esa-sceva/satcom-chunk-collection huggingface dataset:
# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
###### Abstract
The Calabria region (South Italy) allows honey varieties with different properties to be obtained [30; 31]. To date, limited pieces of information that correlate amino acid content and aromatic compounds are available, while both factors are bound to both genetic and environmental characteristics. To our knowledge, this is the first time that a similar investigation has been carried out on the honeys. Previous research and bridges between recent trends and the past: The exploration of honey's intricate composition and sensory attributes builds upon a foundation of previous research endeavors that have sought to unravel the secrets of this cherished natural product. Historical uses of honey for its sweetness and therapeutic benefits have been documented, emphasizing its significance across time. However, in recent years, scientific advancements and innovative methodologies have allowed us to delve deeper into the complex realm of honey's characteristics. Past studies have laid the groundwork for our understanding of the multifaceted relationship between honey's amino acid profiles and its aromatic compounds. These investigations have paved the way for the current study's innovative approach. By combining hydrophilic interaction chromatography (HILIC) and mass spectrometry, as well as headspace-solid phase micro-extraction (HS-SPME) coupled with gas chromatography mass spectrometry (GC-MS), this study takes a significant step forward in unraveling the intricate connections between amino acids and aroma compounds in different honey types. As recent trends in the scientific community lean towards a holistic and comprehensive understanding of food products, this study aligns itself with this trajectory. The integration of advanced chemometric techniques offers a contemporary perspective on the interplay between honey's composition and sensory attributes, establishing a bridge between traditional wisdom and modern analytical capabilities.
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 5 Joint Channel Estimation Algorithm Based on DFT and DWT
### Computational Complexity Analysis  
We use the number of complex multiplications as a measure of the computational complexity of the algorithm. The LS channel estimation algorithm requires \(N_{\text{P}}\) complex multiplications. The \(N\)-point FFT and IFFT require \(\frac{N}{2}\text{log}_{2}N\) complex multiplications, respectively [12]. The T-DFT and ST-DFT algorithms need to calculate the power on each CIR, which requires \(N\) complex multiplications. The calculation of the noise variance estimate requires \(N_{\text{P}}\) complex multiplications. The computational complexity of both fast wavelet decomposition and reconstruction is \(N_{\text{P}}\)[16]. Then the computational complexity of different algorithms is shown in Table 1.  
The LS algorithm has the lowest computational complexity, followed by the DWT algorithm. The computational complexity of the different DFT-based algorithms is influenced by the computational complexity of the threshold. It can be observed that T-DFT and ST-DFT have the same complexity. Since the joint channel estimation algorithm is the combination of two algorithms, it has the highest complexity among the compared algorithms, which is increased by about 48% compared with that of the T-DFT algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Computational Complexity** & **Value** \\ \hline LS & \(N_{\text{P}}\) & 242 \\ \hline DFT [11] & \(N_{\text{P}}+N\text{log}_{2}N\) & 2290 \\ \hline T-DFT [13] & \(N_{\text{P}}+N\text{log}_{2}N+N\) & 2546 \\ \hline ST-DFT & \(N_{\text{P}}+N\text{log}_{2}N+N\) & 2546 \\ \hline DWT & \(4N_{\text{P}}\) & 968 \\ \hline Joint Algorithm & \(6N_{\text{P}}+N\text{log}_{2}N+N\) & 3756 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of computational complexity of different algorithms.
## 3 Numerical Simulation of UDRC Spallation
### Finite Element Model Setup  
An axisymmetric finite element model was developed to simulate the one-dimensional stress spallation experiments. The model focused on the specimen itself, which had a length of 1000 mm and a diameter of 74 mm. For UDRC specimens, a 6 mm diameter steel reinforcement bar (either plain or deformed, corresponding to the experimental series) was modeled centrally. Four-node axisymmetric elements with a characteristic size of 1 mm were used; this mesh density was determined to be adequate based on convergence studies (details omitted for brevity but ensured stable and reasonably accurate results for the phenomena of interest). Perfect bonding between the reinforcement and concrete was assumed and implemented via shared nodes at their interface.  
Figure 2: Typical free surface velocity history illustrating the peak velocity (\(v_{peak}\)), rebound velocity (\(v_{relound}\)), and the calculation of the pull-back velocity (\(\Delta v_{pb}\)).  
