esa-sceva/satcom-chunk-collection huggingface dataset:
# A Predictive Model of Chlorophyll a in Western Lake Erie Based on Artificial Neural Network
## 4 Conclusions  
In this study, the process-based model GLM-AED and the statistical model ANN were established to predict water quality conditions in western Lake Erie. Based on the same inputs as in GLM-AED and the simulated Chl-a concentrations from calibrated GLM-AED during 2002 and 2010, we trained the ANN model, and then we compared the predicted Chl-a concentrations from GLM-AED and ANN with GLENDA observations during 2011 and 2015. The smaller RMSE and MAE values between ANN predictions and observations showed ANN has higher accuracy than GLM-AED. Lastly, the established ANN model was used to predict the future 10-year water quality of WLE, indicating that moderate adverse health effects would occur in the next decade. Particularly, with the lack of inputs from 2016 to 2020, we can still apply trained ANN to predict future water quality conditions, while it is impossible to apply GLM-AED with a lack of continuous input data. Furthermore, the specialized knowledge requirements in the ANN model application are less than the process-based GLM-AED model. Therefore, the ANN model in our study could be a potential tool replacing process-based models for informing long-term future water quality changes in WLE. Conceptualization, Q.W. and S.W. ; methodology, Q.W. and S.W. ; software, Q.W. and S.W. ; validation, Q.W. and S.W. ; formal analysis, Q.W. and S.W. ; investigation, Q.W. ; resources, Q.W. ; data curation, Q.W. ; writing--original draft preparation, Q.W. ; writing--review and editing, S.W. ; visualization, Q.W. ; supervision, S.W. ; project administration, S.W. ; funding acquisition, S.W. Both authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Relative Probability of Acute Health Effects** & **Low** & **Moderate** & **High** & **Very High** \\ \hline Chlorophyll a (\(\upmu\)g L\({}^{-1}\)) & \textless{}10 & 10–50 & 50–5000 & \textgreater{}5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: WHO Recreational Guidance for Chlorophyll a. **Funding:** This research is partially supported by the Natural Sciences and Engineering Research Council of Canada. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. **Appendix A**  
**Table A1. Summary of Water Quality Data Sources of the Detroit River. **
# Impact of Heat Exchanger Effectiveness and EGR on Energy and Emission Performance of a CI Engine
## 3 Results and Discussion
### Investigation of Engine Energy Indicators  
The conducted studies have shown that the EGR system has a negative impact on both brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The use of EGR increases BSFC by 2.0-2.8% (Figure 15a), as the recirculated CO2 and reduced excess air ratio lower the combustion temperature (Figure 13) and quality, thereby decreasing the thermal efficiency of the engine. Although combustion slows down, leading to a rise in exhaust gas temperature (Figure 15a), which in turn results in greater heat energy losses. These findings indicate that the EGR system is an effective tool for reducing combustion temperature, but it does not contribute to lowering fuel consumption [45]. The increase in fuel consumption represents a trade-off between NOx reduction and thermal efficiency. Improved cooling of the intake air has a positive effect, reducing BSFC by up to 2.5% when the EGR system is deactivated (from 299.8 g/kWh to 292.3 g/kWh at BMEP = 0.6 MPa). A comprehensive evaluation of the combined use of EGR and HEE has shown that, when EGR is active and 100% HEE is applied, fuel consumption remains largely unchanged compared to the case with EGR off and HEE at 0%.  
In the absence of EGR, increasing HEE to 50% and 100% results in a greater intake air mass, which in turn reduces both the combustion temperature (Figure 7) and exhaust gas temperature (Figure 9b). This leads to lower heat losses and an improvement in  
Figure 14: In-cylinder temperature rise during combustion: (a) EGR off; (b) EGR on.  
Figure 15: Effect of engine load and heat exchanger effectiveness on engine energy performance indicators: (a) Brake specific fuel consumption; (b) Brake thermal efficiency.  
