esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things-Based Healthcare Systems: An Overview of Privacy-Preserving Mechanisms
## 8 Privacy-Preserving Patient Monitoring  
Regarding the healthcare revolution driven by the IoT, which includes wearable devices for continuous patient monitoring, this section focuses on the technical aspects of preserving patient privacy during monitoring. It discusses the benefits of health tracking and monitoring while exploring the safeguarding of patient confidentiality through the use of various technologies, such as encryption, secure data aggregation, and anomaly detection.
## 5 Chitosan/Graphene Oxide as a Drug Delivery Device  
[PERSON] et al. [63] prepared chitosan/graphene oxide (CS/GO) nanocomposites, such as microneedle arrays, that were designed for transdermal drug delivery. The hybrid nanocomposite was found to provide faster and more effective drug release compared to pure chitosan, and the drug delivery profile was dependent on the ratio of the loaded drug. The drug release of these particular nanocomposites was also pH sensitive, with 48% less release under acidic conditions compared to a neutral environment. In another study, chitosan/sulfonated graphene oxide nanohybrid scaffolds were designed for drug delivery and as a tissue-engineering scaffold [64]. Greater hydrophilicity and improved  
Figure 7: Cross-linking reaction between GO and CS to form nanocomposite membranes [43].  
mechanical properties were observed compared to chitosan alone. These nanohybrid prototype scaffolds had a uniform porous structure that facilitated the sustained release of an antimicrobial drug, tetracycline hydrochloride. In addition, they were highly biocompatible.  
A genipin-crosslinked chitosan/graphene oxide (CS/GO) composite was prepared using a solution-casting method [65]. The presence of the GO decreased the expansion ratio of the composite film when exposed to physiological conditions and increased the resistance to in vitro degradation by lysozyme. A cell culture study using mouse MC3T3-E1 pre-osteoblasts showed positive adhesion and cell proliferation, and the prototype films were considered attractive candidates for tissue engineering and drug delivery applications. In another study, chitosan-functionalized graphene oxide hybrid nanosheets were synthesized and they demonstrated that the experimental nanosheets significantly improved the solubility of GO in aqueous acidic media [66]. They also provided controlled release of drugs, such as ibuprofen and 5-fluorouracil, and exhibited long-term biocompatibility, suggesting that these CS/GO-functionalized devices were suitable for biomedical applications such as drug delivery. Table 3 presents some other examples of the application of CS/GO films to serve as drug delivery devices.
# The Effect of Silica-Filler on Polyurethane Adhesives Based on Renewable Resource for Wood Bonding
## 3 Results and Discussion
### Wetting and Surface Properties  
As the analyses of contact angle, surface free energy and work of adhesion indicate the addition of SiO\({}_{2}\) resulted in a lower water contact angle (Figure 5) and increased work of adhesion (Table 4). A 17.9% increase in \(W_{\text{a}}\) was observed for the C-based PUR and 23.6% for the A-based PUR. In this work, the contact angles for water due to silica addition were 23\({}^{\circ}\)-27\({}^{\circ}\), while that observed for nanosilica-filled PURs reported in [27] was 53\({}^{\circ}\)-67\({}^{\circ}\) as well as surface free energy enhanced 44% which is 2-times that found in the literature for PURs with nanosilica doped at a comparable level (4.1 wt%).  
Work of adhesion values are slightly higher to those reported for water-beech or water-oak wood interactions (126-133 mJ/m\({}^{2}\)) [40]. As interactions between PUR and water are enhanced and water is considered the model of a polar phase, it can be inferred that PUR interactions with hydrophilic wood surface containing high concentration of OH groups (ca. 10\({}^{21}\) groups per gram [52]) are also enhanced. The phenomenon confirms that filling the adhesive with SiO\({}_{2}\) was beneficial for adhesion between veneer and adhesive and the observed changes in surface properties promote adhesive interactions. The significance of substrate/adhesive surface energy relation on the adhesion forces has been recognized and associated with interfacial tension [40; 53]. As Equation (3) indicates \(W_{a}\) is maximized when the interfacial tension \(\gamma_{SL}\) is equal zero. Thus, as silica addition induced an increase in PUR surface energy, the interfacial tension \(\gamma_{SL}\) was lowered and, subsequently, work of adhesion increased, which resulted in increased PUR/substrate interactions.  
