esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Design of Helmholtz Protector to Improve High-Frequency Response of Insert Earphone
## 4 Design of Helmholtz Protector  
The experimental and simulation results show that there are differences between the target SPL and analyzed earphone SPL. There are two peaks due to the front chamber and tubes (ear canal) in the front end. These two peaks can be decreased using an acoustic structure to decrease the SPL difference between the analyzed earphone and target. In this study, a Helmholtz protector is applied as such an acoustic structure.  
The Helmholtz protector consists of two sets of cavities and holes, as shown in Figure 19. The cavity is treated as an acoustical compliance. The small hole is treated as an acoustical resistance and acoustical mass. Therefore, there will be two peaks because there are two sets of cavities and holes. The equivalent circuit of the Helmholtz cavity and hole is parallel to the acoustic sound tube that goes to the IEC-60711 coupler. The cavity and hole set can act as a resonant muffler. The acoustical compliance of cavity is given in Equation (3), and the acoustical mass of the hole is described in Equation (5). The volume of the cavity controls the acoustical compliance, and the length controls the acoustical mass. The protector is manufactured by injection molding. The manufacture tolerance is 0.05 mm. The goal is to cause the resonance of the Helmholtz protector to occur at 7 kHz and 10 kHz. The lengths of the 1 st and 2 nd holes are selected to be 0.65 mm and 1.6 mm, respectively. The volumes of the 1 st and 2 nd Helmholtz cavities are 9 mm\({}^{3}\) and 2.5 mm\({}^{3}\), respectively. With the Helmholtz protector, the earphone equivalent circuit is depicted in Figure 20. Figure 21 shows a comparison of the simulation results for an earphone with a Helmholtz protector and an earphone without a Helmholtz protector. The SPL values at 7 kHz and 10 kHz decrease. Not only are the peak values at 7 kHz and 10 kHz decreased, but there is also a decrease in the entire range from 7 kHz to 10 kHz. Thus, the SPL difference is reduced using the Helmholtz protector.  
Figure 19: Helmholtz protector geometry.  
Figure 18: Experimental and simulated results (without Helmholtz protector).
## 3 Results and Discussion
### Optimization of Network Architecture and Parameters
\begin{table}
\begin{tabular}{l c} \hline \hline
**Training Function** & **Success Rate** \\ \hline trainscg (Scaled conjugate gradient backpropagation) & 72.7\% \\ traincgb (Conjugate gradient backpropagation with Powell-Beale restarts) & 72.6\% \\ traincgf (Conjugate gradient backpropagation with Fletcher–Reeves updates) & 69.5\% \\ traincgp (Conjugate gradient backpropagation with Polak–Ribiere updates) & 70.8\% \\ traingd (Gradient descent backpropagation) & 69.5\% \\ traingda (Gradient descent with adaptive lr backpropagation) & 69.1\% \\ traingdm (Gradient descent with momentum) & 68.5\% \\ traingdx (Gradient descent with momentum and adaptive lr backpropagation) & 69.2\% \\ trainoss (One step secant backpropagation) & 71.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Success rate depending on the training function of the ANN. Figure 8: Success rates for different sizes of the hidden layers in the ANN. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value/Setting** \\ \hline Neurons—input layer & 15,000 (5000) \\ Neurons—hidden layer 1 & 40 (60) \\ Neurons—hidden layer 2 & 20 (60) \\ Neurons—output layer & 2 (2) \\ Activation function—hidden layers & tansig \\ Transfer function—output layer & softmax \\ Error measure & mean squared error (mse) \\ Training function & Scaled Conjugate Gradient Backpropagation \\ Early Stopping & 80 epochs \\ Input signal & normalized FFT \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimized network architecture with the most important training parameters for a sample number of 30,000 (for a sample number of 10,000 in parentheses).
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 5 Molecular Imprinting
### Elements of Molecular Imprinting
#### 5.3.4 Porogens (Solvent)  
Porogenic solvents are used as a dispersive medium and pore-constructive element for the MIP synthesis process, hence they are essential for the polymerization process. The polarity of the solvent used affects the interaction between template molecule and the functional monomer, thus affecting the adsorption properties of the MIPs. In the case of the non-covalent method, usually non-polar and less polar solvents such as acetonitrile, toluene, chloroform, etc. are used to achieve good imprinting efficiency because the solvent used strongly affects the binding and morphology of the polymer. A theoretical study based on density functional theory (DFT) for the selection of the solvent required for producing MIPs has been reported by [PERSON] et al. [114]. In this study, the effects of four different solvents: acetone, chloroform, methanol and acetonitrile on the binding energy of template: monomers ratio have been studied. Chloroform is one of the broadly used solvents because it dissolves many of the monomers and templates without suppressing the hydrogen bonding.