The concrete material was represented using the Karagozian and Case Concrete (KCC) model [22], which is well suited for capturing essential dynamic characteristics of concrete, including strain rate dependency and damage evolution. The specific parameters for the KCC model adopted in these simulations are detailed in Table 1. The steel reinforcement was modeled using an elastic material model (\"MAT_ELASTIC* in LS-DYNA terminology), with its material properties listed in Table 2. This elastic assumption for steel is justified as stresses in the rebar were anticipated to remain below its yield strength during the primary concrete spallation event.  
Loading was applied as a velocity boundary condition at the input end (left end) of the specimen model. This velocity history, shown in Figure 3, was derived from experimental strain gauge data recorded by the first strain gauge (strain gauge #1) on the specimen near the bar-specimen interface, and converted to particle velocity using Equation (5) [14]. The other end of the specimen was modeled as a free surface to allow for wave reflection. Standard symmetry boundary conditions were applied along the central axis of the axisymmetric model.  
\[v(t)=\sqrt{E/\rho}\,\epsilon_{gauge}(t) \tag{5}\]
# Improved Simplified Engineering Fault Displacement Hazard Evaluation Method for On-Fault Sites
## 2 Development of the Improved, Simplified Hazard Evaluation Method
### The Improved Hazard Evaluation Procedure
#### 2.4.3 Screening of the Probability That the Ruptures Intersect the Site  
For the magnitudes \(m_{j}\) in the magnitude interval \([m_{min},\,m_{u}]\), calculate the rupture length, \(L_{j}\), using the empirical scaling relation for the rupture length \(L_{j}=L\left(m_{j}\right)\), where \(m_{min}\) is a reasonable established minimum magnitude where displacement can be expected; in our case, \(m_{min}=4.5\). The empirical correlation in [39] is preferred in the case studied, since it reproduces the mapped fault length of 27.9 km for the ultimate magnitude \(m_{u}=6.7\).  
Select those ruptures, for which the geometrical relations \(x_{i}<x_{s}\), \(x_{i}+L>x_{s}\) and \(x_{i}+L\leq FL\) hold; see Tables 1 and 2 as examples. Note that \(x_{s}\) and \(FL\) are fixed values, \(L_{j}\) is fixed corresponding to \(m_{j}\), and the \(x_{i}\) is a random variable within \([0,FL]\).  
Figure 5: The conditional probability for magnitude exceedance (1-P(m)) and the probability of the non-zero surface rupture versus the magnitude.  
2.4.4. Calculation of the Displacements at the Site Location \(\mathrm{x_{s}}\)  
At all \(m_{j}\), for each screened-in pair of \(x_{i}\) and \(L_{j}\), calculate the displacements at the site location \(x_{s}\) by Equations (5) and (6) using \(m_{j}\) and \(x^{*}(x_{s}-x_{i})/L_{j}\).  
2.4.5. Calculation of the Expected Displacements at the Site Location \(\mathrm{x_{s}}\)  
For all \(m_{j}\), calculate the expected displacements at the site location \(x_{s}\), assuming \(x_{i}\) is uniformly distributed over, i.e., \(P(x)=x_{i}/FL\) in \([0,x_{i})\). The expected displacement value is the arithmetic mean of displacements at different screened-in \(x_{i}\)-s.  
2.4.6. Calculation of the Annual Exceedance Rate (AER) \(\mathrm{\
u(D\geq D_{0})}\)  
The calculation is based on Equations (7) and (8), assuming the probability \(P(x_{i}\in[0,x_{s})=x_{s}/FL\).
## 2 Materials and Methods
### Chemical Compositions Measurement
#### 2.3.4 Method Validation and Quantitative Analysis  
The linearity was examined using the standard solutions of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid. The linearity of the calibration curves was determined byplotting the peak area (Y) which was measured according to the above chromatographic conditions versus concentration (X). The following linear regression equations for the calibration curves were obtained: Y = 1563.03X \(-\) 5238.72 (R\({}^{2}\) = 0.9998, chlorogenic acid), Y = 2956.06X \(-\) 7074.40 (R\({}^{2}\) = 0.9997, luteolin-7-O-glucoside), Y = 2276.86X \(-\) 14,801.07 (R\({}^{2}\) = 0.9999, 3,5-O-dicaffeoylquinic acid). The linear ranges of chlorogenic acid, luteolin-7-O-glucose, and 3,5-O-dicaffeoylquinic acid were 35.04-525.60 ng, 24.99-374.85 ng, and 80.23-1203.45 ng, respectively. The accuracy was determined for the different compounds, where chlorogenic acid had an accuracy of 98.2% \(\pm\) 1.24% (\(n\) = 6), luteolin-7-O-glucose 96.9% \(\pm\) 2.23% (\(n\) = 6), and 3,5-O-dicaffeoylquinic acid 97.7% \(\pm\) 1.47% (\(n\) = 6). The injection volume was 5 \(\mu\)L of the standard solution and sample solution. The peak area was measured according to the above chromatographic conditions and the content was calculated by the linear equations.