Figure 4: Bondline shear strength of the studied PUR adhesives in solid wood bonded at 80 \({}^{\circ}\)C, 110 \({}^{\circ}\)C, or 130 \({}^{\circ}\)C. Numbers denote percentage of substrate failure (see Figure S2).
## 2 Materials and Methods
### Computational Fluid Dynamics Setup  
#### 2.1.1 Mesh Method  
Three curve files have been used to create the geometry of the hub, blade, and shroud according to the specification of the geometry, as given in Table 1. Automatic Topology and Meshing (ATM Optimized) is selected for high-quality mesh, and there is no need for a control point of adjustment. ANSYS-Turbo-Grid(r) and CFX software was used to generate high-quality hexahedral meshes and obtain the CFD solution for the rotor's steady-state case, keeping in view that five different meshes with different grid sizes were generated to study solution dependence upon mesh refinement. 3D mesh at coarse, medium, fine, superfine, and very-fine states were generated. For grid analysis, computations were carried out with 0.37 million to 1.47 million mesh nodes. The grid points were graduated towards the blade surface, the leading edge, the trailing edge, the hub, the rotor blade tip, and the housing to ensure that the y+ computed stayed below 100 (between 30-100). This value of y+ was within the usual range for the implementation of standard wall functions. It indicated a fair grid resolution near the walls, as the flow solution was assumed to be periodic across the blade row. Table 2 shows the details of the meshes used in the mesh dependence study, and Figure 1 shows the hexahedral-grid mesh with 0.97 million mesh elements, which is a reasonable compromise between computational and experimental results.
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 2 Materials and Methods  
### Study Area  
The study area is situated in Xigao Village, Pan'an County, Jinhua City, Zhejiang Province, China (28\({}^{\circ}\)55\({}^{\prime}\)20.1\({}^{\prime\prime}\) N, 120\({}^{\circ}\)29\({}^{\prime}\)07.4\({}^{\prime\prime}\) E) (Figure 1). Since the 1950s, the area has been predominantly covered by broad-leaved (evergreen and deciduous),iferous, and mixed broad-leaved andiferous forests. Bamboo cultivation was not previously practiced in this region in accordance with government policies. However, in recent decades, the demand for bamboo has increased because of social and economic factors, leading to a significant rise in bamboo cultivation near the villages in the study area. Nevertheless, the declining economic benefits of bamboo production have led to many of these planted bamboo forests being abandoned in recent years. These unmanaged bamboo forests have subsequently encroached upon adjacent natural forests, resulting in transitional areas characterized by a mix of bamboo and secondary forests. Field surveys indicate that forests in the study area comprise approximately 15 major tree species, namely _Phyllostachys edulis, Cunningham lancoleata, Pinus massoniana, Castanopsis sclerophylla, Castanopsis eyrei, Cryptomeria japonica, Sassafras tzumu, Houpoa officinalis, Schina superba, Liriodendron chinese, Liquidambar formosana, Lindera glauca, Camphora officinarum, Choerospondias axillaris_, and _Quercus glauca_.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 3 Methodology
### Mapping Module  
Aiming to remove dynamic objects from the local maps, the motion regions in the images obtained in Section 3.3 are used to determine whether the semantic masks in the BiSeNet V2 network are dynamic object masks. Specifically, the proportion \(\rho_{i}\) of dynamic regions in each semantic mask is first calculated according to Equation (5). if \(\rho_{i}\) is higher than the threshold \(\varphi\), the semantic mask is determined to be dynamic, otherwise it is static, as shown in Figure 9. Then, the local three-dimensional semantic map is generated after  
Figure 8: Comparison of the effect of three-dimensional semantic annotation. **(a)** Original image of schematic diagram before and after keyboard semantic optimization; **(b)** Original image of schematic diagram before and after mouse semantic optimization; **(c)** Original image of schematic diagram before and after Book Semantic optimization; **(d)** Original image of schematic diagram before and after display semantic optimization.  