BTE of up to 2.5% (from 0.338 to 0.347 at BMEP = 0.6 MPa) (Figure 15b). This indicates that the heat exchanger has a significant impact on engine performance, although potential trade-offs between thermal efficiency and pollutant emissions should be taken into account [46; 47]. Higher engine loads (0.6 MPa) result in increased BTE and simultaneously reduced BSFC, particularly under more intensive HEE operation. This suggests that higher load is associated with greater energy demand, which requires more fuel, but also increases the mechanical efficiency of the engine, thereby enabling improved BTE [46; 47]. However, when EGR is applied, BTE tends to decrease, although high-intensity HEE helps to maintain BTE at a nearly unchanged level. The research results indicate that optimal engine efficiency can be achieved by balancing engine load, EGR operation, and HEE intensity; however, the engine's environmental performance indicators must also be taken into account.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 5 Discussion
### Long-Term Operational Capability  
The stability of ML has been tested in many other applied cases and is considered trustworthy. In this study, we sought to operationalize the trained model to estimate routine emissions of all power plants as a third-party information source to help regulators to formalize and enforce environment policy. The trained model should be deployed as a service to take the newly observed TROPOMI and other input variables and output a predicted value for each power plant NO\({}_{2}\) emission every day. The near-real-time TROPOMI products are normally ready with an average time lag of three hours after acquisition. The computation of converting format and projection, extracting values, triggering the model, and outputting the values normally takes less than ten minutes. Theoretically, the approach can refresh the NO\({}_{2}\) estimate of the power plants within less than one day as long as TROPOMI captures a valid value on that day for the power plant location. There will be gaps due to clouds and maintenance of the satellite, which should be notified to the stakeholders via email or network platforms. Gaps of one or two days should have trivial impacts on the regular enforcement of the environmental policy. However, long gaps will have impacts which might, unfortunately, create opportunities for big emitters to violate the rules. This is an existing problem and will be further discussed in the future. Potential solutions include developing NO\({}_{2}\) products from more reliable spectral bands that can spear through the clouds or to use third-party data sources, such as on-demand aerial-borne observations. In other words, once satellites fly over the power plants on a clear-sky day, the ML-derived NO\({}_{2}\) emission estimates will be calculated and contained in a report and sent to environment enforcement agencies within as little as four hours.
# Leveraging Artificial Intelligence in Blockchain-Based E-Health for Safer Decision Making Framework
## 5 Results and Discussion
### Smart Contract Performance  
This paper employs Blockchain technology through smart contracts. This subsection measures the performance and cost of such employment.  
Table 5 shows details of the deployed smart contracts, including the deployment cost in gas. ML models of SVM and MLP were separately implemented to evaluate their performance better. Table 4 shows that the SVM contract has the most expensive deployment.  
There was no clear way to measure the performance of smart contract functions regarding the CPU performance of the EVM. For this case, this study chooses to follow the judgment of each procedure's performance based on the elapsed time taken to complete each operation.  
Table 6 shows the elapsed time for each read and write operation in the proposed methodology. It is worth noting that these measurements were taken on a local Blockchain network, with each transaction mined instantly to avoid any additional time latency.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline SVM & **0.81** & 0.86 & 0.72 & 0.74 \\ MLP & 0.71 & 0.67 & 0.61 & 0.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: ML Classifiers Performance.  
Figure 3: ROC curve for SVM and MLP classifiers.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Smart Contract** & **Language** & **Gas Cost (gwei)** \\ \hline Dataset Handler & Solidity & 357,364 \\ SVM Model Handler & Vyper & 3,320,656 \\ MLP Model Handler & Vyper & 1,046,866 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Smart Contract Deployment.  
The elapsed time analysis showed that the write operation with smart contract setter functions has low time efficiency than the better functions. The longest time was to set SVM's support vectors since writing a single support vector takes about two seconds to complete.  
As mentioned before, the smart contract did not allow for the direct passing of decimal vectors; vectors were sent as integers and then converted back to decimals. These additional conversion steps could be the reason for the setting support vector procedure's low time efficiency; a more thorough CPU analysis could determine the cause for such latency.
