esa-sceva/satcom-chunk-collection huggingface dataset:
# Grain Orientation and Hardness in the Graded Interlayer of Plasma Sprayed W on CuCrZr
## 4 Conclusions  
W coating was deposited by means of PS in an Ar-H\({}_{2}\) atmosphere on a substrate of CuCrZr, with an interlayer consisting of gradually changing fractions of Cu and W. The W coating and the interlayer showed good adhesion and no cracks were observed. The work investigated the hardness of the two metals along the interlayer through nano-indentation tests in correlation with their microstructural characteristics. The results can be summarized as follows.  
1. Both metals exhibit a hardness gradient in the interlayer: hardness of Cu is nearly constant (2.2 \(\pm\) 0.2 GPa) up to a distance of \(\sim\)800 \(\upmu\)m from the substrate; it then increases until it reaches a value of 3.9 \(\pm\) 0.2 GPa near the coating, while that of W continuously increases from 3.8 \(\pm\) 0.3 GPa near the substrate up to 9.9 \(\pm\) 0.1 GPa on the opposite side.
2. The grain morphology and size of W and Cu do not change substantially along the interlayer; the dislocation density is constant in Cu, while it slightly decreases in W near the coating.
3. The texture of both W and Cu exhibits significant differences, depending on the specific position in the interlayer.
4. The preferred orientation of crystalline grains, which is the result of a process of repeated nucleation and growth occurring when droplets impact the surface and solidify, is the key factor explaining the observed hardness gradients. The multiplication and movement of dislocations, and localized plasticity under the indenting tip, are strongly affected by grain orientation.  
Conceptualization, E.P., R.M. and M.C.; methodology, E.P., A.F., A.V., C.P. and M.R.; formal analysis, R.M., M.R. and A.V.; supervision, R.M. and M.C.; draft paper preparation, E.P. and R.M.; final paper preparation, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors are grateful to [PERSON] of Department of Industrial Engineering--University of Rome \"Tor Vergata\" for the assistance in sample preparation.  
The authors declare no conflict of interest.
## 2 Experimental Apparatus and Methods
### The Corona-Stabilized Switch
#### 2.1.1 Inhomogeneity of Electric Field  
The inhomogeneity of the electric field in a corona-stabilized switch determines the distribution of the electric field in the gas gap and affects both the position and intensity of the corona discharge activity. This is crucial to achieving corona stabilization and is a core parameter in designing such switches. To investigate the influence of inhomogeneous electric fields, high-voltage electrodes (R group) with different rod radii but the same length (50.0 mm) were employed to obtain varying inhomogeneity coefficients of the electric fields. To further investigate the impact of the spatial electric field distribution on corona stabilization, a high-voltage electrode with a sharp (group N) structure was designed, as the inhomogeneity coefficient of an electric field can only reflect the peak property of the electric field strength in the gap. A schematic diagram depicting the structure of the rod electrode and needle electrode is presented in Figure 3. The precise geometric dimensions of the rod electrode and needle electrode are delineated in Tables 1 and 2, respectively. The field distribution for different electrodes was simulated using commercial finite element analysis software, and the inhomogeneity coefficient of the electric field \(f=E_{\max}/E_{\mathrm{av}}\) was calculated. \(E_{\max}\) represents the maximum electric field intensity in the electrode gap  
Figure 1: Schematic diagram of the experimental setup.  
Figure 2: Schematic diagram of the corona-stabilized switch.  
obtained through simulation analysis, while \(\mathbf{E}_{\text{av}}=U/s\) represents the average electric field intensity in the gap (\(U\) represents the applied voltage and \(s\) represents the gap spacing).