## 4 Simulation Results and Discussion  
### Prediction Results  
Two different data, including real and predicted data, have been shown in Figure 5 to describe an ordinary summer day during this research. Figure 6 and Table 1 show an error histogram and linear regression results for predicted weather parameters. As can be seen from the results, it was evident that predicted values for temperature and solar irradiance were predicted accurately enough. However, wind speed was not predicted precisely in comparison to the two other parameters due to its high hourly variations. It is worth noting that in this paper, the application of MLP in time series prediction was investigated. Some theoretical details of the MLP are available in [43]. Prediction values were used for calculating PV and WT output power according to the mentioned equations. In the following, a case study was defined in a further subsection (Section 4.3.3) in order to analyze the level of accuracy of these predictions.  
Figure 4: One-year data gathered for: (**a**) solar irradiance; (**b**) temperature; and (**c**) wind speed.  
Figure 5: Real and predicted data of (**a**) solar irradiance; (**b**) temperature; and (**c**) wind speed.  
Figure 6: Error histograms for predicted (**a**) solar irradiance; (**b**) temperature; and (**c**) wind speed.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 3 Experimental Result
### Performance Comparison with State-of-the-Art Methods  
A comparative experiment was conducted with the proposed FHRR-Net and two state-of-art methods: MSCNN [14] and DerainNet [10]. We first compare the state-of-the-art methods on synthetic datasets, evaluating qualitatively and quantitatively. Figure 8b is a part of the synthesized test data which includes both rain and haze components. We also compare the state-of-the-art methods on real-world road images to show that the network is robust.  
In Figure 8, DerainNet only removes the rain streak part of the synthesized image. Specifically, Figure 8c shows that the fog is not removed. On the other hand, MSCNN only removes the hazy part of the synthesized image. In Figure 8d, the rain streak components are not removed with MSCNN. Quantitative evaluations of the results on Figure 8 are shown in Table 2. FHRR-Net obtains better PSNR and SSIM indicators than other state-of-the-art methods. Based on the above experimental results, FHRR-Net shows the best results for the synthesized data.  
Visual comparison results from real-world road data are shown in Figure 9. Some images on Figure 9c show that MSCNN is unable to remove rain from the actual road image with haze and rain together. In addition, there is excessive enhancement in the process of removing the haze.  
Figure 8: Visual comparison with the state-of-art methods on the synthesized dataset. (**a**) Ground truth, (**b**) Inputs, (c) DerainNet [10] (**d**) MSCNN [14], (**e**) FHRR-Net.  
Figure 7: Example of created synthetic dataset. (**a**) Cityscape foggy dataset [32; 33; 34]. (**b**) Our new rain and haze road dataset. To make the dataset more general, we created a new rain and haze road dataset by combining various directions and thicknesses of the rain streak.
## 2 KITSUNE Satellite  
### Overview  
The KITSUNE CubeSat is designed using a 6U platform consisting of a 3U imaging payload, 2U main bus system, and 1U SPATIUM-2 (space precision atomic-clock timing utility mission). Four main objectives are introduced for the 2U main bus and 3U camera payload: (1) Earth observation with a 5-m-class resolution of visible images, (2) development of a 2U Kyutech standard bus system, (3) downlink of a low-resolution image from a secondary camera by C-band uplink, and (4) demonstration of C-band communication with the main and mobile ground station (GS). Each mission design is well-distributed among the collaborators. Harada Seiki engineers and the Kyutech camera team have focused on the imaging mission, while missions 3 and 4 are conducted by the Addnics engineer and Kyutech communication team.  
