esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of an Indoor Visible Light Communication System Using Optisystem
## 5 Hardware Design of VLC Transmitter and Receiver  
A VLC transmitter that uses an LED as a transmitter is shown in Figure 8. The circuit diagram's first section depicts the conversion of 12V DC to 5V DC. A 5V DC supply powers the LED. To transform signals from an RS-232 serial port into signals usable in transistor-transistor logic (TTL)-compatible digital logic circuits, we employed the MAX232 IC in the circuit. It is common practice to convert the RX, TX, clear to send (CTS), and request to send (RTS) signals using the MAX232 dual transmitter/receiver. A dB9 connection is used to connect the MAX232's output. A cable connects the dB9 connector to the input from the personal computer (PC). We enter the values into the PC using the XCTU software.  
Figure 7: VLC system model with a single reflection wall and LED is placed on the room ceiling.  
The transmitted data are translated to binary values in the XCTU terminal, and the LED begins blinking based on these binary values. We can adjust the baud rate, flow rate, data bits, parity bits, and stop bits in the software terminal. The XCTU terminal used in our implementation is shown in Figure 9.  
A phototransistor is used to detect the incoming light pulses produced by the transmitter on the receiving side. In a phototransistor, light energy is transformed into electric energy. When an LED blinks, the phototransistor in the VLC receiver notices it and turns the information into data. A phototransistor's output is coupled to an LM358 integrated circuit. The dual-channel Op-amp IC LM358 is a low-power, user-friendly device. This IC is primarily used to run at various voltages from a single power source. The LED attached to the LM358 IC serves only as a simple signal of data reception. The LED goes ON when data are received; if not, it remains OFF. The dB9 connector, which is used to connect to the PC, is attached to the LM358's output. We employ the same XCTU software on the PC that we did on the transmitter side. The exact specifications of the transmitter terminal must apply to the receiver terminal's baud rate, flow control, data bits, parity bits, and stop bits. The schematic for the receiver is shown in Figure 10.  
Figure 8: VLC transmitter design for hardware implementation.  
Figure 10: VLC receiver design for hardware implementation.  
Figure 9: XCTU terminal.
## 3 Retention Modeling  
### Model Development  
For a retention model to be useful, it should relate the retention factor \(k\) to operating conditions such as pressure, temperature, and composition of the mobile phase. Once such a model is obtained, the retention factor can then be related to the distribution  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Column** & **Kromasil®** & **Kromasil®** & **Kromasil®** & \\  & **60-5-SIL** & **100-5-SIL** & **300-5-SIL** & **Aerogel** \\ \hline Abbreviation & SIL-60 & SIL-100 & SIL-300 & SIL-Aerogel \\ \({}^{*}\) Particle size, \(\mu\)m & 6.2 & 6.1 & 6.2 & 17.4 \\ \({}^{**}\) Mean pore diameter, nm & 4 & 6 & 23 & 27 \\ \({}^{**}\) Specific surface area, \(\text{m}^{2}\,\text{g}^{-1}\) & 540 & 320 & 110 & 858 \\ \({}^{**}\) Specific pore volume, \(\text{cm}^{3}\,\text{g}^{-1}\) & 1.1 & 0.9 & 0.9 & 5.2 \\ \hline \hline \end{tabular} \({}^{*}\) Measured by electrical sensing zone particle analyzer and scanning electron microscope. ** Measured by N2-adsorption/desorption with Nova 3000e surface area and pore analyzer (Quantachrome instruments); BET and BJH methods were employed for surface area and pore volume, respectively.  
\end{table}
Table 1: Textural properties of the porous materials employed as stationary phases.  
constant of a solute between mobile phase and stationary phases (Equation (2)) and other thermodynamic quantities [14].  
\[k=D\beta=\frac{c_{s}}{c_{m}}\beta \tag{2}\]  
Here, \(D\) is the distribution coefficient, \(c_{s}\) and \(c_{m}\) are the concentrations of a solute (mg  mL\({}^{-1}\)) in the stationary and mobile phases, and \(\beta\) is the ratio of stationary to mobile phase volumes [19].  
