esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data Preparation
### Data and Pre-Processing
#### 2.2.2 GF-1 WFV Data  
The GF-1 satellite is a Chinese sun-synchronous, high-resolution satellite. It was launched on 26 April 2013. The GF-1 satellite is equipped with two types of sensors: two panchromatic multispectral sensors (MPS) and four WFV cameras. It can acquire panchromatic images with a resolution of 2 m, and multispectral images at a resolution of 8 m or 16 m. Table 3 lists the sensors' parameters for the GF-1 satellite.  
Six GF-1 WFV datasets, provided by the GF Satellite Application Technology Centre, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, were used in this study (Table 1). These data were orthorectified with ENVI 5.0 software, using rational polynomial coefficients (RPC) with a Digital Elevation Model (DEM) and 36 GCPs selected from a 1:10,000 topographic map. The data were then atmospherically corrected using the FLAASH Atmospheric Correction Model with the ENVI 5.0 software provided by Esri China Information Technology Co., Ltd., Beijing, China.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Satellite** & **Sensor** & **Band** & **Bandwidth (nm)** & **Resolution (m)** & **Width (km)** & **Return Cycle (Days)** \\ \hline \multirow{4}{*}{HJ-1-A} & \multirow{4}{*}{CCD} & 1 & 430–520 & & & \\  & & 2 & 520–600 & & & \\  & & 3 & 630–690 & & & \\  & & 4 & 760–900 & & & \\ \cline{2-6}  & \multirow{2}{*}{HSI} & 128 bands & 450–950 & 100 & 50 & 4 \\ \hline \multirow{4}{*}{HJ-1-B} & \multirow{4}{*}{CCD} & 1 & 430–520 & & & \\  & & 2 & 520–600 & & & \\ \cline{1-1}  & & 3 & 630–690 & & 700 & 4 \\ \cline{1-1}  & & 4 & 760–900 & & & \\ \cline{1-1} \cline{2-6}  & \multirow{4}{*}{IRS} & 5 & 750–1100 & & & \\ \cline{1-1}  & & 6 & 1550–1750 & 150 & & \\ \cline{1-1}  & & 7 & 3500–3900 & & & \\ \cline{1-1} \cline{2-6}  & & 8 & 10500–12500 & 300 & & \\ \hline \end{tabular}
\end{table}
Table 2: Parameters for HJ satellite constellation sensors.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sensor** & **Bandwidth (nm)** & **Resolution (m)** & **Width (km)** & **Return Cycle (Days)** \\ \hline \multirow{4}{*}{MPS} & 450–900 & 2 & & \\ \cline{2-5}  & 430–520 & & & 4 (side swing) \\ \cline{1-1}  & 520–600 & & 8 & 41 (no side swing) \\ \cline{1-1}  & 630–690 & & & \\ \cline{1-1}  & 760–900 & & & \\ \hline \multirow{4}{*}{WFV} & 430–520 & & & \\ \cline{1-1}  & 520–600 & & & \\ \cline{1-1}  & 630–690 & & & \\ \cline{1-1}  & 760–900 & & & \\ \hline \end{tabular}
\end{table}
Table 3: Parameters for the GF-1 satellite sensors.
# 3D Numerical Simulation and Performance Analysis of CO2 Vortex Tubes
## 4 Conclusions  
In the present study, numerical simulations were carried out on the designed swirl flow pipes of 125 and 175 mm length, and the process of energy separation was discussed. By varying the inlet pressure and comparing the maximum and minimum temperatures of two swirl flow pipes of different lengths, it can be concluded that the low-temperature effect of the swirl flow pipe first increases, then decreases, with increasing inlet pressure. Furthermore, pressure has a greater influence (on temperature) than pipe length. At an inlet pressure of 0.8 MPa, the lowest axial temperature of the 125 mm swirl flow pipe was 252.4 K, and the lowest axial temperature of the 175 mm pipe was 246.0 K. In summary, increasing the pressure increases the energy separation effect of the swirl flow pipe, all other things being equal. Moreover, as the pressure increases, the heating effect of the swirl flow pipe is greater than the cooling effect. The tube length also has little effect on the radial velocity of the swirl flow pipe but increases the maximum temperature. The numerical simulation found that as the pressure increases, the single-nozzle swirl flow pipe becomes more heavily burdened, and the vortex chamber wears out. A solution can be found by studying the effect of the number of swirl flow pipe inlets. When the swirl flow pipe is used as refrigeration equipment, the inlet pressure of the swirl flow pipe can be increased appropriately to obtain a lower thermodynamic temperature. When the swirl flow pipe is used as heat-producing equipment, the maximum heat-producing temperature can be increased by appropriately increasing the tube length of the swirl flow pipe.  