## 2 Materials and Methods
### FT-IR Spectral Fingerprints of Samples  
The analyzed extractions, pomace, and lipids from tomatoes exhibited distinctive spectral features (Figure 2). They displayed an asymmetric stretching of alkanes in the range of 3000-2850 cm\({}^{-1}\), although sample 6 and sample 5 showed a lower quantity of alkanes. Additionally, spectral peaks were observed for the N-O symmetric stretching of nitro compounds (1360-1290 cm\({}^{-1}\)), C=C stretching of alkynes (2260-2100 cm\({}^{-1}\)), and C=C stretching of alkenes (1680-1640 cm\({}^{-1}\)). The absorption of tomatoes was notably higher in the spectral region between 1200 and 900 cm\({}^{-1}\), which is crucial for sugars and acids due to the strong C-O stretching vibration compared to other varieties. Other observed peaks included the C-H curve of alkanes (1470-1350 cm\({}^{-1}\)), C-N stretching of aliphatic amines (1250-1020 cm\({}^{-1}\)), and C-O stretching of esters (1300-1000 cm\({}^{-1}\)). Moreover, detections were made for the C=O stretching of unsaturated \(\alpha\), \(\beta\) aldehydes and ketones (1710-1665 cm\({}^{-1}\)), asymmetric stretching of nitro compounds (1550-1475 cm\({}^{-1}\)), and C-N stretching of aromatic amines (1335-1250 cm\({}^{-1}\)). The FT-IR spectral analysis revealed OH stretching and C=C stretching of lycopene, along with aliphatic CH vibrations (3437-3426 cm\({}^{-1}\), 1635-1637 cm\({}^{-1}\), and 1410-1414 cm\({}^{-1}\), respectively). Furthermore, the band at 1150 cm\({}^{-1}\), typically found in fresh tomatoes and tomato juice, was detected, along with the stretching of polyene CH3 (1033-1037 cm\({}^{-1}\)). Standard lycopene exhibited specific spectral functional bands, such as symmetric and asymmetric CH stretching (2921 and 2854 cm\({}^{-1}\)), cis-vinylene vibration stretching (1630 cm\({}^{-1}\)), CH3 deformation, and trans-RHC-CH-R (970 cm\({}^{-1}\)). Fresh tomatoes, tomato juice, tomato paste, and ketchup extracts showed different spectral function groupings in the regions of 2920-2924 cm\({}^{-1}\) and 1381-1390 cm\({}^{-1}\), attributed to symmetric CH stretching and asymmetric vibration, stretching of cis-vinylene, and CH3 deformation. Ketchup extract was additionally characterized by multiple trans-double bonds at 920 cm\({}^{-1}\)[27]. The FT-IR spectrum revealed prominent bands associated with lipid groups in the range of 1730-1765 cm\({}^{-1}\). Additionally, other bands related to fats were observed at 1477-1400 cm\({}^{-1}\) (C-H bending), 1100-1400 cm\({}^{-1}\) (C-C and stretching C-C-H), and 1170-1115 cm\({}^{-1}\) (stretching C-O). Notably, strong and broad water absorption bands were evident in the 3700-3000 cm\({}^{-1}\) and 1600-1700 cm\({}^{-1}\) regions, with the latter potentially affecting the vibrations of amide I [28]. The frequency range between 1200 and 900 cm\({}^{-1}\) showed intense bands attributed to v(C-O-C) vibrational modes of various carbohydrates and acids, which are abundant groups in tomatoes [29]. Frequencies associated with all types of deformations were typically below 1000 cm\({}^{-1}\), forming a distinct band indicating the presence of specific functional group charges [28].