With a retention model in hand which is valid at zero modifier concentration, the retention factor \(k_{0}\) can be then extracted from experimental retention data. In this work, two classes of retention models were developed based on different assumptions.
# Effect of Surface Roughness on Early Stage Oxidation Behavior of Ni-Base Superalloy IN 625
###### Abstract  
In the present work the effect of surface roughness on oxidation behavior during the early stages of high temperature exposure of Ni-base superalloy IN 625 is described. The surface roughness was described using standard contact profilometer as well as novel method, fractal analysis. It was found that the different surface preparation resulted in a difference in roughness with a parameter increase of at least one order of magnitude for the ground sample as compared with the polished sample. The oxidation test was performed in a horizontal tube furnace. Post-exposure analyses including glow discharge optical emission spectrometry (GD-OES) and scanning electron microscopy (SEM), which revealed that grinding lowers the oxidation kinetics of IN 625 from \(1.76\times 10^{-12}\) cm\({}^{2}\cdot\)s\({}^{-1}\), obtained for polished sample, to 9.04 \(\times\)\(10^{-13}\) cm\({}^{2}\cdot\)s\({}^{-1}\). It was found that surface preparation influences the oxide scale composition and morphology. The hypothesis explaining the mechanism responsible for the changes in oxidation behavior is proposed as well.  
Ni-base superalloy; IN 625; oxidation kinetics; surface roughness; fractal analysis Article
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 3 Results
### Phantom Experiment  
#### 3.2.1 Point Target Phantom  
In the point target phantom, there is a point at 0.28 mm and a hyperechoic region with a radius of 4 mm. Figure 9 shows the beamformed images of this complex scenario from depths 20-35 mm using CPWC, CPWC with Hamming apodization, and our CPWC + SVD method over a 60 dB display dynamic range. The performance of our algorithm is still robust in this situation. It can be seen in Figure 9 that the proposed method maintains the mainlobe with the same width as CPWC. To compute the mean values in the hyperechoic region and in the background, we considered two circles with diameters of 4 and 6 mm, located at \(-\)10 and 10 mm in the lateral direction, respectively. Table 4 gives the quantitative results. While the parameters of the resolution are near equal in CPWC and our CPWC + SVD method, our proposed beamformer can find an improvement of 2.6 dB in CR due to the sidelobe reduction outside the hyperechoic region. Compared to the Hamming CPWC, our beamformer shows a small contrast improvement due to the better sidelobe suppression ability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Beamformer** & **CR (dB)** & **CNR** \\ \hline CPWC & 27.85 & 3.11 \\ CPWC + Hamming & 31.37 & 3.34 \\ CPWC + our SVD filter & 33.52 & 3.48 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contrast ratio (CR) and contrast-to-noise ratio (CNR) of the simulated cyst phantom.  
Figure 8: Beamformed images of simulated cyst phantom: (**a**) CPWC; (**b**) CPWC with Hamming window; and (**c**) CPWC + SVD. All images are shown with a dynamic range of 60 dB.
## 4 Experiments and Comparisons  
### Experimental Data  
Data set refers to a common data set in the field of image processing. The most widely used data sets are image net, urban100, general-100, set5, set14, manager109, bsd300 and bsd500. The test data often used in image processing are set5, set14 and manager109. The infrared image and visible image databases [21; 22] have also been applied to various computer vision tasks (such as image enhancement, pedestrian detection and region segmentation), but most of the data sets do not consider as high-resolution IR. Therefore, RGB and IR pairs are used as inputs to the network to effectively train a deeper network. The Kaist dataset contains images acquired through day and night, mainly outdoor (garden, playground and building) scenes. Because infrared is sensitive to thermal radiation, the human body is conspicuous in IR image at night time.  
In order to train the depth network effectively, both the variety and quantity of the image database need to be extensive. In this paper, we collected datasets containing IR and RGB images using the designed infrared imaging system and Canon EOS 700D camera (Dafen, Japan). The datasets contain day and night images of the sea scene. For the same scene image, an image registration algorithm based on local self-similarity is used according to the target size and resolution of IR. The whole flow chart is shown in Figure 8. We collected 200 pairs of IR and RGB images with different lighting conditions and scenes to form the experimental data, and combined them with 300 pairs of images selected from the Kaist dataset to form final experimental datasets. We provided the download address of the Kaist dataset at the end of this paper.  