Q.X.: conceptualization, formal analysis, data curation, writing--original draft, review and editing; J.X. and J.W.: conceptualization, funding acquisition, supervision, writing--original draft, review and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Radial velocity distribution for two lengths of swirl flow pipe at the same pressure.  
**Funding:** This research was supported by the Science and Technology Innovation Action Plan of the Shanghai Science and Technology Commission (19 DZ1207503) and the Public Service Platform Project of the Shanghai Science and Technology Commission (20 DZ2292200).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results and Discussion
### Comparative Study of Probability-Distribution-Function-Based WSP Models
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**RMSE**} \\ \hline
**Model** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **t = 7** & **Average** \\ \hline WEB & 3.47 & 1.68 & 1.85 & 2.02 & 2.75 & 2.94 & 2.43 & 2.45 \\ WEBS & 0.35 & 5.91 & 2.54 & 0.55 & 0.68 & 1.51 & 1.04 & 1.80 \\ WEBSEC & 0.19 & 0.25 & 3.7 & 0.63 & 0.7 & 2.65 & 2.32 & 1.49 \\ RYM & 3.45 & 1.69 & 1.86 & 2.03 & 2.76 & 2.94 & 2.44 & 2.45 \\ RYMS & 4.64 & 4.96 & 4.65 & 3.13 & 0.75 & 5.59 & 0.78 & 3.50 \\ RYMSEC & 1.41 & 0.75 & 3.28 & 1.21 & 0.66 & 2.7 & 0.37 & 1.48 \\ \hline \multicolumn{6}{c}{**MAE**} \\ \hline
**Model** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **t = 7** & **Average** \\ \hline WEB & 0.63 & 0.15 & 0.17 & 0.18 & 0.23 & 0.24 & 0.21 & 0.26 \\ WEBS & 0.06 & 0.55 & 0.23 & 0.04 & 0.05 & 0.12 & 0.12 & 0.17 \\ WEBSEC & 0.04 & 0.24 & 0.344 & 0.06 & 0.06 & 0.22 & 0.2 & 0.17 \\ RYM & 0.63 & 0.16 & 0.17 & 0.18 & 0.23 & 0.24 & 0.21 & 0.26 \\ RYMS & 0.85 & 0.46 & 0.43 & 0.28 & 0.06 & 0.47 & 0.06 & 0.37 \\ RYMSEC & 0.26 & 0.07 & 0.3 & 0.11 & 0.05 & 0.22 & 0.03 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results of different WSP models for Southern Offshore TX region. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**RMSE**} \\ \hline
**Model** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **t = 7** & **Average** \\ \hline WEB & 0.05 & 1.28 & 1.77 & 2.24 & 2.49 & 2.48 & 2.54 & 1.84 \\ WEBS & 0.94 & 3.03 & 5.56 & 0.2 & 0.1 & 2.47 & 2.9 & 2.17 \\ WEBSEC & 2.07 & 0.26 & 1.39 & 0.41 & 1.84 & 1.69 & 1.44 & 1.30 \\ RYM & 0.05 & 1.28 & 1.76 & 2.24 & 2.48 & 2.47 & 2.54 & 1.83 \\ RYMS & 0.91 & 1.63 & 0.02 & 4.29 & 8.11 & 2.8 & 0.6 & 2.62 \\ RYMSEC & 0.2 & 2.29 & 1.65 & 0.06 & 0.07 & 1.06 & 0.59 & 0.85 \\ \hline \multicolumn{6}{c}{**MAE**} \\ \hline
**Model** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **t = 7** & **Average** \\ WEB & 0.05 & 1.28 & 1.77 & 2.22 & 2.49 & 2.48 & 2.54 & 1.83 \\ WEBS & 0.94 & 3.03 & 5.56 & 0.2 & 0.1 & 2.47 & 2.9 & 2.17 \\ WEBSEC & 2.07 & 0.26 & 1.39 & 0.411 & 0.84 & 1.69 & 1.44 & 1.16 \\ RYM & 0.05 & 1.28 & 1.76 & 2.24 & 2.48 & 2.47 & 2.54 & 1.83 \\ RYMS & 0.91 & 1.63 & 0.29 & 4.29 & 8.11 & 2.8 & 0.6 & 2.66 \\ RYMSEC & 0.2 & 2.29 & 1.65 & 0.06 & 0.07 & 1.06 & 0.59 & 0.85 \\ \hline \multicolumn{6}{c}{**MAPE**} \\ \hline
**Model** & **t = 1** & **t = 2** & **t = 3** & **t = 4** & **t = 5** & **t = 6** & **t = 7** & **Average** \\ WEB & 0.007 & 0.15 & 0.19 & 0.23 & 0.25 & 0.25 & 0.26 & 0.19 \\ WEBS & 0.13 & 0.35 & 0.61 & 0.02 & 0.01 & 0.25 & 0.29 & 0.24 \\ WEBSEC & 0.28 & 0.03 & 0.15 & 0.04 & 0.18 & 0.17 & 0.14 & 0.14 \\ RYM & 0.008 & 0.14 & 0.19 & 0.23 & 0.25 & 0.25 & 0.25 & 0.19 \\ RYMS & 0.12 & 0.19 & 0.003 & 0.45 & 0.82 & 0.28 & 0.06 & 0.27 \\ RYMSEC & 0.02 & 0.26 & 0.18 & 0.006 & 0.007 & 0.1 & 0.06 & 0.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance results of different WSP models for West Virginia Hills.