## 2 Materials and Methods
### Beta-Defensin Analysis  
Commercial ELISA kits were used to determine the concentrations of human beta-defensins 1-3 (Peprotech, catalog numbers 900-M202, 900-M172, 900-M210). Standards for hBDs 1-3 were provided in the ELISA kit. The analyses were performed according to the manufacturer's instructions with the exception that samples and standards were assayed in duplicates, not triplicates, due to the low volume of the samples. A 1:30 dilution was used for the hBD-1 analysis, and hBD-2 and hBD-3 were analyzed from undiluted samples. The absorbances of the samples were read at 405 nm wavelength with a Multiskan FC Microplate photometer (Thermo Fisher Scientific catalog number 51119000), and the data were treated with SkanIt software V.3.1.0.4 (Thermo Fisher Scientific catalog number 5187139). Readings were obtained at 5-min intervals as instructed by the manufacturer, and readings from 25 min were chosen for further analysis. In order to account for any levels of hBD-1, hBD-2, and hBD-3 that were below the limit of detection (LOD) of the assay, values under the LOD were substituted with half of the LOD of each ELISA kit [37]. Specifically, hBD-1 levels below the LOD were substituted with 2 pg/mL, hBD-2 levels below the LOD with 8 pg/mL, and hBD-3 levels below the LOD with 31 pg/mL.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 4 Preliminary Performance Evaluation
### Issues to be Addressed
#### 4.2.2 Chances of Measuring RTT  
Weak RTT estimation is useful when there are few chances to estimate RTT without taking sample RTTs using retransmitted segments. Figure 3 illustrates the cases where most of the TCP segments after connection establishment were retransmitted. In particular, Figure 3a shows that there were only a couple of RTT measurements which exceeded 3 s. This indicates that utilizing weak RTT estimation might have been able to reduce unnecessary retransmissions.
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 5 Model Prediction Performance Verification Results  
This study aimed to develop an optimal model to address the missing traffic volume data by comparing and analyzing statistical learning, machine learning, and deep learning models. The SARIMA model was employed to account for seasonality in the traffic volume data, the Prophet model was used for univariate predictions specializing in time series estimation, such as traffic volume, and the LSTM model was utilized to address long-term trends and uncertainties.  
Table 7 presents the prediction results based on MAE for the analyzed statistical learning, machine learning, and deep learning models. The verification results indicated that the deep learning models performed the best in predicting traffic volume, followed by machine learning models and statistical learning models. Notably, the LSTM model in deep learning exhibited a higher MAE than the Prophet model in machine learning.  
To address this problem, this study proposed the power-function-based LSTM model. This modified LSTM model, utilizing the developed power function, achieved superior performance in correcting and predicting traffic volume compared to the existing models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Model** & **MAE** \\ \hline Statistical learning & SARIMA & 0.158 \\ \hline Machine learning & Prophet & 0.117 \\ \hline \multirow{2}{*}{Deep learning} & LSTM & 0.128 \\ \cline{2-3}  & Power-function-based LSTM model & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model prediction performance verification results.  
Figure 10: LSTM model’s prediction results (point 10001, 50% data missing).  
Table 8 compares the error rates among the statistical learning, machine learning, and deep learning models, revealing that the power-function-based LSTM model demonstrated lower errors when compared to SARIMA (84.18%), Prophet (78.63%), and LSTM (80.47%). The power-function-based LSTM model recorded an MAE of 0.025, indicating the best performance among all evaluated models.  
To further verify the prediction accuracy of the proposed power-function-based LSTM model, the ADT at point 10001 was assumed to have a 50% missing rate when compared to the actual values. The verification results demonstrated a difference of approximately 24 vehicles/day using the existing methods, with an error rate of 0.164%. The power-function-based LSTM model exhibited a difference of approximately four vehicles/day, with an error rate of 0.025%.  
The accuracy verification for the AADT indicated a difference of 20 vehicles/day and 8760 vehicles/year, demonstrating that the proposed model significantly outperformed existing methods in correcting traffic volume based on actual values. Table 9 presents the accuracy verification results of the power-function-based LSTM model.