removing the dynamic object mask areas in the color image and depth image, as shown in Figure 10. Each frame of the point cloud can be concatenated to form a global map based on the pose information. Finally, the global map is optimized to generate an accurate 3D semantic map based on loopback detection and global BA in ORB-SLAM2.  
\[\rho_{i}=\frac{F_{i}}{M_{i}} \tag{5}\]  
where \(M_{i}\) represents the number of pixels of one of semantic mask in the image and \(F_{i}\) represents the number of pixels in the dynamic region in \(M_{i}\).  
After constructing a local map containing semantic information for the key frames, the global map is joined by the camera pose. Let the 3D coordinate points constructed in frame \(i\) be the point set \(P_{i}\), and the local point cloud map is constructed by stitching the 3D coordinate points of a single frame. When loopback is detected, the vSLAM  
Figure 10: Schematic diagram of restoring 3D coordinates from depth information. (**a**) Constructing 3D point cloud from original depth image; (**b**) Combining static depth image and geometric segmentation to construct 3D point cloud.  
Figure 9: Schematic diagram of dynamic target detection.  
system optimizes the overall motion pose and outputs the global point cloud map with pose information.
## 2 Frequency Factors and Constraints  
In space-borne, gravitational-wave detection missions, laser links need to accomplish ultralong-range laser interference and various auxiliary functions, such as clock-noise transfer, pseudorandom noise (PRN) ranging, and information transfer [10; 17]. The Doppler shift affects the beat-note signal generated by heterodyne interference; therefore, offset frequencies need to be added to the phase-locking process to make them fall into a reasonable range. The signal is sampled and analyzed by the ADC and PM and triggered by the USO onboard. The USO signal is first multiplied to the GHz level and imprinted on the sidebands of the outgoing laser beam by EOM to perform inter-spacecraft clock transfer. Setting the sideband frequency and power ratio will affect the size of the readout displacement noise. The USO is then multiplied to the MHz level to provide the internal clock signal for the ADC and PM operations and construct the pilot tone to eliminate ADC sampling jitter and clock noise. In this process, some parameters have complex mutual constraints and need to be optimized systematically.
# An Early Warning System to Detect At-Risk Students in Online Higher Education
###### Abstract  
Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students' risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.  
early warning system; artificial intelligence; predictive models; personalized feedback; online learning +
Footnote †: journal: Applied Sciences
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
## 2 Hydration-Based Property Evaluation Model of Binary Blends of Cement-Slag
### Hydration Heat  
For hydrating a binary composite system, the slag reaction and cement hydration contribute to the hydration heat. The total released heat equals the sum of the individual reactions of binders as follows:  
\[Q=C_{0}*H_{C}*\alpha+P*H_{SG}*\alpha_{SG}, \tag{6}\]  
where \(H_{C}\) and \(H_{SG}\) denote the specific heat generation from cement hydration and the slag reaction, respectively. \(H_{C}\) can be determined based on the cement's mineral composition [22]. The value of the specific heat generation of slag reaction \(H_{SC}\) was proposed as 630 J/g [25; 26]. The terms \(C_{0}*H_{C}*\alpha\) and \(P*H_{SG}*\alpha_{SG}\) consider the release of heat from the cement hydration and the slag reaction, respectively.  
