esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation of Hybrid Photovoltaic Solar Assisted Loop Heat Pipe/Heat Pump System
## 2 System Description  
The schematic diagram of the hybrid PV-SALHP/HP system is shown in Figure 1, with the configuration details of its PV/T evaporator illustrated in Figure 2. The system is mainly composed of a PV/T evaporator, compressor, water tank with immersed condenser, and a thermostatic expansion valve. The PV/T evaporator is a flat-plate solar collector covering an area of 4 m\({}^{2}\). From top to bottom, there are armored glass (0.3 mm), ethylene-vinyl acetate (EVA) film (0.35 mm) + silicon cells (0.2 mm) + EVA film (0.35 mm) + tedlar-polyester-tedlar (TPT) film (0.3 mm), aluminum sheet (0.2 mm), copper tubes, and insulating layer (20 mm). The space between each parallel copper tube is 120 mm, the inner diameter of the tubes is 9 mm, the compressor theoretical displacement is 0.4 m\({}^{3}\).h\({}^{-1}\), and R134a is used as the refrigerant.  
Such a hybrid system can operate in three modes by controlling the valves. (1) Independent HP mode: by closing valve 3 and 4, and opening valve 1 and 2, the system produces hot water only in independent HP mode. The evaporated refrigerant in the collector will be compressed at a higher temperature. After transferring extra heat into the water tank, the condensed liquid flows throw the expansion valve and is sent back to the collector at a lower temperature; (2) Independent LHP mode: by closing valve 1 and 2, and opening valve 3 and 4, the system produces hot water only in independent LHP mode. The refrigerant absorbs heat and evaporated vapor transfers heat energy into the water tank directly, then the condensed refrigerant flows back to the PV/T evaporator according to gravity; (3) Hybrid LHP/HP mode: LHP mode will first be utilized when solar radiation is strong and the temperature of the working medium in the PV/T evaporator is higher than that in the condenser. The system will be switched to HP mode when solar radiation is weak or the temperature difference of the working medium in the PV/T evaporator and the condenser cannot satisfy the condition of LHP mode. In this paper, independent HP mode and hybrid LHP/HP mode were compared.
## 5 Experiments  
The tests presented in the next sections were designed to validate the functionality of the proposed protocol in a controlled static environment, such as the inside of a home, and in an industrial environment, such as the interior of shipyard workhouses. The showcased use cases involved the exchange of messages between two nodes, with one acting as the sender and the other one as the receiver. Specifically, during these experiments, we have focused on latency measurement. The choice of latency as a priority metric is based on its direct impact on the effectiveness of critical IoT applications, where data must reach their destination within narrow time windows to trigger timely responses. In scenarios such as disaster response, the timing of data reception plays an important role in the decision-making process. In opportunistic networks that allow messages to be sent even when there is a temporary disconnection and where the arrangement of nodes changes frequently, it is crucial to minimize latency so that there is enough time for the message to reach its destination before the connection times out. For the first experiment, a total of four tests were carried out, each with 100 packets transmitted with a transmission time of 9 s between each message. The objective was to see how different locations impacted latency and the number of lost packets. All source code needed to replicate the presented experiments is available online [43], allowing any researcher to use and further extend the developed OEC system.
# Application of Lignin for Slope Bioengineering: Effect on Soil Improvement and Plant Growth
## 4 Results and Discussion
### Seed Germination and Growth  
Vegetation is an essential factor in increasing soil resistance to erosion. The vegetation root system can reinforce soil mass, preventing soil particle detachment. Fast vegetation growth contributes to effective soil stability treatment; however, the vegetation can fail due to insufficient precipitation (water access). In this study, seed germination and growth were monitored over 40 days, and the following observations were made: (1) The seed growth occurred for all types of soil and all concentrations of lignin (Figures 8 and 9). (2) Rhodes grass growth was much better than the growth of the other two types of seeds. This was the case for all types of soil and lignin concentrations. (3) Better growth was observed for Soil 1 compared to the other two types of soil. (4) Less growth was observed for Soil 2 as its surface underwent significant drying. (5) The spray method was associated with better vegetation growth for all three soils.  
To stimulate seed germination and growth, 100 mL of water was sprayed on the top of each soil twice per week. The rationale was to establish whether the seeds would germinate in lignin-treated soil and how it would be different from untreated soil under the same conditions. It is noted that daily watering and precipitation-controlled conditions would provide a better environment for seed growth [39], while less watering could result in fewer germinations [34] compared to the present study. The results indicated that considering the amount of watering in this study, the lignin-treated soils provided a suitable environment for seed germination and growth, comparable with the untreated soil. This study showed similar rates of seed germination and growth for both lignin-treated and untreated soils.  