## 2 Materials and Methods
### Datasets Used in the Study
#### 2.1.1 Global Land Mapping XCO\({}_{2}\) Data Derived from GOSAT Retrievals  
Global land mapping dataset of XCO\({}_{2}\), spanning from 1 June 2009 to 31 May 2014, is generated by filling gaps of ACOS-GOSAT Level 2 XCO\({}_{2}\) retrievals (v3.5), downloaded from the NASA data portal [32], using the mapping approach developed by [PERSON] et al. [19; 20] based on spatio-temporal geostatistics. A detail description and accuracy assessment of the global mapping method can be found in [PERSON] et al. [20]. Unlike [PERSON] et al. [20], in this study, when modeling the spatio-temporal trend during the mapping process, we use the original ACOS-GOSAT XCO\({}_{2}\) data instead of model simulations. In this way, the method makes full use of information inside the data and is therefore a completely data-driven mapping approach. The resulted mapping XCO\({}_{2}\) data, GM-XCO\({}_{2}\), are gridded dataset and regular in space and time with spatial resolution of 1\({}^{\circ}\) by 1\({}^{\circ}\) and temporal resolution of 3 days. To screen out data with high uncertainty, mapping data with prediction standard deviation [19] \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Resolution Spatial Temporal** & **Description** & **Reference** \\ \hline Global mapping & \multirow{2}{*}{\(1\times 1\) degree} & \multirow{2}{*}{3 day} & Global land mapping of XCO\({}_{2}\) & \multirow{2}{*}{[PERSON] et al. [20]; approach using GOSAT XCO\({}_{2}\) (v 3.5)} & \\  & & & retrievals from ACOS project team. & \\ \hline Simulated XCO\({}_{2}\) & \multirow{2}{*}{2 \(\times\) 3 degree} & \multirow{2}{*}{3 day} & Global simulations of XCO\({}_{2}\) derived from multi-layer CO\({}_{2}\) data simulated by CarbonTracker CT2015 & \\ \hline NDVI & \multirow{2}{*}{0.05 degree} & \multirow{2}{*}{monthly} & Normalized Difference Vegetation & \multirow{2}{*}{[PERSON] et al. [28]} \\  & & Index (NDVI) from MOD09 & & \\  & & & reflectance product (MYD13C2) & \\ \hline EVI & \multirow{2}{*}{0.05 degree} & \multirow{2}{*}{monthly} & Enhanced Vegetation Index & \multirow{2}{*}{[PERSON] et al. [28]} \\  & & & (EVI) from MOD09 reflectance & \\  & & & product (MYD13C2) & \\ \hline GPP & \multirow{2}{*}{1 km} & \multirow{2}{*}{monthly} & Gross Primary Productivity & \multirow{2}{*}{[PERSON] et al. [29]} \\  & & & (GPP) from MODIS data & \\  & & & product (MOD17A2) & \\ \hline LAI & \multirow{2}{*}{1 km} & \multirow{2}{*}{8 days} & Leaf Area Index (LAI) from MODIS & \multirow{2}{*}{[PERSON] et al. [30]} \\  & & & data product (MCD15A2) & \\ \hline LST & \multirow{2}{*}{0.05 degree} & \multirow{2}{*}{monthly} & Land Surface Temperature (LST) from MODIS data product (MOD11C3) & \\ \hline \end{tabular}
\end{table} 
Table 1: Specifications of the used satellite observations from GOSAT and MODIS and model simulations from CarbonTracker spanning from 1 June 2009 to 31 May 2014.
## 4 Problem characterization and Abstraction
### User Survey on HMI Design  
The development of an effective Human-Machine Interface (HMI) requires a detailed understanding of user needs and preferences [78]. To achieve this, a comprehensive user survey was conducted to gain insights into interaction patterns, user experiences, and design expectations. The findings of this survey serve as a foundation for optimizing the HMI design to ensure intuitive usability while simultaneously fulfilling the technical requirements of the system. The survey was structured to generate a robust data set that would inform the user-centered development of the interface. This approach ensures that the resulting HMI is not only technically functional but also highly tailored to end-user requirements.  
Methodology  
The survey targeted operators, supervisors, and support staff who regularly interact with the HMI. The diversity of participants was essential in capturing a broad spectrum of usage scenarios and experiences, ensuring that the interface is accessible and functional across different roles.  