As shown in Equation (6), cumulative hydration heat can be used as an indirect method to determine the extent of the reaction of slag. Moreover, the coefficients of the reaction model of slag can be determined based on the calibration of the results of the experimental study of hydration heat. In addition, because the proposed model covers the interactions between individual reactions of slag and cement, the coefficients of the slag reaction model and the cement hydration model are constants for various mixtures with different slag replacement percentages and water/binder ratios.
_Applied_  
_sciences_  
_Article_  
**Longitudinal Tear Protein Changes Correlate with Ocular Chronic GVHD Development in Allogeneicic Hematopoietic Stem Cell Transplant Patients**  
**[PERSON] 1(c)**, [PERSON] 1, [PERSON] 1(c)**, [PERSON] 2, [PERSON] 2, [PERSON] 2 and [PERSON] 1,2,* 2  
\begin{tabular}{c c}  & 1 Laboratory for Ocular Surface Analysis and Translational Research, DIMES, Alma Mater Studiorum \\  & University of Bologna, 40138 Bologna, Italy; [EMAIL_ADDRESS] (C.C.); [PERSON]] (G.A.); [PERSON]N.V) \\
2 IRCCS Azienda Ospedalde Universitaria di Bologna, Policinico di Sant'Orsola, Bologna, 40138 Bologna, Italy; [EMAIL_ADDRESS] (F.B.) \\
* Correspondence: [EMAIL_ADDRESS] \\ \end{tabular}  
**Featured Application: This study reports for the first time an individual proteomic analysis of tears in patients pre-post HSCT, earlier than oGVHD onset. Total protein content, Lactoferrin, Transferrin, and Zinc-alpha-2-glycoprotein pre-post changes might be significant predictors of oGVHD development. Future efforts should further explore and support the appropriateness of incorporating tear proteome analysis in the clinical work up.**  
**Abstract: Ocular graft-versus-host disease (oGVHD) is a manifestation of chronic GVHD, frequently occurring in patients after allogeneic hematopoietic stem cell transplant (HSCT). We analyzed tear protein changes before and after allogeneic HSCT, and correlated their levels with the oGVHD development. This retrospective study included 102 patients, and data were recorded before the conditioning treatment, and after 3 to 6 months postoperatively. Year protein analysis was performed with the Agilent-2100 Bioanalyzer on individual tears sampled by aspiration. Total protein (TP), Lysozyme-C (Lys-C), Lactoferrin (LACTO), Lipocalin-1 (LIPOC-1), Transferrin (TRANSF), Albumin (ALB), and Zinc-alpha-2-glycoprotein (ZAG-2) levels were retrieved and statistically analyzed. Following HSCT forty-three patients developed oGVHD. TP, LACTO, Lys-C, and ZAG-2 levels significantly decreased post-HSCT as compared to pre HSCT levels. In univariate analysis, TP, LACTO, and ZAG-2 decrease was associated with an increased development of oGVHD (OR = 4.49; 95% CI, 1.9 to 10.5; \(p<0.001\); OR = 3.08; 95% CI 1.3 to 7.6; \(p=0.01\); OR = 11.1; 95% CI 2.7 to 46.6; \(p<0.001\), respectively). TRANSF post-HSCT levels significantly increased (OR 15.7; 95% CI, 4.1 to 52.2; \(p=0.0001\)). No pre-post-HSCT changes were shown in ALB and LIPOC-1 levels. Data suggest that TP content, LACTO, TRANSF, and ZAG-2 pre-post changes might be significant predictors of oGVHD development.**  
**Keywords: tear proteins; ocular GVHD; HSCT; odd ratio; lactoferrin; lysozyme; albumin; transferrin; lipocalin-1; zinc-alpha-2-glycoprotein; proteomic**
# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 5 GA-Based Network Clustering Model
### GA Processing  
Some parameters must be defined in the proposed algorithm. \(Q\) and \(q\) represent the size and number of generations, respectively. \(P\) denotes the population size. \(L\) indicates the library of chromosomes of individuals that are randomly generated. \(n\) indicates the number of nodes.  