# Modeling Population Dynamics and Assessing Ecological Impacts of Lampreys via Sex Ratio Regulation
## 5 Sex Ratio Impact on Ecosystem Stability Model (SRIES Model)
### Symbiotic Sex Ratio Dynamics Model (SSRDM)  
The objective of this model is to analyze how changes in sex ratios within lamprey populations affect the dynamics of related populations in an ecosystem under symbiotic relationships. The model takes into account the interactions between the two populations and introduces a logistic growth function to simulate the self-limiting properties of population growth [16; 17].  
Growth modeling of population 1:  
\[\frac{dx_{1}}{dt}=r_{1}x_{1}\bigg{(}1-\frac{x_{1}}{N_{1}}+a_{1}\frac{x_{2}}{N_{ 2}}\bigg{)} \tag{30}\]  
where \(x_{1}\) and \(x_{2}\) are the numbers of the two populations, \(r_{1}\) is the natural growth rate of population 1, \(N_{1}\) and \(N_{2}\) are the environmental carrying capacities of population 1 and population 2, respectively, and \(a_{1}\) is the coefficient of influence of population 2 on the growth of population 1.  
Growth model for population 2:  
\[\frac{dx_{2}}{dt}=r_{2}x_{2}\bigg{(}1-a_{2}\frac{x_{1}}{N_{1}}-\frac{x_{2}}{N_ {2}}\bigg{)} \tag{31}\]  
where \(r_{2}\) is the natural growth rate of population 2 and \(a_{2}\) is the coefficient of influence of population 1 on the growth of population 2.
## 4 Results and Analysis
### Simulation Results of Arch Culvert under Periodic Vehicle Load
#### 4.2.1 Deformation of Steel Corrugated Pipe
The sedimentation from SP1 to SP5 decreased gradually. The settlement rate began to show an inflection point after a short period of linear increase from PB0 to PB300 and tended to zero at PB300. With a maximum settlement of \(-4\) mm for SP1 and a minimum settlement of \(-2\) mm for SP5, the settlement during this period was stable. Figure 23b shows that when the structure first exhibits a minor right-wing movement between PB0 and PB40, that is, the structure begins to squeeze the soil towards one side. After PB40, the structure starts to exhibit a left-wing deformation. For PB0-PB300, the lateral displacement increased linearly, and subsequently, the settlement rate began to decrease. The functions and curves obtained from the curve fitting of SP1 and SP5 (starting from PB40) are shown in Figure 24. The lateral deformation of SP1 and SP5 is predicted to be stable at PB1200 and PB1500, approximately \(-7\) mm. Figure 23: Deformation curve of the arch’s shoulder (upper): (**a**) Settlement; (**b**) Lateral displacement. Figure 22: Vault settlement curve: (**a**) Numerical simulation results; (**b**) Fitting results. Figure 25 shows the settlement and lateral displacement of the arch's lower shoulder (the points of SP6 to SP10). As shown in Figure 25a, from SP6 to SP10, the uplift and uplift rates increased gradually. Due to the presence of the deformation transition points ST1 and ST2, when the vault sinks, the arch shoulder will experience uplift. From PB0 to PB50, the uplift increased linearly. Subsequently, the arching rate gradually decreased, with a maximum arch of 6 mm for SP6 and a minimum arch of 4 mm for SP10. As shown in Figure 25b, due to the short distance between SP1 and SP5, and SP6-SP10, the latter first moved towards the vacant surface of the structure and then began to deform towards the backfill soil, resulting in soil compression. The deformation convergence trend shown in Figure 25 indicates that the stable deformation of the arching and lateral displacement is predicted to be small.
## 4 Analytical Framework for Heterogeneous Service Provisioning
### Problem Overview and Healthcare Service Requirements
#### 4.1.5 Traffic and Demand Prediction  
In smart healthcare IoT systems, traffic patterns can vary due to unpredictable patient mobility, device behavior, and monitoring intensity. To anticipate such variations, we employ an LSTM neural network trained on historical service request traces and real-time link load data. The LSTM model forecasts traffic demand on each communication link over a short-term horizon, yielding predicted traffic levels \(\delta_{s}^{t}\). These forecasts are used to proactively update pricing coefficients \(p^{t}\) and inform adaptive scheduling before congestion occurs.