###### Abstract  
The penetration depth of an evanescent wave in Attenuated Total Reflection (ATR) is dependent on the wavelength of the radiation utilised. At THz frequencies, the penetration depth into biological tissues is in the order of 0.1 to 0.5 mm; rendered pig lard was used as a model sample in this study. A method for the direct measurement of the evanescent wave penetration depth is presented which allows for the estimation of the dispersion of the complex refractive index by using the reflection of the evanescent wave from varying sample depths. The method employs frustrated total internal reflection, and has been demonstrated by using the THz/Far-IR beamline at the Australian synchrotron, and modelled using finite difference time domain (FDTD) simulations.  
Keywords:ATR; THz; synchrotron radiation; lard +
Footnote †: journal: Applied Physics Letters
###### Abstract  
This paper studied two useful vibration signal processing methods for detection and diagnosis of wheel flats. First, the cepstrum analysis method combined with order analysis was applied to the vibration signal to detect periodic responses in the spectrum for a rotating body such as a wheel. In the case of railway vehicles, changes in speed occur while driving. Thus, it is difficult to effectively evaluate the flat signal of the wheel because the time cycle of the flat signal changes frequently. Thus, the order analysis was combined with the existing cepstrum analysis method to consider the changes in train speed. The order analysis changes the domain of the vibration signal from time domain to rotating angular domain to consider the train speed change in the cepstrum analysis. Second, the cross correlation analysis method combined with the order analysis was applied to evaluate the flat signal from the vibration signal well containing the severe field noise produced by the vibrations of the rail irregularities and bogie components. Unlike the cepstrum analysis method, it can find out the wheel flat size because the flat signal linearly increases to the wheel flat. Thus, it is more effective when checking the size of the wheel flat. Finally, the data tested in the Korea Railroad Research Institute were used to confirm that the cepstrum analysis and cross correlation analysis methods are appropriate for not only simulation but also test data.  
fault detection and diagnosis; vibration signal processing technique; wheel flat cepstrum analysis; cross correlation analysis +
Footnote †: journal: Applied Sciences
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 3 Proposed Methodology  
In this section, we shall present in detail the proposed geo-clustering algorithm which extends previous work [35]. In brief, it aims to identify AOIs, based on user-generated metadata. To this goal, a pre-selected input region is first divided into square sub-regions, which will be referred to interchangeably as \"tiles\". The size of tiles is fixed and selected to be relatively small, based on heuristic observations within the initial region. A discussion on this will follow, in Section 4. A graph-based representation is adopted, so as to model the connectivity of neighboring tiles, each described by a set of tags. Tiles are merged and upon an iterative process, a set of larger areas is determined within the initial region. Opposed to the majority of the state-of-the-art methods that have been presented in Section 2.2, our AOI extraction framework relies solely on user-generated metadata and attempts to improve related supervised clustering approaches, by adding the inherent semantics of user-tagged images, shared within a social network. Within the whole process we do not include any kind of knowledge. To facilitate the reader, symbols used throughout this paper and corresponding descriptions have been summarized in Table 1. Moreover, at the following we shall use the terms AOI, region and geo-cluster interchangeably.
## 1 Introduction
It solves the limitations of interpolation by fitting all discrete data points to find a continuous curve. Cubic spline interpolation is a standard method in numerical analysis that can assist in establishing the functional relationship between discrete variables. Compared with Hermite interpolation, the data curve obtained by cubic spline interpolation is smoother and more suitable for fundamental data changes. The generative adversarial net uses a GAN-based generator network to provide an effective regular representation for invisible data distributions. It uses a residual pairwise network as a discriminator to measure the similarity of paired samples. However, the extended samples obtained by these expansion methods can only guarantee the continuity of the small curves at the connection points. However, they cannot guarantee the authenticity of the sample data, which may cause cumulative errors and affect the experimental results. In addition, the complex data distribution is not captured, and the generated features are not interpretable. This cannot meet the prediction accuracy requirements of deep learning models. When expanding data samples, meeting the needs of model training and ensuring the authenticity of data sources as much as possible are still problems to be solved. Therefore, a small-sample learning method based on data enhancement is proposed to solve the problem. This data enhancement algorithm expands the characteristics of the samples to obtain more samples containing the original data's characteristics so that the model can extract features better. This paper explores the prediction solution in small-scale time-series data. The limited data are expanded with a designed augmentation method. Then, the parallel augmentation datasets are predicted with the appropriate models corresponding to the data subsets that were decomposed [13]. The sub-prediction results are integrated with the error covariance intersection. The features of the small-scale time series are expanded and excavated in the proposed method. Moreover, the method is verified with a typical simulation of time series and practical systems. This paper is organized as follows. In Section 2, related work on time series predictions is introduced, including the development of shallow neural networks and deep neural networks. Section 3 introduces the details of the proposed framework, including data augmentation, data decomposition based on time series trends, multi-model prediction,and error estimation fusion with the intersection of covariances. Section 4 tests the proposed and comparative models on simulation data in MG and MSO systems and real data in a fraction ratio in a food safety check system and SO\({}_{2}\) concentration in an atmospheric quality monitoring system. In Section 5, the experimental results are discussed and analyzed, and the experimental results of the proposed model and the comparative model for the dataset are compared and analyzed. Section 6 summarizes the approach, and conclusions and outlooks are provided.
