dependence with XCO\({}_{2}\).  
multivariate spatial prediction; uncertainty quantification; cokriging; full bivariate Matern model; photosynthesis; solar-induced chlorophyll fluorescence; column-average carbon dioxide; Orbiting Carbon Observatory-2 2023  
15 August 2023
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
###### Abstract  
With the development of localization technologies, researchers solve the indoor localization problems using diverse methods and equipment. Most localization techniques require either specialized devices or fingerprints, which are inconvenient for daily use. Therefore, we propose and implement an accurate, efficient and lightweight system for indoor direction finding using common smartphones and loudspeakers. Our method is derived from a key insight: By moving a smartphone in regular patterns, we can effectively emulate the sensitivity and functionality of a Uniform Antenna Array to estimate the angle of arrival of the target signal. Specifically, a user only needs to hold his smartphone still in front of him, and then rotate his body around 360\({}^{\circ}\) duration with the smartphone at an approximate constant velocity. Then, our system can provide accurate directional guidance and lead the user to their destinations (normal loudspeakers we preset in the indoor environment transmitting high frequency acoustic signals) after a few measurements. Major challenges in implementing our system are not only imitating a virtual antenna array by ordinary smartphones but also overcoming the detection difficulties caused by the complex indoor environment. In addition, we leverage the gyroscope of the smartphone to reduce the impact of a user's motion pattern change to the accuracy of our system. In order to get rid of the multipath effect, we leverage multiple signal classification to calculate the direction of the target signal, and then design and deploy our system in various indoor scenes. Extensive comparative experiments show that our system is reliable under various circumstances.  
Keywords: indoor direction finding; antenna array simulation; multiple signal classification; multipath effect +
Footnote †: journal: Sensors
# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 4 Experiments
### Implementation and Performance Evaluation of Samples Balance Strategies  
For the observation of sample balancing, we consider balancing the positive and negative, and easy and hard samples using SBS, which is short for Sample Balance Strategies. In this paper, the SBS method aims to balance the distribution of positive and negative samples and suppress the excessive negative samples through easy and hard samples. In order to reduce the computation of IoU, we propose that the Offset-Head replace the original FCOS-head which is based on the anchor-free detector FCOS. The Offset-Head directly regresses the offset (\(\triangle x\), \(\triangle y\)) between the anchor point and the center point of the ground-truth object. In order to verify SBS's effectiveness, we first use the same datasets, such as MSCOCO minival set, to compare the ATSS with our own algorithm, as shown below in Table 1.  
Our method with SBS can achieves \(AP\) 39.44%, and improves detection by 0.23% on \(AP\), 0.03% on \(AP_{50}\), 1.79% on \(AP_{s}\), 0.03% on \(AP_{m}\). To be specific, SBS can significantly improve the AP performance of small objects significantly. Because of the balance between positive and negative, many negative samples are suppressed. However, 0.03% are declined on \(AP_{L}\). As shown in Figure 9, excluding its for small object detection capabilities, the samples balance method is not much different.  
We apply our method with SBS only on the Visdrone2020 dataset and the result can be found as in the below Figure 9. The candidates' boxes with higher accuracy and a lower amount of false-positives can be better regressed by our method than by FPN + RPN and FCOS w/ATSS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & \(AP\)**[\%]** & \(AP_{50}\)**[\%]** & \(AP_{75}\)**[\%]** & \(AP_{S}\)**[\%]** & \(AP_{M}\)**[\%]** & \(AP_{L}\)**[\%]** \\ \hline FCOS + ATSS & 39.21 & 57.38 & **41.36** & 22.34 & 42.54 & 49.80 \\ Ours w/SBS & **39.44** & **57.41** & 41.11 & **24.13** & **42.55** & **49.77** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Verification of the proposed Sample Balance Strategies (SBS) on MSCOCO minival set by comparing with the FCOS + ATSS method.  
By comparing the experimental verification and analysis, we can draw the following conclusions for our SBS method:  
* Small object detection can achieve a higher performance with sample balance strategies for positive and negative samples, and easy and hard samples.
* In this task, the AP performance of our method is higher than others' performance, such as those from FCOS w/ATSS.
## 3 Classical Methods for Generating OAM Waves in RF Domain
### Flat Drilled Phase Plate  
SPP can also be designed by drilling holes in a constant height dielectric bulk material in order to engineer the required spiral phase profile through effective permittivity tailoring. This kind of SPP can be referred as flat drilled phase plate. Such work has been realized at 32 GHz to generate OAM waves with topological charge \(l=-1\)[30; 31]. A horn antenna, commonly used in the microwave range for its high directivity and low sidelobes level, was used as the incident wave source to illuminate the phase plate, compared to a Gaussian beam generally used in optics.  
Figure 2: Schematic design of a spiral phase plate and results obtained at 60 GHz in [29].