Due to the lack of an online survey infrastructure, the survey was conducted using pre-printed forms. The questionnaire was designed to be simple and easy to complete, allowing participation even for users with limited technical expertise. A combination of closed-ended and open-ended questions was used to collect both quantifiable data and qualitative insights into user preferences and experiences.  
To maximize participation, the survey forms were distributed directly at the workplace during working hours, ensuring a high response rate. A designated collection box facilitated the return of completed forms, making the process convenient for participants. Given the relatively small number of respondents, the survey results were manually analyzed. Closed-ended responses were quantitatively evaluated, while open-ended responses were thematically analyzed to extract deeper insights into specific user requirements.
# Thermal Characteristics and Parametric Analysis of an Improved Solar Wall
## 5 Conclusions  
To solve the problem of inconvenient manual control in the process of operation, the optimized color-changing solar wall with rotatable shutters and automatic control components was proposed in this paper. A quantitative study was conducted for improving the thermal performance of solar walls. Conclusions were drawn as follows.  
* Based on the monitoring, experimental data showed that the average daily heat output of the color-changing solar wall is 1.08 MJ per unit floor area on clear days in winter and the average thermal efficiency of the solar wall is 56.8%. Furthermore, the  
Figure 16: Effect of vent size on volume flow and heat gain of the solar wall.  
Figure 15: Effect of shape ratio on outlet temperature and heat gain of the solar wall.  
thermal characteristic of the automatic control components was also carried out by field monitoring and theoretical analysis.
* Meanwhile, to improve the thermal performance of the solar walls system, thermal characteristic optimization analysis of the solar wall was also conducted based on the mathematical model. The structure dimensions and absorber materials were optimized. Optimization results showed that an optimal aspect ratio is between 2 and 3 and the ideal diameter of the vent is about 200 mm.  
The improved solar wall with color-changing absorber board and automatic control components proposed in this paper had a positive effect on reducing building energy consumption in cold areas, which presented the theoretical basis and technical support for renewable energy utilization.  
Conceptualization, X.Z. and J.Z.; methodology, X.Z. and J.Z.; software, X.Z.; formal analysis, J.Z., W.L. and R.L.; investigation, R.L. and W.L.; data curation, B.C.; writing--original draft preparation, X.Z.; writing--review and editing, J.Z.; visualization, J.Z.; supervision, X.Z.; project administration, R.L. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51808506) and Key Specialized Research and Promotion Special Project (Science and Technology Targeted) in Henan province (No. 212102310573).  
Not applicable.  
Data Availability Statement: Data available on request.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Results and Discussion  
This literature review comprehensively addresses academic studies conducted over the past 10 years on carbon footprint, manufacturing processes, energy efficiency, supply chain management, and Industry 4.0. The studies highlight the growing interest in carbon footprint reduction strategies and the increase in publications in this area. The U.S. and China stand out as leading contributors to research on carbon footprint and manufacturing processes, making significant contributions to the literature. One of the most commonly used methodologies in the studies is LCA, which plays a critical role in managing the environmental impacts of manufacturing processes.  
Studies on Supply Chain Management and Carbon Footprint have revealed that green supply chain management can reduce greenhouse gas emissions by 51% to 72%. However, challenges such as data inconsistencies and access difficulties across different sectors persist. Additionally, the role of ERP systems in carbon management and reporting processes presents a critical challenge, particularly regarding data integration and customization issues.  
Industry 4.0 technologies have the potential to reduce the carbon footprint by contributing to energy efficiency, waste reduction, and production efficiency. However, challenges such as data security and interoperability, which hinder the full integration of these technologies, remain.  
The carbon footprint management depicted in the graph is based on three main components: ERP Systems, Industry 4.0, and Sustainability. ERP systems help businesses manage carbon emissions and optimize energy consumption, while Industry 4.0 technologies enable more efficient use of resources and reduce carbon emissions. Sustainability strategies, on the other hand, minimize the environmental impact of production through methods such as LCA and promote eco-friendly processes by establishing green supply chains.  