Among all the data collected and combined, 400 pairs of images are taken as the training dataset, which its main image pairs include IR and corresponding RGB images. In order to expand the training dataset, the image pairs are horizontally flipped, vertically flipped or rotated by 90 degrees, 180 degrees and 270 degrees, and then are enlarged two, three and four times, using interpolation method restore the size and to get new data. The sample size, which is cut to 128 \(\times\) 128, are sent to the network for training. After the training is completed, the pre-trained model is capable of performing super-resolution at 2\(\times\), 3\(\times\) and 4\(\times\) scales, respectively. During the test, we are able to select the trained model parameters and a new input of image for model performance verification.
###### Abstract  
Low Earth Orbit (LEO) satellite networks can provide complete connectivity and worldwide data transmission capability for the internet of things. However, arbitrary flow arrival and uneven traffic load among areas bring about unbalanced traffic distribution over the LEO constellation. Therefore, the routing strategy in LEO networks should have the ability to adjust routing paths based on changes in network status adaptively. In this paper, we propose a Two-Hops State-Aware Routing Strategy Based on Deep Reinforcement Learning (DRL-THSA) for LEO satellite networks. In this strategy, each node only needs to obtain the link state within the range of two-hop neighbors, and the optimal next-hop node can be output. The link state is divided into three levels, and the traffic forwarding strategy for each level is proposed, which allows DRL-THSA to cope with link outage or congestion. The Double-Deep Q Network (DDQN) is proposed in DRL-THSA to figure out the optional next hop by inputting the two-hops link states. The DDQN is analyzed from three aspects: model setting, training process and running process. The effectiveness of DRL-THSA, in terms of end-to-end delay, throughput, and packet drop rate, is verified via a set of simulations using the Network Simulator 3 (NS3).  
LEO satellite networks; satellite routing; state aware; virtual node; deep reinforcement leaning +
Footnote †: journal: Accepted: 20 August 2019; Published: 22 August 2019
## 3 Effect of External Ionization Field  
External ionization sources, such as radioactive elements in the ground and cosmic radiation, consistently influence the dielectric performance of HVDC gas-insulated equipment [14].  
In the realm of nuclear fusion applications, HVDC-GISs are forced to operate in a harsh environment due to high ration levels, mainly \(\gamma\)-rays and neutrons coming from the MCF device. In the early stages of the development of NNBI of ITER, the issue of radiation drove the re-design of the SF\({}_{6}\)-insulated high-voltage pushing (HVB), connecting the last part of the TL to the ion acceleration grids. Indeed, the estimated absorbed dose rate in SF\({}_{6}\) of about 0.1 Gy/s results in unacceptable levels of ionization current \(I_{sat}\) flowing in the gas [24]. This is the RIC phenomenon. In the case of ITER NNBI, the SF\({}_{6}\)-insulated HVB is substituted with a SF\({}_{6}\) to vacuum HVB rated for the \(-1\) MV voltage level [5; 25]. An experimental formula for estimating \(I_{sat}\) for a given volume of gas \(v\), with a pressure \(P\) [MPa], and subjected to an absorbed dose rate \(\phi\) is  
\[I_{sat}=f_{gas}\times v\times P\times\phi \tag{4}\]  
where \(f_{gas}\) is a gas-dependent constant [24]. As illustrated in Figure 2, showing the Tokamak hall of DTT, the HVB connecting the TL to the ion source is subjected to the irradiation level of the machine [9].  
Figure 1: Portion of solid–gas interface layer \(\Gamma_{S}\) of thickness \(\delta\). The normal and tangent vectors (\(\mathbf{n}\) and \(\mathbf{t}\), respectively) and the direction of current densities across the layer are also drawn.  
Equation (4) can in principle be used to estimate the saturation current; however, the trend expressed by such an equation is based on experimental results with controlled geometries and cannot reproduce the effective electrode configuration of the component. To obtain information about the actual current density within the insulating gas under a high-ionization field for general geometries, more accurate numerical models are required. The way the ionization field affects the dielectric gas conductivity \(\sigma_{\text{G}}\) is a central topic for modeling HVDC-GISs for nuclear fusion equipment.