# Deep Learning-Based Community Detection Approach on Multimedia Social Networks
## 3 Methodology
### Multimedia Social Network Model
In the case of user-to-user and user-to-object relationships, the relative weight can in some way be considered proportional in the first case to the frequency of relationships between users, while in the second case, it is related to the topic published. For simplicity, we can always consider the weight of these arcs to be equal to 1. Figure 1 shows an example of a given MSN in which users can be connected either because they interact with the same multimedia content (through orange hyperarcs) or because they interact with similar content (through blue hyperarcs). Such kinds of information can be very useful to community detection algorithms to identify the right clusters of users. Once the MSN model has been defined, it can be seen that there are different paths connecting two users. These paths allow estimating how users interact with each other; obviously, not all of them are eligible for relationship description, but it depends on the considered SNA application. Therefore, it is necessary to introduce the concept of a _relevant path_, which is a sequence of hyperarcs that meets a given condition \(\Theta\). Such a condition can be defined on nodes and hyperarc attributes and allows us to take full advantage of all the features of social networks. In particular, the estimation of the interaction strength between two users is performed using the concept of a _relevant social path_, which is a path between two users that can exploit multimedia objects on which they both interact or that are similar or involve other MSN users. Figure 2 shows an example of a relevant path defined between two users, based on the fact that they are interested in two similar objects. Figure 1: Example of an MSN with 4 users (yellow), 5 multimedia objects (green), 7 user–multimedia object relationships (orange), and 1 similarity relationship (blue). These paths, then, allow us to construct a weighted adjacency matrix, called the _weighted relevant path matrix_, whose element \((i,j)\) represents the probability of interaction, estimated from the number and weight of _relevant social paths_, between users \(i\) and \(j\). It should be noted that any update of the graph requires a corresponding change in the adjacency matrix; in particular, our method easily handles potential changes in network topology by exploiting the sparsity of the adjacency matrix. Figure 3 describes the generated adjacency matrix for the MSN in Figure 1, considering as relevant paths all those leveraging user-to-content and similarity relationships.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 6 On Orbit Demonstration  
The satellites were launched on 22 February 2021 and deployed from the International Space Station on 14 March 2021. The HNT mission of the Tsuru satellite was turned on at 06:01 (the following are all in UTC), 15 December 2021, by command. The Tsuru satellite passed over the Kyushu Institute of Technology between 06:14 and 06:25. The maximum elevation was 68.5 deg at 06:20. During this pass, the ground station received signals from both the dipole antenna and the Loop Hentenna. Table 3 shows the specifications of the ground stations. Figure 12 shows an example of the receiving spectrum by RTL-SDR and SDR#. Please refer to \"SDRSharp_2021125_061940Z_437375000 Hz_IQ.wav\" in Supplementary Materials for detailed data of this part. This shows the Loop Hentenna implemented on BIRDS-4 was working.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Value** & **Unit** & **Remark** \\ \hline Transmission Power & 13.0 & dBm & Measured Value \\ Line Loss (Satellite) & 1.0 & dB & \\ Antenna Gain (Loop Hentenna) & \(-\)5.7 & dBi & Measured Value \\ Pointing Loss & 10.0 & dB & \\ Polarization Loss & 3.0 & dB & \\ Ionization Loss & 1.0 & dB & \\ Atmospheric Loss & 1.0 & dB & \\ Rain Loss & 0.0 & dB & \\ Elevation & 30.0 & deg & \\ Range & 739.3 & km & 400 km altitude \\ Frequency & 437.375 & MHz & BIRDS-4 Hentenna Mission Beacon \\ Free Space Path Loss & 142.6 & dB & \\ Antenna Gain(Ground Station) & 22.0 & dBi & ref. M2 Antenna Systems, \\  & & & 436 CP42 UG 2 Stacks \\ Line Loss (Ground Station) & 3.0 & dB & \\ Receiving Signal & \(-\)132.3 & dBm & \\ Noise Temperature & 600 & K & \\ Bandwidth (BW) & 500 & Hz & \\ Noise Power within BW & \(-\)143.8 & dBm & \\ Signal Noise Ratio (SNR) & 11.5 & dB & \\ Required SNR & 10.0 & dB & \\ Link Margin & 1.5 & dB & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The link budget calculation for HNT mission.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Latitude (deg) & \(+\)33.8925 \\ Longitude (deg) & \(+\)130.8401 \\ Height & 50 m \\ Antenna & 22 element cross Yagi antenna \(\times\) 2 \\ Antenna gain & 22.0 dBi \\ Receiver & RTL-SDR \\ Receiving software & AIRSPY SDR# v1.0.0.1831 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ground station specification for HNT mission at Kyushu Institute of Technology.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.2 Differential Measurement Scheme to Reduce Background Spectrum  