Figure 8: _Cont._Figure 8: Seed germination and growth in Soil 1: untreated soil (**a–c**), treated with 1% lignin solution (**d–f**), and treated with 3% lignin solution (**g–i**)—10 days (**a,d,g**), 20 days (**b,e,h**), and 30 days (**c,f,i**).  
Figure 9: Seed germination and growth in Soil 3: untreated soil (**a–c**), treated with 1% lignin solution (**d–f**), and treated with 3% lignin solution (**g–i**)—10 days (**a,d,g**), 20 days (**b,e,h**), and 30 days (**c,f,i**).
## Materials and Methods
### Robocasting
#### 2.1.2 Materials  
A commercially available hydroxyapatite powder, Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\) (Captal S, batch P270S BM 192 Lot 2, Plasma Biotal, Buxton, UK) was used as the constituent phase in the pastes. Full characterizations of the powder as well as its optimum sintering condition were already published in the authors' previous paper [48]. Briefly, the powder is a calcium-deficient HA (CD-HA) with a Ca/P atomic ratio of 1.59 and a starting particle size of 2.48 \(\upmu\)m. By wet ball milling, the average particle size was reduced to 0.74 \(\upmu\)m [48] as measured by laser granulometry (Laser Particle Sizer Analysette 22; [PERSON], Idar-Oberstein, Germany).  
For the paste preparation, Darvan C-N an ammonium polymethacrylate dispersing agent (15,000 g\(\cdot\)mol\({}^{-1}\), Vanderbilt Minerals, LLC, Norwalk, CT, USA) was mixed with demineralized water followed by the dissolution of the required amount of hydroxypropyl methylcellulose (Method E4M, Dow Chemical Company, Midland, MI, USA) as a viscosifying agent. Then, ball-milled HA powder was added to the mixture in small amounts and homogenized properly after each addition. The final solid loadings of the pastes are presented in Table 1. In order to remove large agglomerates, the powder was already sieved under 80 \(\upmu\)m. Polyethylenimine (PEI, branched, Sigma Aldrich, Darmstadt, Germany) was finally added to produce the gelation of the paste. The different paste formulations used for the fabrication of the robocast parts are reported in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scaffold Type** & **Paste Code** & \
\begin{tabular}{c} **Solid Loading** \\ **(vol\%)** \\ \end{tabular} & **Dispersant (\%)** & **Method (\%)** & **PEI (\%)** &
\begin{tabular}{c} **Printing Speed** \\ **(mm/min)** \\ \end{tabular} \\ \hline \multirow{3}{*}{Lattice} & P1 & 49.2 & 4 & 6.5 & 16 & 290 \\  & P2 & 49.2 & 4 & 3.5 & 8 & 290 \\ \cline{1-1}  & P3 & 51.0 & 4 & 6.5 & 8 & 290 \\ \cline{1-1}  & P3 & 51.0 & 4 & 6.5 & 8 & 226 \\ \cline{1-1}  & P3 & 51.0 & 4 & 6.5 & 8 & 129 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different paste formulations used for the fabrication of the robocast parts.
# Seeking a Definition of Digital Twins for Construction and Infrastructure Management
## 6 Discussion of the Proposed Definitions and Use Cases
### Comparison with Other Fields  
In the aeronautics industry, DTs are extensively used for the maintenance, repair, and operation (MRO) of aircraft. The level of precision and real-time data integration observed in aeronautics is something that construction is beginning to emulate. For example, the systematic approach to data handling and predictive maintenance in aeronautics can be paralleled with the integration of IoT and real-time data management in the Bidebieta-Basauri railway station project, albeit with a focus on infrastructure health monitoring instead of aircraft.  
In the medical field, DTs enable precision medicine through simulations of human physiological conditions, which can be likened to the manner in which construction DTs simulate building conditions. The UPM's Civil Engineering School's use of DTs during the COVID-19 pandemic to manage physical distancing and safety protocols mirrors the medical industry's use of DTs to enhance patient care management and emergency response strategies.  
The application of DTs in smart cities, such as in the management of urban infrastructure in Herrenberg, Germany, involves integrating data from diverse sources to improve city planning and citizen services. Similarly, the DT-based management systems developed for railway stations and UPM demonstrate that integrating multiple data streams can significantly enhance operational efficiency and service delivery in construction and infrastructure management.  