The 2U main bus system is solely designed by the Kyutech team, which is adopted from the BIRDS project configuration [34]. Onboard computer (OBC), electrical power system (EPS), attitude determination and control system (ADCS), and communication boards (COM) are well-integrated by stacking horizontally on a backplane board (BPB) (Figure 1). This design reduces the number of harnesses where a point of failure might happen. The OBC has a redundant pair of PIC microcontrollers that act as the central command and data handling as well as communication for transmitting the beacon of the satellite. On the other hand, the EPS has a vital role in supplying sufficient power to the satellite. The primary EPS of KITSUNE has 34 solar cells attached to every solar panel board of each axis, except the -Z axis. Meanwhile, the secondary EPS is the rechargeable batteries that can provide a maximum of 14 W. An MAI-401 active control module is used for the ADCS. It is a commercial off-the-shelf (COTS) product that consists of reaction wheels and sun sensors, where the GPS and magnetometer are integrated into the other boards. The GPS is placed on the solar panel, and the magnetometer is connected 10 cm away from the ADCS module in order to avoid the electromagnetic noise generated by the reaction wheels. Two communication subsystems are installed in the KITSUNE satellite for uplink and downlink purposes. First, the C-band radio is mainly used for the camera payload to download the images to the Kyutech GS and the ADCS data during the image capturing. Secondly, an ultra-high-frequency (UHF) transceiver receives the uplink commands from the GS, downlinks the thumbnail images, and transmits the telemetry and continuous wave (CW) beacon. Nevertheless, the focus of this paper is only on the main mission, which is the camera payload and related main bus systems.
## 4 Experiment Results and Comparison  
In this section, we conduct an experimental study to demonstrate the effectiveness of the proposed algorithm on well-known benchmark traffic flow data sets. As an illustration, we compare the proposed SAL approach against two baseline algorithms, including the probabilistic graphical model (PGM) [7] and various widths clustering (kNNVWC) [28]. Section 4.1 describes the network traffic data sets and Section 4.3 presents the evaluation metrics. Third, the experimental settings for a proposed SAL approach and the baseline algorithms and its procedure are specified in Section 4.4. Finally, we present and explain the results and findings of our experiment and compare it to the baseline algorithms in Section 4.5.
# Characterization of Dynamic Blockage Probability in Industrial Millimeter Wave 5G Deployments
## 5 Blockage in Industrial Deployments  
In this section, we determine the LoS blockage probability in industrial deployments. We first outline the proposed approach and then proceed to address the case of BS-to-UE and UE-to-UE communications separately.
## 1 Introduction
The contributions of this work are summarized as follows:  
* We propose the crop yield prediction method by using time series multispatial images [8; 9; 10] and a 3D convolutional neural network [11; 12], which has been proven to be effective. * We believe that regions with similar crop yields should have similar features learned by the network. Thus, the triplet constraint is used for the penultimate feature learned by the 3D convolutional neural network. As a result, the discriminative feature learning in the crop yield prediction task can be facilitated by the above constraints. * We propose a multitask learning method to simultaneously learn regression and deep feature embedding for crop yield prediction.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 5 Diamond-Based Electric Biosensors
### Biosensors for Influenza
#### 5.3.2 Detecting Whole Influenza Viral Particles via Hemagglutinin (HA) Recognition  
A biosensor for whole viral particles of influenza was built from a BDD thin film functionalised by the electrochemical deposition of a diazonium salt with the subsequent attachment of a sialic acid mimic dimeric peptide [57]. This peptide was designed for the recognition of hemagglutinin (HA), one of the surface antigens of the influenza virus. The first sequence of tests demonstrated that the biosensor does recognize pure HA in a PBS solution when operating in the mode of electric impedance spectroscopy. The biosensor was tested with whole viral particles, having proven successful in responding to both H1N1 and H3N2 strains in the concentration range of 400-8000 pfu/mL (where pfu is a plaque-forming unit).