## 2 System Design and Its Components
### Overview of KOMPSAT-3/3a  
The multi-spectral band specification of KOMPSAT-3 launched on 17 May 2012, is as follows: 450-900 \(\upmu\)m for the panchromatic band, 450-520 \(\upmu\)m for blue, 520-600 \(\upmu\)m for green, 630-690 \(\upmu\)m for red, and 760-900 \(\upmu\)m for near-infrared (NIR). The GSD, which stands for ground sample distance at the nadir, is 0.70 m for a panchromatic image. The GSD value for multi-spectral bands and NIR data is 2.8 m and 5.5 m, respectively. KOMPSAT-3A was launched on March 25, 2015, as a successor to this satellite. This has the same multi-spectral band specifications as KOMPSAT-3. The GSD of a panchromatic image is 0.55 m at the nadir. The value for multi-spectral bands and NIR data are 2.2 m and 5.5 m, respectively. The swath width at the nadir for KOMPSAT-3 and KOMPSAT-3A is 15 and 12 km, respectively.  
The average size of the KOMPSAT-3/3A image data is approximately 2.1 GB and 2.5 GB, respectively, based on the Level-1-Geometric corrected (Level-1G) bundled product.  
KOMPSAT-3 and KOMPSAT-3A image data are archived at a rate of 270 TB and 160 TB per year, respectively, including other types of products, including Level-1G Bundled products. It is estimated that the KOMPSAT-3/3A images accumulated by 2024 will be approximately 100 petabytes, based on an estimate, although this is not official data from the distribution agency or sales company.  
In this study, ARD refers to pre-processed and corrected data that can be used for various purposes, as it is not yet in the self-assessment and peer review stage of CEOSCARD4L-ARD, which is the official ARD for KOMPSAT-3/3A images according to CEOS's Product Family Specifications. The KARI is currently developing a dedicated system for KOMPSAT ARD production in accordance with the CARD4L guidelines. The prototype model proposed in this study focuses on creating ARD-formatted data based on open source and providing data cube services that can utilize this ARD separately from KARI's KOMPSAT ARD.
###### Abstract  
In this article, we employed a satellite-enabled Internet of Remote Things (IoRT) network as a promising solution to retrieve data in the most remote areas of interest, where public networks are absent. This article presents a system network based on the satellite-enabled IoRT, a new paradigm that defines a network where each environmental monitoring device can autonomously establish a network with a remote data center. The Xingyun satellite constellation was employed for data retrieval on the Tibetan Plateau (TP). The monitoring system was mainly composed of a ground Internet of Things (IoT) terminal that was built with satellite transceivers, environmental monitoring devices, and system software. We deployed five of these newly developed terminals in harsh areas to monitor environmental variables, and accordingly, air temperature and relative humidity, precipitation, snow depth, land surface temperature, tree stemflow rate, and photosynthetically active radiation were retrieved with the satellite-enabled IoRT network. Field experiments were conducted to evaluate the performance of the proposed system network, and the results indicated that the average time delay with and without the packet creation mode reached 32 and 32.7 s, respectively, and the average packet loss rate with and without the packet creation mode reached 5.63% and 4.48%, respectively. The successful implementation of the satellite-enabled IoRT network for the rapid retrieval of monitoring data in remote glacier, forestland, and canyon areas at very high altitudes on the TP provides an entirely new and revolutionary data retrieval means for backhauling data from remote areas of interest.  
s 29 March 2022  
11 May 2022  
13 May 2022  
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## 5 Discussion of Hierarchical Bayesian Data Analysis for Radiometric Calibration  
The advantages of using hierarchical Bayesian data anlysis for radiometric calibration were laid out before: The approach can jointly answer typical analysis questions in radiometric calibration while fully exploiting the hierarchical nature of external calibration data and fulfilling the requirement on reporting measurement uncertainties and confidence intervals. This section shall add a critical discussion of the proposed method.  