The construction industry can draw valuable insights from these fields to accelerate the adoption and optimisation of DT technologies. This could lead to enhanced project delivery, better resource management, and improved safety and sustainability practices, paralleling the advances in other technologically advanced sectors.  
The application of DTs in construction is less mature than that in fields such as aeronautics and medicine. The successful deployment of DTs in these sectors provides a valuable roadmap for the construction industry. For example, the precision and efficiency achieved in aircraft maintenance and healthcare management through DTs can be adapted to enhance building maintenance protocols and infrastructure management, potentially revolutionising safety and operational standards.
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
## 3 Prediction of the Fire Weather Grade
### Computation of Fire Weather Grade  
The phenological correction index \(E\) in the industrial standard only considers the degree of coverage. The litter layer soil in forests contains many combustibles. The soil moisture of the litter layer has significant effects on the occurrence and spread of forest fire [26].  
In the system, the phenological correction index E is comprised of \(Y^{\prime}_{E1}\) (the degree of coverage) and \(Y^{\prime}_{E2}\) (the litter layer soil moisture index); \(Y^{\prime}_{E1}\) is obtained on the basis of the statistical empirical value, while \(Y^{\prime}_{E1}\) can be increased to 15 in the fire season and to 20 in the core fire season, and can be decreased to 0 beyond the fire season. The litter layer is comprised of a undecomposed layer, a semi-decomposed layer, and a humus layer in a top-down order. The soil moisture sensor can directly measure the volumetric water content of the humus layer and determine the size of \(Y^{\prime}_{E2}\).  
There are significant differences in the moisture content of the litter layer soil between different tree species. According to the laboratory test, the volumetric moisture content of the cork oak humus layer ranges between 4.2 and 97.6% [27]. \(Y^{\prime}_{E2}\) is set to -20 when the volumetric moisture content of the humus layer is above 30%. \(Y^{\prime}_{E2}\) is set to 0 when the volumetric moisture content of the humus layer is below 5%. \(Y^{\prime}_{E2}\) and the volumetric moisture content of the humus layer exhibit a linear relationship when the volumetric moisture content of the humus layer is between 5% and 30%. By linear fitting, we can obtain the computation function of Index \(Y^{\prime}_{E2}\):  
\[Y^{\prime}_{E2}=\left\{\begin{array}{cc}0&(X_{E2}<5\%)\\ 4-80X_{E2}&(5\%\leq X_{E2}<30\%)\\ -20&(X_{E2}\geq 30\%)\end{array}\right.. \tag{7}\]  
The air temperature, air moisture, precipitation, wind force, and the soil moisture at a depth of 10 cm obtained from the field ecological monitoring station are substituted into Equations (3)-(7) in sequence to obtain \(Y^{\prime}_{i}\). The corrected index of fire weather grade is  
\[Y^{\prime}_{A}+Y^{\prime}_{B}+Y^{\prime}_{C}+Y^{\prime}_{D}+Y^{\prime}_{E1}+Y^ {\prime}_{E2}. \tag{8}\]  
In the system, the fire weather grade is determined by Equation (8). The correspondence relationship is shown in Table 2[24].  
Figure 8 presents the grade of fire weather in the forest microclimate displayed on the PC website in real time, which is updated at an interval of 10 min in the form of a dashboard.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fire Weather Grade** & \\ \hline Grade I & \(\leq\)25 \\ Grade II & 26–50 \\ Grade III & 51–72 \\ Grade IV & 73–90 \\ Grade V & \(\geq\)91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Forest fire weather grade form.
###### Abstract  
In this paper, we obtain the intensity and phase distributions of the scattering and external fields of a vector Bessel-Gaussian vortex beam in the far-field region after being scattered by a particle. In our analysis, we use the Generalized Lorenz-Mie theory (GLMT) and the angular spectrum decomposition method (ASDM). The orbital angular momentum (OAM) spectra of the fields are analyzed by using the spiral spectrum expansion method, which is a frequently used tool for studying the propagation of vortex beams in turbulent atmospheres. Both scattered and external fields show a significant difference in spiral spectra for particles with different characteristic parameters, such as the size and complex refractive index. We also examine sampling the phase along with a circle and show that it is unable to fully express the information of the fields. This study can provide a theoretical basis for the inversion of characteristic parameters of the Bessel-Gaussian vortex beam and spherical particle by OAM spectra with applications in remote sensing engineering.  