# A Systematic Revision of the NFIP Claims Hazard Data in Florida for Flood Risk Assessment  
[PERSON]  
1 Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306-2840, USA; [EMAIL_ADDRESS] (D.W.S.); [EMAIL_ADDRESS] (S.C.)1 Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL 32306-2840, USA; shin@coaps.  
-flood depth and damage functions, which involve disparate relationships that match poorly with observations. However, these studies used the original NFIP claim data without any modification attempts in their analysis and/or development of vulnerability curves.  
The widely used standard flood depth and damage curves were compiled by the United States Army Corps of Engineers (USACE) [7]. The USACE developed these curves beginning in 1970 using early NFIP loss data, local Corps studies, and the collective judgment of experts [8]. They were assumed to be well calibrated and universally applicable when used in the majority of engineering communities, even though their simple relationships do not match well with observations. Therefore, [PERSON] et al. [9,10] developed a better set of flood vulnerability curves using some modified NFIP claim data for the FPFLM project. However, they did not present the detailed analysis and revision process for the NFIP loss data in their paper.  
The FLOIR provided the FPFLM group with the NFIP claim data in Florida. Benefiting from this special access, the authors analyzed the NFIP claims filed between 19 July 1975 and 10 January 2014 in the current study. The claim data contained flood hazard information (e.g., date and type of event, cause of loss) and building information (e.g., house location, values, elevation, age). However, the provided NFIP claim data were not always complete or accurate. Thus, it was necessary to make significant modifications to the NFIP claim data to properly assess the risk of all significant hazard events leading to flooding. The authors undertook a major task to identify the hazard event associated with each claim in the NFIP database. While the NFIP claim data included a _catastrophe number_ that purports to identify the event, it was found that this information was often incorrect or identified as unknown. Furthermore, the NFIP database listed a _cause of loss_, which the authors also found to be inaccurate or unknown.  
The paper is organized as follows: the provided NFIP claim data and additional data used to revise the claims hazard information are presented in Section 2; Section 3 shows the revision process of the NFIP claim hazard data, the results thereof, and further analyses for flood risk assessments; and the conclusions follow in Section 4.
# Snack Texture Estimation System Using a Simple Equipment and Neural Network Model +
## 2 System Structure  
Figure 2 shows the system construction. In Figure 2a, the equipment comprises an air cylinder that moves the metal probe up and down; under the flat and round metal probe is the food sample, i.e., potato chips. The air cylinder moves the probe up and down once it gains air pressure. On the other hand, the load sensor is a load cell fixed between the probe and the air cylinder rod, while the sound sensor is fixed on the metal probe. The system structure is shown in Figure 2b. Signals from the sound and load sensors are amplified and transmitted to the computer via a data acquisition device. As noise is not filtered, the experiment should be performed in a quiet environment. The computer calculates input parameters of the neural network model: W1-W5 and F1-F5 express the characteristics of the load change and the sound features, respectively. The model then outputs the texture level range of (0,1) for \"crunchiness\" and \"crispness\". Here, \"crunchy\" texture refers to a feeling with a certain load accompanied by a loud sound, while \"crispy\" texture is the feeling with small load accompanied by high-frequency sound.  
Food viscosity or elasticity are measured by a rheometer [11], which is generally used to measure only the force response of the food through an electrical motor that moves the probe. In our proposed equipment, we focus on measuring sound and relate it to \"crunchiness\" or \"crispness\" of the food specimen. For this reason, we employ the air cylinder instead of the electrical motor, which causes mechanical noise.  
As shown in Figure 3, if such an intelligent system is connected to the internet, then a user holding the equipment can obtain texture information from the server quickly due to the neural network model trained by a big amount of data.  
Figure 3: Schematic of the proposed system connected to the internet.  
Figure 2: System construction: (**a**) Image of the proposed equipment and; (**b**) system structure for estimation of “crunchiness” and “crispness” textures.
