esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Reboiler Heat Duty  
The reboiler heat duty of IAP was measured at various solvent flow rates, and the minimum reboiler heat duty was 3.3 GJ/t- CO\({}_{2}\) at L/G of 1.7 as shown in Figure 6. This is lower than the value for MEA by 0.7 GJ/t- CO\({}_{2}\). The optimum L/G of 1.7 for IAP indicates that the same amount of CO\({}_{2}\) could be captured with only about 40% of the solvent flow rate compared to the optimum L/G of 4.3 for MEA at a 150 Nm\({}^{3}\)/h blue gas flow rate.  
The PCCP has significant heat loss compared to commercial processes. In fact, during the operation of a 0.5 MW process employed by our research group, the minimum reboiler heat duty for MEA was measured as 3.6 GJ/t- CO\({}_{2}\), showing a gap of 0.4 GJ/t- CO\({}_{2}\) compared with the result in the PCCP. Based on this observation, IAP can be assessed to have minimum reboiler heat duty lower than 2.9 GJ/t- CO\({}_{2}\).  
Table 7 compares CO\({}_{2}\) loading values of the lean state, mid-rich state (after the first column), and rich state (after the second column) at optimum operating conditions for MEA and IAP. It can be seen that IAP reaches 82.5% of the working capacity in the first column and achieves the remaining  
Figure 5: Rich/lean loading and working capacity results depending on L/G for IAP.  
Figure 6: Reboiler heat duty outcomes for MEA 30 mass% and IAP.  
17.5% in the second column. On the other hand, MEA attains 55% and 45% of the working capacity in the first and second columns, respectively. These results show that IAP possesses a faster CO\({}_{2}\) absorption rate than MEA.
# IoT-Enabled Vehicle Speed Monitoring System
## 3 Proposed IoT-Enabled Speed Monitoring System
### Smart Speed-Monitoring Website  
The website of a Smart Speed Monitoring System (SSMS) is a platform that provides easy average speed calculations by allowing users to automatically find a vehicle that has  
Figure 4: Overall system process for detecting license plates.  
Figure 5: Launching three buckets.  
exceeded the speed limit while moving between two terminals. It provides cheap and efficient speed monitoring management that comprises more automation and less manual activity. The website is user-friendly, which allows live information about speeding vehicles on motorways between any two fixed points.  
To present the data and show the results, the website was created using HTML, CSS, and PHP, with Firebase-comprehensive APIs for use on the live website. To access the website remotely, Elastic Beanstalk assigns it a public IP address.  
All the vehicle data, such as the entry point, exit point, license plate and time taken to travel between two points, are shown on the website. The website automatically compares the data with an already installed dataset and decides whether the vehicle violated traffic laws or not. A screenshot of the website's home page is in Figure 6. The site map for the SSMS website is in Figure 7.  
The SSMS website provides all the necessary information and details regarding the speed monitoring system. It comprises the following main pages:  
(1) Home Page  
Figure 6: IoT-enabled speed monitoring system home page.  
Figure 7: Site map for the SSMS website.  
* (2) Notifications (3) Admin Panel (4) Alerts (5) User panel (6) Gross speed (7) About us (8) Contact us The SSMS website consists of six modules; each module is interactive and shares information with other modules. The two user sides (admin and staff) have their own attributes, consisting of authorized functionalities. Screenshots of user and admin login pages are presented in Figure 8. The admin side deals with the following functionalities. (1) Create new staff; update, delete and assign user passwords and IDs. (2) Add cameras, update, and delete them. (3) Process queries from the Contact Us section. (4) Fetch data sent from server to website (5) Notifications (6) Alerts on the website and the application.  
The user side deals with the functionalities below:  
* (1) See staff details. (2) Add camera, update, and delete them.  
Figure 8: Screenshots of (**a**) the User Login page and (**b**) the Admin Login page of the SSMS website.  
(3) Fetch data sent from server to website (4) Notifications (5) Alerts on website and the application.