This analysis and Figure 6 demonstrate that the approaches and tools used to achieve carbon footprint management and sustainability goals are addressed from a holistic per  
Figure 5: Carbon Footprint Reduction and Energy Efficiency in Manufacturing: A Systematic Review of Research Trends and Methodologies.  
spective. These approaches not only enhance environmental sustainability but also support operational efficiency.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 4 Results and Validation
### Validation with Ground-Based PM10 Measurements  
#### 4.2.1 Results of the Validation of Four Algorithms  
Four dust detection algorithms (BTD, NDDI, IDDI, and DST) were verified using PM10 concentration data at a 2 h interval from 02:00 to 08:00 UTC in spring 2021, and the results are shown in Tables 3-6. The mean POCD for the four algorithms was 56.15%, 39.39%, 48.22%, and 46.75%, respectively. The mean POFD was 74.22%, 95.07%, 74.19%, and 80.49%, respectively. Overall, the BTD had the highest agreement with PM measurements  
Figure 4: FY-4A AGRI true color images (**a,g,m,s)**, PM10 (**b,h,n,t)**, BTD (**c,i,o,u)**, NDDI (**d,j,p,v)**, IDDI (**e,k,q,w**), and DST (**f,l,r,x**) corresponding to the times of 16 March 2021 from 02:00 to 08:00 UTC, respectively.  
with a smaller PoFD. The NDDI had the highest average PoFD and lowest POCD. The small number of matches for IDDI and DST was due to their inability to discern dust at nighttime and to detect dust in the west and east at 02:00 and 08:00 UTC, respectively.
## 2 Materials and Methods
### Problem Statement  
Hydrocarbon supply chain (HCSC) is a complex and long network extending from the production of crude oil and natural gas to marketing the oil and gas products. The entities along the HCSC constitute oil and gas fields, oil and gas separation plants (GOSPs), primary storage facilities, oil and gas processing plants, refinery plants, fractionation plants, secondary storage facilities, and demand nodes. The crude oil from oil fields and/or GOSPs is pumped to oil processing plants for removing sulfur. After removing the sulfur, the oil is shipped to international and local terminals. Locally, the crude oil is further transformed in refinery plants into its oil by-products (i.e., diesel, naphtha, gasoline, Liquefied Petroleum Gas (LPG), fuel oil, asphalt, and kerosene). The oil by-products are then used to satisfy international and local demands. The associated natural gas produced at gas-oil separation plants (GOSPs) and the non-associated natural gas produced directly from pure gas fields are streamed to gas plants in which they are processed into natural gas liquid (NGL), methane and hydrogen sulfide. The methane is used as a raw material by industry, and the NGL is fractionated in fractionation plants into its gas products (i.e., ethane, propane, butane, and natural gasoline) used to satisfy the local and international demand. A general representation of the entities of the HCSC are shown in Figure 1.  
In the real-world, HCSC plants might be totally or partially unavailable due to periodic maintenance and sudden shutdown. Thus, their production capacities will be reduced or reach zero. To restore  
Figure 1: Schematic representation of downstream oil and gas supply chain.  
these plants to their full capacities and keep them in good condition, plants should be maintained regularly and routinely to prevent unplanned failures, increase plant production rates, and prolong their life. Thus, operation planning models need to be more realistic and should consider maintenance scheduling. The maintenance cycle and duration of petroleum plants such as oil processing plants, refinery plants, gas plants, and fractionation plants must be identified before model formulation.  
The aim of the proposed MIP model is to coordinate the operation and maintenance activities of the HCSC plant network, taking into account the best use of available manpower and maintenance resources, while satisfying maintenance and operational constraints. The objective of the proposed model is to maximize the HCSC profit. The model will help to determine the optimal production, inventory and transport volumes and at the same time, generate an optimal maintenance schedule. Each HCSC plant is assumed to have its own storage facility to satisfy the needs during planned and/or unexpected failures, as well as a plant shutdown. In the hydrocarbon industry, inventory is an important and expensive asset; therefore, it is important to manage the inventory level and specify their capacities precisely.