Another way to reduce the background spectrum was invented by Professor [PERSON] et al. [52], which is called the \"differential measurement scheme\" [52]. In this scheme, a phase modulator is introduced in either the pump or probe path. By the PM modulation, the shape of the signal that was spectrum caused at the correlation peak position was much deteriorated, but the background spectrum shape was not so affected. Then, by calculating the difference between the BOCDA spectra of the two cases with and without the PM, the background spectrum is reduced to clearly show the signal spectrum peak. This scheme is also effective for the BOCDR. The detail of the scheme is shown in the chapter for the BOCDR.
## 2 Data and Method
### Methods
#### 2.2.3 The Normalized Tree Points (NTP)  
The elevation value of the airborne LiDAR point cloud data is the superposition of the ground object elevation value and the ground elevation value. In forestry applications, if the terrain causes the tall vegetation on the lowland to undulate greatly, the terrain will have a higher elevation value than the low vegetation on the highland, which makes the elevation value of the LiDAR point cloud data unable to directly reflect the true height of vegetation. Therefore, it is usually necessary to normalize the LiDAR data for the vegetation related research.  
Figure 2: Procedure of trees species classification: First, a DEM is generated; second, the normalized tree points are generated; third, rough tree location generation; fourth, a rotating profile analysis algorithm refines the tree locations; fifth, segmentation of the tree crowns; sixth, calculation of the key points from the segmented crowns points; seventh, tree species classification using shape fitting method.  
Point cloud normalization refers to subtracting the elevation value of the DEM, which is generated by the denoised point cloud. The normalized LiDAR point cloud data can eliminate the influence of the terrain; then the highest single tree point obtained after segmentation can be approximated as the tree height. In this process, the software LiDARMate is used to get the normalized tree points.
# Current Status of Optical Systems for Measuring Lycopene Content in Fruits: Review
## 4 Current Systems for Estimating Lycopene
### Ultraviolet-Visible (UV-Vis) and Infrared (NIR) Spectroscopy
Figure 8: Identification of the maturity of tomato using spectroscopy (NIR) from [PERSON] et al. [57]. Figure 7: Elements that integrate an optical system of spectroscopy (NIR) [72]. [PERSON] et al. [66] performed a prediction model for lycopene and \(\beta\)-carotene in tomatoes, using a laptop and Vis-NIR spectra. In the development of the model, they used a range of 500-1100 nm and reported some relationship coefficients for the lycopene calibration of R\({}^{2}\)C = 0.89092 and in the case of \(\beta\)-carotene R\({}^{2}\)C = 0.88158. The correlation coefficients for the predictions were R\({}^{2}\)p = 0.85106 and R\({}^{2}\)P = 0.77353. The transmittance spectra of the intact tomatoes were obtained with a Vis-NIR spectrometer (Life & Tech, CO, Ltd., Yongin, Korea) using a halogen lamp as the Vis-NIR light source. The measurement on the fruit was done six times to reduce the noise, obtaining a total of 3500 data for each measurement with a spectrum resolution of 0.2 nm. Together with this, a total of 1160 spectra were obtained, which represent the breaking, pink, and red maturity state of the fruits in the study. For the calibration and cross-validation process, they used 50% of the spectra for each stage. The spectra obtained from the fruits were processed to eliminate unwanted information and noise. Original spectra were treated by a Hanning Window, standard normal variance (SNV), multiplicative dispersion correction (MSC), and the first derivative. Another study focused on lycopene measurement is that of [PERSON] et al. [55], who analyzed, in a non-destructive way, the soluble solids and carotenoids in tomato products. The tomato spectroscopy NIR was acquired after opening the samples. The aliquot of the fruit was placed at the bottom of a Petri dish (Schott 23 755 48 05), and the readings were performed on a Buchi NIRLab Spectrometer N-200 (Buchi Labortechnik AG, Postfach) equipped with a diffuse reflectance cell MSC-100. The method for the calibration models was that of partial least squares (PLS). For this, 42 samples were used during the