# Energy Consumption Characteristics for Design Parameters of Permanent Magnet-Based Al Billet Heater
## 2 Theoretical Background  
### Electromagnetic-Thermal Analysis for Permanent Magnetic Heating System  
In this study, the billet heating characteristics were analyzed using electromagnetic-thermal analysis of the induction heating system. The rotary induction heating system generates a magnetic field and electric field by the movement of the permanent magnet according to [PERSON]'s law, and a heat source is generated when the electromagnetic energy is converted into thermal energy, owing to the specific resistance of the billet. On this basis, the governing equation for the electromagnetic-thermal analysis can be expressed as follows [38,39]:  
\[\
abla\times E=-\mu\frac{\partial H}{\partial t} \tag{1}\]  
\[\
abla\times H=\varepsilon_{0}\big{(}\epsilon_{r}^{\prime}-j\dot{\epsilon_{r} ^{\prime\prime}}\big{)}\frac{\partial E}{\partial t} \tag{2}\]  
where \(E\) is the electric field, \(H\) is the magnetic field, \(\mu\) is the permeability, \(\varepsilon_{0}\) is the free space permittivity, \(\varepsilon_{r}^{\prime}\) is the dielectric constant, and \(\dot{\epsilon_{r}^{\prime\prime}}\) is the dielectric loss factor. Furthermore, as the billet rotates, the magnetic field and electric field are changed by the relative movement of the permanent magnet. Thus, the dissipated power density can be expressed as follows:  
\[Q=\pi f\varepsilon_{0}\varepsilon_{r}^{\prime\prime}|E|^{2} \tag{3}\]  
where \(f\) is the frequency. The frequency value is changed by the number of permanent magnets and rotational speed of the billet heater. Furthermore, the reactance of the billet is changed by the frequency, and a Joule heat (\(Q\)) is generated inside the billet as a result. The generated heat source can be expressed by the transient temperature change over time using the Fourier law and thermal conductivity equation as follows:  
\[\overset{\rightarrow}{\phi}=-[k]g\pi a\overset{\rightarrow}{d}T \tag{4}\]  
\[Q=div\big{(}\overset{\rightarrow}{\phi}\big{)}+\rho C_{p}\frac{\partial T}{ \partial t} \tag{5}\]  
where \(k\), \(\phi\), \(\rho\), and \(C_{p}\) are the thermal conductivity, heat flux density, density, and specific heat, respectively. For electromagnetic-thermal analysis of the billet heater using permanent magnets, the dissipated power density is calculated using Equation (3) based on the Maxwell energy equations (Equations (1) and (2)). For the relationship between the dissipated power density and temperature, the billet temperature characteristics over time were analyzed on the basis of the transient thermal energy equation of Equation (5). In other words, they were analyzed via co-simulation based on the Maxwell energy equation and the transient thermal energy equation.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Study of FL-LoRaMAC
#### 6.2.3 Energy Consumption of FL-LoRaMAC  
As discussed earlier, FL can be implemented using legacy LoRaWAN protocol using the Class-C mode of operation for end devices. However, doing so will incur enormous energy costs. This section compares the energy consumption of the proposed FL-LoRaMAC framework to the Class-C operation of the legacy LoRaWAN protocol.  
The uplink transmissions for FL-LoRaMAC are similar to those of the legacy protocol. Hence, the energy consumption for uplink transmissions will be the same for both cases. However, for downlink, the end devices according to FL-LoRaMAC will conserve energy by putting its radios to sleep.  
The energy consumed by the LoRa transceiver at the end device for receiving one downlink packet in FL-LoRaMAC can be calculated as follows:  
\[E_{T}=E_{S}+E_{Un}+E_{Sl} \tag{6}\]  
Here, \(E_{S}\) is the energy consumed when the device starts listening and actually receives the downlink packet, \(E_{Un}\) is the energy consumed when the device starts listening and does not receive any data, and \(E_{Sl}\) is the energy consumed by the transceiver module in sleep mode.  