# From Modeling to Failure Prognosis of Permanent Magnet Synchronous Machine
## 4 Aging of PMM's Components
### Aging of the Stator's Coil  
The stator's coils are made of copper wire and its insulation. The main causes of the wire's aging are temperature, humidity and corrosion. The internal resistance of the wire is considered the parameter that impacts its age. The aging equation of resistance is [32]  
\[\frac{\Delta\text{R}}{\text{R}^{0}}=\text{A t}^{\text{n}}\;\text{e}^{(-\frac{ \text{T}}{\text{T}})}\;\%. \tag{19}\]  
T: operating temperature of resistor in \({}^{\circ}\)C. t: time the resistor is operating at temperature T in hours. R0: initial value of the resistor in ohms. \(\Delta\)R: increase in resistance of the resistor operating at temperature T for a time t, in ohms. A: statistically independent random variables (the value of this variable can be taken as \(1.51\times 1012\).) n: calibration parameter (it can be considered '0.610'). T0: initial temperature.  
The type of copper used in the machine's coil is of class 200 \({}^{\circ}\)C, grade 2. The wire's diameter is 0.6 mm and its resistance is 0.05876-0.06222 ohm/m. At ambient temperature, 20 \({}^{\circ}\)C, the resistance of one phase is 0.42 ohm [33].  
According to the Copper Development Association, the lifetime of the conductor part of the wire is 100 years. The lifetime of the wire's insulation is 70 years. This remains true if no fault or abnormal environmental characteristics occur. However, the average life cycle of a permanent magnet machine is up to 14 years. Hence, it is useful to study the aging effect of copper resistance during the lifetime of the machine only. Table 4 shows the variation of the coil's resistance value due to aging, where no physical fault occurs in the machine.  
Although the resistance of the coil increases over the years, its impact is negligible on the operation of the machine as long as no fault occurs and the temperature at the coil level is within the tolerated values.  
According to [34], the insulation's aging is directly related and inversely proportional to temperature (T). It is expressed as  
\[\text{InsulationAging}=\text{A}\ast\exp(\frac{\text{B}}{\text{T}}). \tag{20}\]  
A and B are constant characteristics of the insulation. The aging is in years and the temperature is in Celsius.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Time (year)** & \(\Delta\)**R/R0 (\%)** & \(\Delta\)**R** & **Rnew** \\ \hline
1 & 7.622031 & 0.032013 & 0.452013 \\
5 & 17.04338 & 0.071582 & 0.491582 \\
10 & 24.10298 & 0.101233 & 0.521233 \\
14 & 28.51903 & 0.11978 & 0.53978 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variation of coil’s resistance due to aging.  
The insulation aging degradation function of the machine's temperature ranges is illustrated in Figure 23. According to this figure, if normal operation of the electric machine is guaranteed, there is no need to worry about the insulation aging. However, when a fault occurs, insulation aging becomes a huge concern; ignoring it will lead to catastrophic consequences.
# From GPS Receiver to GNSS Reflectometry Payload Development for the Triton Satellite Mission
## 2 GPS Receiver (GPSR) Design/Development
test, a vibration test, a thermal vacuum test, an electromagnetic compatibility (EMC) test, and a radiation test [15]. \begin{table}
\begin{tabular}{c c} \hline \hline Input power & 28 V/3.5 W \\ \hline Electrical interface & RS422/UART@115200 bps \& 1 IPS output \\ \hline Tracking channels & GPS L1 C/A\(\times\)12 \\ \hline Tracking threshold & 33 dB-Hz \\ \hline Max Doppler shift & \(\pm\)65 KHz. \\ \hline Position accuracy & Better than 8 m (1 sigma) \\ \hline Velocity accuracy & Better than 0.1 m/s (1 sigma) \\ \hline Time accuracy & Better than 1 micro-second \\ \hline Cold start time & Within 90 s \\ \hline Trajectory dynamic & Up to 12 g \\ \hline Mass & 1.8 kg \\ \hline Dimension & 160 \(\times\) 120 \(\times\) 40 mm \\ \hline Mission life & 5 years \\ \hline Total doze & 35 Krad \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The specifications of the NSPO GPSR. Figure 7: The tracking stability of the NSPO GPSR.