calibration stage, with 126 spectra obtained. In the validation, 37 samples and 111 spectra were used. The proposed models used three regions that were 1000 to 1250 nm, 1250 to 1666.67 nm, and 1666.67 to 2500 nm. The original spectra were pre-processed by applying a medium smoother with a window width of 15 wave numbers. Among the proposed models, the main difference is the number of spectrum regions and the number of factors or main components they use. The best model reported by [PERSON] et al. [55] is the one that used the treatment with multiplicative signal correction (MSC) and the use of the spectrum from 1250 to 2500 nm using 5 factors. The correlation factor was 0.9996 and a mean squares error of prediction of 21.5779.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 3 Energy Consumption Based Selfish Nodes  
### Selfish Nodes  
Selfishness is a normal behavior that is present in all aspects of life, and MANET is not an exception. The term \"selfish node\" appears in the work of [PERSON] et al. [35]. In this paper, a selfish node is the node that takes advantage of the network by sending and receiving data for its own interests. However, it behaves selfishly not to forward data, even the routing control packets, but for other nodes in order to reduce the power consumption.  
There are two types of selfish nodes considered in this paper: static selfish nodes and dynamic selfish nodes. Static selfish nodes are those where their selfishness is static (unchanging) during the whole network procedure. Dynamic selfish nodes are nodes where their selfishness dynamically changes depending on some conditions.
# Characterisation of Coastal Sediment Properties from Spectral Reflectance Data
## 2 Study Area and Methods
Additionally, a derived parameter herein called _span_, which describes the width of the particle size distribution, was calculated as  
\[Span=\frac{(D_{90}-D_{10})}{D_{50}} \tag{1}\]  
where \(D_{90}\) and \(D_{10}\) are the 90 th and 10 th percentile values of the grain size distribution, and \(D_{50}\) is the median grain size. The spectral signatures of sediment and shell samples were acquired under controlled environmental conditions in the laboratory using an Analytical Spectral Device (ASD)  
Figure 2: Shell samples collected from Oyster Bay. (**a**) Cape brooding oyster (_Ostraa__therstoni_, sample 1); (**b**) Brown mussel (_Perna__perna_, sample 2); (**c**) Agulhas ridged nut clam (_Lembulus__bcleri_, sample 3); (**d**) Southern cuttlefish (_Sepia australis_, sample 4). FieldSpec(r)3 spectrometer (ASD Inc., Boulder, CO, USA) and a light mug (Figure 3). The instrument measures wavelengths from 350 to 2500 nm and compares samples to a white reference panel. The 2 mm-sieved sediment and shell samples were placed in 100 mL glass bottles (Figure 3) and then placed on top of the light mug to measure the reflectance from each sample. Five spectral scans were captured for each sample to ensure spectral stability and an average reflectance was considered for further analysis. The spectrometer was calibrated using the white reference Spectralon(r) (Figure 3). The spectrometer was recalibrated after every 20 sample scans. The spectral measurement were stored in a notebook computer connected to the device. Figure 3 shows the laboratory setup used in this study. Following ASD data collection, the spectral data were first combined and converted from digital numbers to reflectance values using the ViewSpecPro(r) software. The spectral resolution was 1 nm which causes several data redundancy difficulties and affects the processing time. The spectral data also went through several pre-processing steps including removing noise-affected spectra located at the edge of the scans. First, the reflectance data less than 375 nm and greater than 2460 nm were removed from further analysis in order to disregard edge effects. Second, the moisture absorption spectral bands (at \(\sim\)2500, 1950 and 1450 nm; [44]) were therefore eliminated from the final pre-processing stage. A correlation analysis was first performed on the data to identify the spectral wavelengths that show a significant association with different sediment properties. Based on the correlation analysis, specific wavelength regions were then selected. Linear regression analysis and partial least squares regression analysis were then performed on the selected spectral wavelengths based on the magnitude of the correlation coefficient. The data were partitioned into training and testing datasets. Approximately 70% (94 samples) of the dataset was used for the statistical model training, and 30% (40 samples) was used for model testing and validation. Figure 3: Laboratory spectral measurement system. The system involves an ASD spectroradiometer and a light mug device.