The energy consumption depends on the operating voltage of the transceiver, the current drawn, and the duration for which the current is drawn. The SX1276 LoRa module operates at 3.3 V, draws 10.8 mA (\(I_{R}\)) during receive mode, and 0.0002 mA (\(I_{Sl}\)) in sleep mode [30]. The gateway transmits downlink frames once every 10 s. In FL-LoRaMAC, the end devices open receive windows every 100 millis, each for 5 millis, and then go to sleep if they do not detect any LoRa preamble. \(E_{S}\), \(E_{Un}\), and \(E_{Sl}\) can be found according to Equations (7)-(9).  
\[E_{S} = V*I_{R}*T_{R}=3.3\;\mathrm{V}*10.8\;\mathrm{mA}*400\;\mathrm{ms} \tag{7}\] \[E_{Un} = V*I_{R}*T_{R}=3.3\;\mathrm{V}*10.8\;\mathrm{mA}*480\;\mathrm{ms}\] (8) \[E_{Sl} = V*I_{Sl}*T_{Sl}\] (9) \[= 3.3\;\mathrm{V}*0.0002\;\mathrm{mA}*(1000-480-400)\mathrm{ms}\]  
Here, \(T_{R}\) and \(T_{Sl}\) are time durations of reception and sleep, respectively. Using Equation (6), the energy consumption of the LoRa transceiver for one downlink packet is 0.0087 mWH. Using SF-7, 13 packets need to be sent in the downlink for transmitting all model parameters in one communication round. Hence, the energy consumed by the transceiver to receive all the parameters in one communication round is 0.1131 mAH.  
In the case of legacy Class-C operation, the transceiver module always stays in receive mode. It will use the standard eight-symbol preamble, not the elongated preamble, so a data packet of SF7 can have 255 bytes. In this scenario, 10 packets will be sufficient for transmitting all 635 model parameters. The energy consumed by the transceiver to receive all model parameters will be 10 \(\times\) (3.3 V \(\times\) 10.8 mA \(\times\) 10,000 s) = 0.99 mAH. These calculations indicate that the legacy Class-C devices will consume approximately nine times more energy than the proposed FL-LoRaMAC for receiving model parameters.
# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
## 2 Overview of UOWC Systems
### Signal Processing Techniques
Another ML algorithm explored in the UOWC system is reinforcement learning, which focuses on developing an optimized strategy by monitoring how an intelligent agent acts in an environment to maximize cumulative reward. The reinforcement learning method has been applied to improve the communication stability [118; 119; 120] and to reduce the power consumption and improve link quality via optimizing the routing protocol in an underwater sensor network [121; 122]. In Section 4, we provide a comprehensive survey on the recent UOWC progress based on ML algorithms.
## 4 Results
### Results of Inference with Optimal DNN Model  
The optimal DNN model was used to predict the ore production and crusher utilization over 10 days, starting on 1 November 2018. Figure 10 presents a comparison of the ore production observed  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Data Type**} & \multicolumn{2}{c}{**Statistics**} \\ \cline{2-3}  & **Coefficient of Determination (R2)** & **MAPE (\%)** \\ \hline Training data & 0.99 & 2.30 \\ Test data & 0.99 & 2.49 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of evaluating coefficient of determination and MAPE of optimal DNN model, with four hidden layers and 40 hidden layer nodes, for predicting crusher utilization.  
Figure 9: Results of correlation analysis between measured and predicted crusher utilization (%) using DNN model with four hidden layers and 40 hidden layer nodes: (**a**) training data and (**b**) test data.  
at the actual mining site and that predicted by the optimal DNN model. The ore production predicted by the DNN model matched the actual observed values for most days. However, in the case of 7 November, the observed values did not match those predicted by the DNN model. This is because the trucks repeatedly visited a path with a longer travel distance than the path normally visited throughout the overall haulage task. Therefore, the actual observed ore production was lower than the value predicted by the DNN model.  
Table 8 displays the RMSE of 10 days of ore production, as predicted by the optimal DNN (case 1), and the RMSE of 9 days of ore production, excluding 7 November (case 2). The RMSE of the 10 days of ore production as predicted by the DNN model was approximately 10 tons. The RMSE of the 9 days of ore production was approximately 7 tons.  