OAM state; scattered field; polarization; spiral spectrum; Bessel-Gaussian vortex beam Article  
Particle Scattering Induced Orbital Angular Momentum Spectrum Change of Vector Bessel-Gaussian Vortex Beam
## 4 Proposed Model
### Model Validation
#### 4.4.2 Events Simulation Software Platform (ESSP)
Key metrics, such as average latency and load distribution, are recorded to evaluate the efficiency of the load balancing algorithm. By leveraging a hybrid cloud-fog architecture, the simulation aims to optimize resource allocation, reduce latency, and improve user QoE. This platform provides a robust foundation for studying dynamic load balancing techniques in highly interactive and variable gaming environments, facilitating advancements in MMO game infrastructure design.
## 3 Technologies for Treating Landfill Leachate
### Biological Treatment Processes
#### 3.3.1 Aerobic Treatment Processes  
The activated sludge process (ASP) and sequencing batch reactor (SBR) are widely used methods for aerobic treatment [135; 136; 137]. [PERSON] et al. [138] assessed the efficiency of activated sludge in treating landfill leachate, achieving a COD degradation rate of 69.9% at an initial COD concentration of 950 mg/L. [PERSON] [139] conducted a comparative study using two 3 L sequencing batch reactors (SBRs)--a granular sludge SBR (GSBR) and an activated sludge SBR (ASBR)--for young leachate treatment (TAN: 1200-1500 mg/L). The GSBR system exhibited remarkable efficiency, with 99% TAN removal and a COD reduction of 67-87% under organic loading rates of 3.2-4.8 kg COD/m\({}^{3}\)-d. In contrast, the ASBR showed inferior performance, with TAN removal fluctuating between 60-80% and COD removal of 52-83% at identical hydraulic retention times (HRT = 24 h). [PERSON] et al. [140] systematically validated the multi-pollutant removal capability of a granular sludge sequencing batch reactor (GSBR) in treating high-strength landfill leachate. Under controlled operational parameters (HRT = 24 h, DO = 2.5-3.5 mg/L, MLSS = 8500 mg/L), the system demonstrated exceptional removal efficiencies of 94% COD (initial 668 \(\pm\) 110 mg/L), 85% NH\({}_{3}\)-N (30 \(\pm\) 3.3 mg/L), and 83% PO\({}_{4}\)-P (147 \(\pm\) 18 mg/L). Figure 3 shows the process of nitrification and denitrification.  
Figure 3: Schematic diagram of nitrification and denitrification [50].
### Aim and Contributions of the Study  
The aim of this study is to develop a machine learning model that predicts hospital admissions from emergency department visits using the MIMIC-IV-ED database, with a particular emphasis on the interpretability of the model through SHAP values. The novelty of this study lies in the integration of SHAP values with a Gradient Boosting Machine (GBM) model, which not only enhances the accuracy of predictions but also provides clear, interpretable insights into the factors influencing these predictions.  
Our contributions are threefold: First, we provide a robust predictive model for hospital admissions that leverages the comprehensive MIMIC-IV-ED dataset. Second, we enhance the interpretability of machine learning predictions in healthcare by employing SHAP values, offering a transparent view of the model's decision-making process. Third, we demonstrate the practical applicability of our model in a clinical setting, addressing the need for reliable decision support tools that can alleviate the burden on healthcare providers in overcrowded EDs. Through these contributions, our study advances the field of healthcare predictive modeling by not only improving prediction accuracy but also ensuring that these models are interpretable and actionable in real-world clinical environments.  
The rest of the paper is organized as follows. The second section explores the relevant literature surrounding the application of machine learning in predicting patient out-comes, with a particular focus on emergency department settings. The third section provides a comprehensive outline of the methodology used in the current study, detailing our data sourcing, preprocessing, and analysis techniques. We specifically delve into the usage of the Gradient Boosting Machine (GBM) model and the SHAP interpretability approach. The fourth section presents the results derived from our model and the corresponding SHAP analysis. This includes both global feature importance insights and individual-level interpretations gleaned from force plots. In the subsequent discussion section, we contextualize our findings within the existing body of research, explore the practical implications of our work, consider potential limitations, and suggest avenues for future research. Finally, we conclude the paper with a summary of the key findings and their broader implications for healthcare predictive modeling and clinical decision making.