###### Abstract
Thus, the point cloud density used for 3D model generation should preferably reflect the same order of roughness. However, the current laser surveying instruments record measurements from distances of several tens of meters or more and provide point cloud information spaced in units of millimeters (Figure 1). Also, the smallest unit of coordinate information is generally millimeters. There are surveying instruments that can identify roughness in the order of microns, but these are for surveying very small areas or samples that can be placed on top of the instrument and cannot be used to survey large structures (such as bridges). To solve this problem, we focused on a method to generate pseudo-dense point cloud information using sparse point cloud information in millimeter units. One such method is densification using distance images. There have been studies on methods to generate dense point cloud information from sparse point cloud information [14], but we consider these methods to be still in the process of research. We thought that there was a possibility to obtain dense point cloud information by converting the point cloud information into a distance image and then converting the result of the distance image into the point cloud information. In this paper, we propose a method of generating pseudo-dense information from sparse information using deep learning. Additionally, this paper describes a method for generating pseudo-information for shooting point distance information, which is information used for calculating point cloud information. Herein, distance information was acquired using Light Detection and Ranging (LiDAR). In principle, LiDAR conducts laser irradiation to calculate the distance between the measuring instrument and the target based on the time required by light to return from the target after reflection. Our proposed method utilized generative adversarial networks (GANs) [15], i.e., a generative model, to generate dense distance information from sparse distance information. A generative model outputs data similar to those used when training a model. As shown in Figure 1, point cloud data acquired by LiDAR are sparse. When distance information is calculated from these, point cloud information is generated into an image, and a low-resolution image is generated. One of the existing GAN-based technologies is a super-resolution technology, which generates pseudo-high-resolution images based on low-resolution images. By generating a high-resolution distance information image from a low-resolution distance information image using super-resolution technology, it is considered possible to obtain pseudo-dense distance information (Figure 2). Based on the pseudo-distance information obtained by super-resolution, it is thought that dense point cloud information can be computed. In this paper, we propose and evaluate a method for generating dense distance images from sparse distance images by applying this super-resolution technique.
# CT-Video Matching for Retrograde Intrarenal Surgery Based on Depth Prediction and Style Transfer
## 2 Materials and Methods
### Depth Prediction
#### 2.1.1 Datasets  
In this paper, the SI-DM dataset was like the RGB-D dataset [18; 19; 20]. It was generated from preoperative CT image sequences. The whole process is depicted in Figure 2. (1) We segmented CT images according to grey-scale thresholding and extracted interested anatomical sites; (2) We used the Marching Cubes (MC) [21] algorithm to reconstruct the interested anatomical sites for the kidney 3D model; (3) We extracted the center path of the kidney 3D model and used a virtual camera to simulate the movement path of a real ureteroscope; (4) We obtained pairwise SI-DM dataset by using the 3D object rendering imaging principle.  
Figure 1: Flowchart of our proposed method.  
We collected CT images for 3D model reconstructions from two patients at Shanghai Changhai Hospital. A Philips CT scanner was used with 1.5 mm scan thickness and 512 \(*\) 512 image resolution. In generating the synthetic data, we considered the points on the center path as the virtual camera's location and rotated the virtual camera at different angles at each interval point in order to get more multi-angle virtual endoscopic images in the virtual endoscope. Here, the virtual camera can perform the functions of an RGB-D camera and can collect the depth map corresponding to each frame of the virtual image. As shown in Table 1, we generated 29,608 SI-DM images, of which 21,429 were training sets and 8179 were test sets.