Figure 11 presents a graph comparing the crusher utilization observed over 10 days while the haulage work was performed at the mine site and the values predicted by the optimal DNN model. The values observed over the 10 days tended to match the crusher utilization predicted by the DNN model. However, as with the ore production, the crusher utilization observed on 7 November differed from the predicted value.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistics** & **Case 1** & **Case 2** \\ \hline RMSE (tons) & 10.16 & 6.92 \\ \hline \hline \end{tabular}
\end{table}
Table 8: RMSE of optimal DNN model for predicted ore production during 10 working days (case 1) and predicted values excluding 7 November (case 2).  
Figure 10: Comparison of measured ore production and value predicted using optimal DNN model.  
Table 9 displays the RMSE of 10 days of crusher utilization as predicted by the DNN model (case 1) and the RMSE of 9 days of crusher utilization, excluding 7 November (case 2). The RMSE of the 10 days of crusher utilization was approximately 0.07%, while that of the 9 days of crusher utilization was comparatively low at approximately 0.04%.
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 3 Experimental Results
### Influencing Factor
#### 3.2.2 Drill Tool Rotation Speed  
Figure 10 illustrates the relationship between cuttings return velocity and maximum bed height at different drill tool rotation speeds. As the rotation speed increases, the total migration time of cuttings in the annulus gradually decreases, leading to a higher volume of cuttings being returned. However, the effect of rotation speed is not as significant as that of the drilling fluid flow rate. Specifically, at a rotation speed of 50 r/min, the return velocity of cuttings is 1.52 times greater than at 10 r/min, resulting in a 36.1% reduction in the height of the cuttings bed.  
The reduction in cuttings bed height is inversely proportional to the drill tool's rotation speed at a given flow rate. This is due to the increased circumferential velocity of the drilling fluid caused by the higher rotation speed, which enhances the lifting force on the cuttings. As a result, the suspended cuttings are transported more efficiently away from the wellhead. However, the impact of rotation speed on cuttings bed height diminishes at very high speeds. This reduced effect may be due to the already lowered cuttings bed height at higher rotation speeds, which decreases the circumferential velocity within the center of the cuttings bed, ultimately reducing the lifting force on the remaining cuttings.
# Nanoarchitectures in Management of Fungal Diseases: An Overview
## 4 Nanocarriers in Management of Fungal Infections
### Vesicle Systems
#### 4.1.4 Transethosomes  
Transethosomes, a combination of both ethosomes and transfersomes, contain phospholipids; a high ethanol concentration (30-40%); edge activators (span 80, span 65, sodium deoxycholate, span 60, tween 20, tween 80, and tween 60); and water. [PERSON] et al. architectured a novel carrier based on transethosome for the improved skin penetration of VCZ. Transethosomes containing VCZ were synthesized and compared with ethosomes, conventional liposomes, deformable liposomes, and controlled (polyethylene glycol) solutions. The study data showed that >90% of vesicles were recovered among all the vesicles due to the presence of ethanol (7-30%). Therefore, transethosomes could serve as a potential subcutaneous DDS for VCZ [42].  
The key goal of the research work of [PERSON] and [PERSON] was to fabricate transethosomes for the effective dermal delivery of econazole nitrate. Transethosomal gel showed more antifungal activity than commercial cream. The transethosomes could deliver coronazole nitrate in a controlled manner to eliminate cutaneous _candidiasis_[43].
# RETRACTED: Image Super-Resolution Algorithm Based on Dual-Channel Convolutional Neural Networks
## 4 Experimental Results and Analysis  
### Parameter Settings  
The experiment used 91 pictures by [PERSON] et al. [39] and one hundred \(2K\) high-definition images selected from the DIV2K dataset. In short, a total of 191 images were used as training datasets to train the network model. Considering that dataset size directly affects network performance, two methods of data expansion were adopted for the image, based on the original training dataset. The image was amplified in two ways: (1) Scaling: each image was zoomed in proportion to 0.9, 0.8, 0.7, and 0.6; (2) Rotating: each image was rotated by 90 degrees, 180 degrees, and 270 degrees. Each image was used 20 times, such that 3820 images were eventually available for the training process. In this process, the sub-sampling size was \(48\times 48\), the initial learning rate of the network was set to 0.001, and the Adam optimization method was adopted to automatically adjust the learning rate so that the network parameters could be learned smoothly. The number of images per batch was set to 64, and the network was trained 1000 times. The testing dataset comprised the internationally common datasets \"Set5\" [40,41] and \"Set14\" [42,43]. The GPU was NVIDIA GeForce 1080 Ti, the experimental environment was Keras, and Python 3.5 and OpenCV 3.0 were applied to carry out the simulation experiments. The results of the network training were compared with those of existing methods in terms of three aspects: subjective visual effect, objective evaluation index, and efficiency comparison.