First, the most recognized approach in metrology for deriving measurement uncertainties is the ISO _Guide to the expression of uncertainty in measurements_ (GUM) [7]. The GUM, which was introduced in 1993, fundamentally applies classical (frequentist) statistics, and is not directly compatible with a Bayesian approach (which was proposed in this paper). Nevertheless, the current GUM approach has been repeatedly criticized, and Bayesian methods have been proposed as a consistent and universally applicable replacement [24, 25, 26]. In practice, uncertainties derived by Bayesian statistics are often equal or approximately equal to uncertainties derived by classical statistics [25] so that both approaches lead to desired results. Yet thanks to the availability of numerical tools, Bayesian computations are now often simpler than classical approaches if hierarchical data is analyzed [14]. Therefore, taking a Bayesian approach for deriving the measurement uncertainty of the absolute calibration factor seems justified.  
From the outset, using Markov chain Monte Carlo (MCMC) simulations to infer model parameters appears to be more complicated than employing classical statistics. The added initial work is offset though by a joint probability model, which allows to derive model parameters on arbitrary hierarchical levels without loss of information. Hence the improved analysis justifies the initial extra work.  
If numerical methods like MCMC are used, problems of non-convergence can occur and must be addressed during analysis. Care must be taken in the assessment of simulation results, and plausibility checks should be added.
## 4 Results and Discussion  
### Flowability  
In its fresh state, the mortar's flowability is the most important factor. To determine the flowability of mortar, the flow table test was utilized according to [30]. This involves placing the material in two layers and compacting it without segregation or bleeding. The flow values of various mixes are represented in Figure 2. Incorporating 15, 25, 35, and 50% lime as a partial replacement for cement significantly reduced the flowability of the cement-lime mortar. Also, a significant reduction was observed in mortar flowability when 25% lime was used as a partial replacement for cement and 10% to 100% granite powder was used as a partial replacement for fine aggregate. Figure 2, part 1 demonstrates a decline in the flowability of the cement-lime mortar as the replacement ratios of lime (15%, 25%, 35%, and 50%) for cement increase. Moreover, in Figure 2, part 2 of the mixtures, a consistent reduction in flowability is seen as the granite powder replacement ratios escalate in the presence of 25% lime. The data indicate that greater replacements lead to a significant  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mix**} & **Cement** & **Lime** & **Silica** & **Sand** & **Granite** & **W/C** \\  & **(g)** & **(g)** & **(g)** & **(g)** & **(g)** & **W/C** \\ \hline
1 & 500 & 0 & 0 & 1375 & 0 & 0.45 \\
2 & 425 & 75 & 50 & 1375 & 0 & 0.45 \\
3 & 375 & 125 & 50 & 1375 & 0 & 0.45 \\
4 & 325 & 175 & 50 & 1375 & 0 & 0.45 \\
5 & 250 & 250 & 50 & 1375 & 0 & 0.45 \\
6 & 375 & 125 & 50 & 1237.5 & 137.5 & 0.45 \\
7 & 375 & 125 & 50 & 1100 & 275 & 0.45 \\
8 & 375 & 125 & 50 & 962.5 & 412.5 & 0.45 \\
9 & 375 & 125 & 50 & 825 & 550 & 0.45 \\
10 & 375 & 125 & 50 & 687.5 & 687.5 & 0.45 \\
11 & 375 & 125 & 50 & 550 & 825 & 0.45 \\
12 & 375 & 125 & 50 & 412.5 & 962.5 & 0.45 \\
13 & 375 & 125 & 50 & 275 & 1100 & 0.45 \\
14 & 375 & 125 & 50 & 137.5 & 1237.5 & 0.45 \\
15 & 375 & 125 & 50 & 0 & 1375 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mix proportions of cement–lime mortar.  
decrease in flowability, due to elevated viscosity and cohesion in the mixture. This conduct is wholly consistent with the findings of [31].