## 2 Materials and Methods
### Design Phase
#### 2.2.3 Dataset  
For implementing and training the selector, this study considered benchmark instances from the literature for the JSSP and FJSSP. This was for several reasons. Firstly, the instances were publicly available, which ensured transparency and reproducibility. Secondly, several papers made use of these instances, which facilitated an overall comparison more easily. Thirdly, the commonly used benchmark instances cover a wide range of problem sizes, from small instances of six jobs and six machines up to one hundred jobs with twenty machines. The data type of both instance sets was semi-structured. There was an internal structure but not one that was suitable for a machine learning process. The data did not provide any direct features; they had to be designed. The data was organized into a schema with patterns and logic, which made the data usable for further processing. The JSSP instances were taken from the instance collection of [44] ([[http://jobshop.jivh.nl/](http://jobshop.jivh.nl/)]([http://jobshop.jivh.nl/](http://jobshop.jivh.nl/)), accessed on 10 December 2022) (Table 1).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Source** & **Abbreviation** & **Number of Instances** & **Number of Jobs** & **Number of AGVs** \\ \hline [PERSON] [45] & ft & 3 & 6, 10, 20 & 5, 6, 10 \\ [PERSON] [46] & la & 40 & 10, 15, 20, 30 & 5, 10, 15 \\ [PERSON] et al. [47] & abz & 5 & 10, 20 & 10, 15 \\ [PERSON] and [PERSON] [48] & orb & 10 & 10 & 10 \\ [PERSON] et al. [49] & swv & 20 & 20, 50 & 10, 15 \\ [PERSON] and [PERSON] [50] & yn & 4 & 20 & 20 \\ [PERSON] [51] & ta & 80 & 15, 20, 30, 50, 100 & 15, 20 \\ [PERSON] et al. [52] & dmu & 80 & 20, 30, 40, 50 & 15, 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the JSSP literature benchmark instance collection.  
For the FJSSP, there were also various benchmark instances in the literature. The most common instances were from [53; 54], who transformed the JSSP instances of [45; 46]. Table 2 lists all available benchmark instances for the FJSSP and their characteristics.  
To obtain a deeper understanding of the number of jobs and AGVs among the instances, Figure 3 displays the distribution of instance size regarding the number of jobs and AGVs over the entire dataset. The instances cover the problem space up to larger instances, but the majority of instances are in the area of 50 jobs x 20 AGVs for JSSP and up to 20 jobs x 10 AGVs for FJSSP.
## 3 Results and Discussion  
### Structural and Magnetic Characterization of Co-Based MWs  
Figure 2a shows a SEM image, where the MW microstructure that is composed of a metallic core and the outermost Pyrex layer is clearly identified. In turn, the XRD pattern (Figure 2b) displays broad amorphous shoulders located one below 30\({}^{\circ}\), and two around \(\sim\)45\({}^{\circ}\) and \(\sim\)82\({}^{\circ}\). The first contribution corresponds to the Pyrex XRD signature (red asterisk) [37] and the other contributions (black asterisks) are associated with the amorphous Co-based alloys [50]. Regarding the magnetic properties, a high saturation magnetization of \(\sim\)81 emu/g (that is normalized to the magnetic mass) and an extremely low coercivity around \(\sim\)0.2 Oe (Figure 2c) are shown. Before saturation condition, the magnetization exhibits a linear dependence on the axially applied magnetic field, as can be expected from its circumferential anisotropy. In this context, the present amorphous characteristics confers a magnetic domain structure that is determined by the magnetoelastic anisotropy arising from the coupling of the negative magnetostriction characteristic of the Co(Fe)-based composition and the mechanical stresses that formed during the manufacturing process maintained by the external cover of Pyrex [50].  
Figure 1: Schematic representation of the microwave scattering mechanism in transmission mode for sensing array platforms composed by Fe\({}_{2.25}\)Co\({}_{27.25}\)Si\({}_{10}\)B\({}_{15}\) (lengths of a few cm). Two Helmholtz coils apply axial magnetic fields to induce large changes in the magnetic state of the Co-based microwires (MWs) and then, giant magneto-impedance (GMI) effects arises. The \(S_{21}\) transmission coefficient is obtained in the (**i**) frequency and (**ii**) time domains.