## 3 Testing on the Spot  
The existing roadbed monitoring tests were carried out on the project site. The tests mainly measured the lateral displacement of the soil between new and old lines, the horizontal displacement of the excavated roadbed slope toe, and the horizontal stress of soil layers at different depths.  
Figure 2: Diagram of the influence of the new line construction on the existing subgrade. (**a**) Impact of foundation excavation on existing lines; (**b**) impact of piling on existing lines; (**c**) impact of roadbed filling on existing lines.  
In order to observe the lateral displacement of the deep soil, two inclinometer pipes were buried on site. The inclinometer pipes were buried at a depth of 22 m and placed at the foot of the excavation slope of the existing roadbed (the edge of the raft plate of the new pile-raft foundation). In order to measure the horizontal displacement of the slope toe of the existing line base more accurately, the horizontal soil-strain gauges were buried at depths of 0.5, 0.8, and 1.5 m at the position of the excavation slope toe, and one was placed every 1.8 m along the longitudinal direction of the line. The embedded horizontal strain gauge used in the test was less affected by construction interference and had high sensitivity. The element was embedded horizontally in the soil, and the preset shrinkage amount was 20 cm (that is, the test limit); after burying it, the surrounding area was covered with medium-coarse sand and compacted, the lead wire was protected by a wire hose, and the internal spring of the strain gauge was under the action of the horizontal load. Displacement was generated, and the displacement data were output through the electric frequency with centimeter accuracy and were used for the high-precision testing of the lateral displacement of the existing line foundation slope toe during construction.  
In order to measure the change in the stress-field distribution between the old and new subgrades during the construction period, stress shovels were buried to test the horizontal lateral stress at different depths between the two lines. The stress shovels were divided into two groups, each of which contained five components with burial depths of 1, 3, 5, 7, and 9 m; the two groups of elements faced the new Shanghai-Nanjing line and the existing Beijing-Shanghai line, respectively. The test point components were buried as shown in Figures 3 and 4.  
Figure 3: The burial profiles of the test components.
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 2 The Establishment of the Cable Icing Data Set
### Image Classification Data Labels  
At present, the stay cable deicing robot mainly uses impact deicing [1] and rotary deicing for deicing. With the increase in the icing amount, the deicing strength must also improve accordingly. Therefore, the influence of the surface ice and icicles on deicing should consider classification. However, a unified classification standard for the severity of cable  
Figure 4: Examples of icing images. (**a**) Original image, (**b**) Clipped image.  
Figure 5: Image enhancement. (**a**) Original image, (**b**) Horizontal flip image, (**c**) Vertical flip image.  
This study used a method to calculate the pixel area and comprehensive icing state of cable icing through the U-Net model. The degree of cable icing was divided into three levels: zero level, first level, and second level, as shown in Table 2. Equation (1) calculates the area of pixels in the red and green channels.  
\[S=S_{\text{icicle}}+S_{\text{surface ice}} \tag{1}\]  
\(\mathbf{s}_{icicle}\) represents the pixel area of the icicles after segmentation, \(\mathbf{S}_{\text{surface ice}}\) represents the pixel area of the icing after segmentation, and S represents the pixel area of the icing.  
Take the cable-stayed sleeve with a diameter of 20 cm as an example. Image observation shows that when S is about 100,000 mm\({}^{2}\), the surface ice initially wraps the cable-stayed sleeve, and there is a small number of icicles. Therefore, 100,000 mm\({}^{2}\) is taken as the critical point. Considering the current icing condition and the area of icing pixels, the icing degree of the cable is divided into different grades, as shown in Figures 6-8. Compared with the method of only using the icing pixel area to quantify the icing degree of stay cables, this method has a better generalization ability.