## 2 Materials and Methods
### Data Extraction  
After applying the eligibility criteria, the included articles decreased to 21. In the second phase, the identifiers of the journal and authors were removed and the articles were critically assessed by two independent authors who did not contribute to the initial search ([PERSON] and K.I.). The assessment of the quality of the articles was carried out based on Quality Assessment and Diagnostic Accuracy Tool (QUADAS-2) guidelines [18]. This tool is used to assess the quality of studies that report on diagnostic tools. The assessment is based on four domains (patient selection, index test, reference standard and flow and timing) each of which is evaluated for risk of bias and applicability. The inter-rater reliability was assessed on a sample of articles, where [PERSON]'s kappa showed 82% agreement between the two authors. For one article, there was a disagreement regarding its inclusion, since the quantifiable outcome measures of performance were not clearly mentioned. This was resolved through a third opinion obtained from (M.A), after which, the article was excluded. Twenty articles finally underwent qualitative synthesis (Figure 1).  
\begin{table}
\begin{tabular}{l l} \hline Research question & What is the performance of AI-based models designed for pediatric patients? \\ \hline Population & Pediatric patients who underwent investigation for oral disease \\ \hline Intervention & AI applications designed for detection, diagnosis, prediction of oral diseases in pediatric patients \\ \hline Comparison & Expert/Specialist opinions, Reference standards/models \\ \hline \multirow{6}{*}{Outcome} & Measurable or predictive outcomes, such as Accuracy, Sensitivity, Specificity, ROC = Receiver Operating Characteristic curve, AUC = Area Under the Curve, Area Under the Receiver Operating Characteristic = AUROC, ICC = Intraclass Correlation Coefficient, IOU = intersection-over-union, PRC = precision recall curve, Statistical Significance, F1 Scores, vDSC: Volumetric Dice Similarity Coefficient, sDSC: Surface Dice Similarity Coefficient, PPV = Positive Predictive Value, NPV = Negative Predictive Value, Mean Decreased Gini (MDC), Mean Decreased Accuracy (MDA) coefficients, Intersection over Union (IoU), Dice coefficient \\ \hline \end{tabular}
\end{table}
Table 1: Description of the PICO (P = Population, I = Intervention, C = Comparison, O = Outcome) elements.
# Detection and Attribution of Greening and Land Degradation of Dryland Areas in China and America
## 2 Materials and Methods
### Estimate of the CO\({}_{2}\) Fertilization  
A theoretical relationship is applied to attribute the CO\({}_{2}\) fertilization effects on vegetation growth. The equation is as follows:  
\[GPP\approx\frac{(C_{a}-\gamma)(C_{a0}+2\gamma)}{(C_{a}+2\gamma)(C_{a0}-\gamma)} \tag{2}\]  
GPP is the relative rate of CO\({}_{2}\) assimilation. \(C_{a}\) (mol/mol) is the atmospheric CO\({}_{2}\) concentration. \(\gamma\) (mol/mol) is the compensation point when the dark respiration is absent. \(C_{a0}\) is set 339 mol/mol as in 1980 and \(\gamma\) = 40 [28].  
Then, the nonlinear relationship in Equation (2) is used to calculate a scaled NDVI\({}_{a}\) that excludes the effects of CO\({}_{2}\). The relationship is as follows:  
\[\frac{\text{NPP}_{\text{o}}}{\text{NPP}_{\text{b}}}\approx\frac{\text{NDVI}_{ \text{o}}}{\text{NDVI}_{\text{a}}}. \tag{3}\]  
In this paper, we assume that there is no change in the ratio of GPP to autotrophic respiration. Based on this assumption, the relative change in GPP equates to the relative change in NPP [29]. NPP\({}_{o}\) is the NPP at the CO\({}_{2}\) concentration of \(C_{a}\). NPP\({}_{b}\) is the basic NPP at the CO\({}_{2}\) concentration of C\({}_{a0}\). The NDVI\({}_{o}\) represents the observed NDVI value, while NDVI\({}_{a}\) is the calculated NPP with the same climate conditions, but a fixed atmospheric CO\({}_{2}\) concentration of C\({}_{a0}\). Equation (3) is to calculate NDVI\({}_{a}\) with an atmospheric CO\({}_{2}\) concentration source from the IPCC historical forcing data. This analysis assumes that NPP is linear approximate to NDVI. NDVI\({}_{m}\) change due to CO\({}_{2}\) fertilization is from the difference between NDVI\({}_{m}\) with and without CO\({}_{2}\) fertilization (NDVI\({}_{o}\)- NDVI\({}_{a}\)). More description about this calculation can be found in [PERSON] et al. [30].