## 2 Materials and Methods
### Field-Measured CIs
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**ID** & **Sites** & **Lat.** & **Lon.** & **IGBP** & \(\Omega_{\rm e}\) & \(\gamma_{\rm e}\) & \(CI_{\rm Field}\) & \multicolumn{2}{c}{\(CI_{\rm MOD}\)} & \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Source**} \\ \cline{1-1} \cline{6-11}  & SRF & 49.250 & \(-\)82.050 & 1 & 0.88 & 1.71 & 0.51 & 0.52 & 0.49 & June 2001 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1} \cline{6-11}  & Krasnoyarsk257.233 & 91.583 & 1 & 0.85 & 1.53 & 0.56 & 0.60 & 0.58 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1} \cline{6-11}  & Okarito & \(-\)43.200 & 170.300 & 5 & 0.87 & 1.4 & 0.62 & 0.71 & 0.66 & January 2003 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1} \cline{6-11}  & SETRES & 34.902 & \(-\)79.486 & 1 & 0.9 & 1.21 & 0.74 & 0.80 & 0.79 & August 2003 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1} \cline{6-11}  & Hertford & 36.383 & \(-\)77.001 & 1 & 0.94 & 1.21 & 0.78 & 0.77 & 0.68 & August 2003 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1} \cline{6-11}  & SOJP & 53.916 & \(-\)104.692 & 1 & 0.85 & 1.42 & 0.6 & 0.60 & 0.57 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1} \cline{6-11}  & HJP75 & 53.875 & \(-\)104.045 & 1 & 0.93 & 1.44 & 0.65 & 0.67 & 0.67 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} \\ \cline{1-1} \cline{6-11}  & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of 48 validation sites and corresponding MODIS CIs. IGBP is the classification code of the International Geosphere-Biosphere Programme (IGBP) classification scheme. \(\Omega_{\rm e}\) denotes the clumping of foliage elements, and \(\gamma_{\rm e}\) is the needle-to-shoot area ratio. \(CI_{\rm Field}\) and \(CI_{\rm MOD}\) refer to field-measured CIs and MODIS-derived monthly CIs, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**ID** & **Sites** & **Lat.** & **Lon.** & **IGBP** & \(\Omega_{\rm e}\) & \(\gamma_{\rm e}\) & **CI\({}_{\rm Field}\)** & \multicolumn{2}{c}{**CI\({}_{\rm MOD}\)**} & \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Source**} \\ \hline
8 & SOBS & 53.987 & \(-\)105.117 & 1 & 0.9 & 1.36 & 0.66 & 0.60 & 0.65 & Summers & Chen et al. [96] \\
9 & Mer & 45.400 & \(-\)75.500 & 3 & 0.87 & 1.36 & 0.627 & 0.72 & 0.63 & August 2005 & [PERSON] et al. [97] \\
10 & [PERSON] & 45.210 & \(-\)68.740 & 1 & 0.98 & 1.6 & 0.61 & 0.68 & 0.55 & June 2007 & [PERSON] (unpublished) \\  & ME & & & & & & & & & \\
11 & RAMI & 58.295 & 27.256 & 1 & 0.84 & 1.42 & 0.59 & 0.58 & 0.56 & July 2008 & [PERSON] (unpublished) \\
12 & [PERSON] & 38.431 & \(-\)120.966 & 4 & 0.82 & 1 & 0.82 & 0.80 & 0.79 & September 2008 & [PERSON] et al. [98] \\  
& September 2009 & \\ \hline \hline \end{tabular}  
* MODIS CI retrievals are somewhat underestimated relative to field-measured CIs.  
\end{table} 
Table 1: _Cont._
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 2 Materials and Methods  
### Image Datasets  
Table 1 shows the two image datasets used for the experiments. The first was collected in May 2008 and covers the city of Nihonmatsu, Japan. The second was collected in May 2006 and covers the city of Yokohama, Japan. The two IKONOS images datasets were provided by the Japan Space Imaging Corporation, Japan. The spatial resolutions of the PAN and the MS images in these datasets were 1 m and 4 m, respectively. The original dataset contained PAN images with \(1024\times 1024\) pixels and MS images with \(256\times 256\) pixels for Nihonmatsu, and PAN images with \(1792\times 1792\) pixels and MS images with \(448\times 448\) pixels for Yokohama.  
The training image datasets had high-resolution PAN images with \(256\times 256\) pixels for Nihonmatsu, and high-resolution PAN images with \(448\times 448\) pixels for the Yokohama.  
To evaluate the quality of the PS images, we experimented with the test images and original images according to the Wald protocol [36]. The test images were used to evaluate the numerical image quality, and the original images were used as reference images for numerical and visual evaluation. We regarded the original images as ground truth images. The spatial resolution of the test PAN images was reduced from 1 to 4 m and that of the test MS images was reduced from 4 to 16 m. Hence, the test image datasets had PAN images with \(256\times 256\) pixels and MS images with \(64\times 64\) pixels for the Nihonmatsu, and PAN images with \(448\times 448\) pixels and MS images with \(112\times 112\) pixels for the Yokohama. The training and test images were downsampled images of the original image with bicubic spline interpolation.