In the crossover process, based on the crossover probability, two chromosomes are randomly chosen from \(L\), and two new chromosomes are generated by switching consecutive genes. In the mutation process, based on the mutation probability, a gene is randomly chosen, and its value is set between zero and one.  
Nodes in the GCUM are represented by a chromosome library \(K\), where \(K=\{k_{1},\ k_{2},\ldots,k_{n}\}\). Based on function (6) and the routing paths from the DCMST, the nodes in the network are clustered. The fitness of each node is calculated by function (7). A normalized fitness function is introduced as follows:  
\[\hat{f}(k)=\frac{f(k)}{\sum_{c}f(k)} \tag{11}\]  
Two different chromosomes are randomly chosen from K. Based on the crossover probability, \(\alpha\) is converted to \(k^{\prime}_{a}\) and \(k^{\prime}_{b}\) by the crossover process. Then, based on the mutation probability \(\beta\), \(k^{\prime}_{a}\) and \(k^{\prime}_{b}\) are changed to \(k^{*}_{a}\) and \(k^{*}_{b}\) by the mutation process. Based on the new fitness level, \(f(k^{*}_{a})\) and \(f(k^{*}_{b})\), which are calculated based on \(k^{*}_{a}\) and \(k^{*}_{b}\), a restructured function is introduced to obtain the clustering structure based on the new library of chromosomes:  
\[k^{*}=arg\underset{k}{\max}f(k) \tag{12}\]  
Therefore, a new cluster structure is obtained by the GCUM when the calculation times reach \(Q\) or the calculation converges.
###### Abstract  
This study explores how urban morphological and environmental factors influence Urban Heat Islands (UHIs) using a geospatial modeling approach. The aim of the research is to develop a methodology to assess UHI effects, emphasizing the role of urban morphology, land use, and vegetation in nighttime heat accumulation. A micro-scale analysis with a 50 m resolution is conducted by integrating a custom QGIS plugin with open-access data, ensuring broad applicability. The 50 m resolution was chosen because it allows for the capture of local variations in UHI intensity while maintaining the scalability of the urban analysis across different city contexts. Non-parametric statistical analyses (ANOVA, Kruskal-Wallis H test, and correlation assessments) were used to evaluate the relationships between the urban parameters--wind corridors, altitude, vegetation (NDVI), surface water (NDWI), and the Sky View Factor (SVF)--and Nighttime Land Surface Temperature (LST). Given that UHI variations during summer, particularly in cities of the Iberian Peninsula, are closely linked to summer heat severity, this factor was considered to classify the cities for the study. Correlation analyses confirm that all tested factors influence LST, with wind corridors being the least significant. The model performance evaluation shows the highest errors in cities with lower summer severity (RMSE = 1.586 \({}^{\circ}\)C, MAE = 1.2686 \({}^{\circ}\)C, MAPE = 6.99%) and the best performance in warmer cities (RMSE = 1.4 \({}^{\circ}\)C, MAE = 1.14 \({}^{\circ}\)C, MAPE = 4.5%). Validation in four cities of the Iberian Peninsula confirmed the model's reliability, with the worst RMSE value of 2.04 \({}^{\circ}\)C. These findings contribute to a better understanding of the factors driving UHIs and provide a scalable assessment framework.  
urban heat island; land surface temperature; buildings; urban morphology; NDVI; NDWI; geospatial analysis; urban planning
# A Comprehensive Review of the Application Characteristics of Biodiesel Blends in Diesel Engines
## 5 Emission Characteristics
### Regulated Emissions
#### 5.1.3. Particulate Matter  
Particulate matter (PM) has three components: dry soot (DS), soluble organic fraction (SOF) and sulfate. Foot accounts for about half of the PM component [98]. Foot formation usually occurs in fuel-rich areas at high temperatures without sufficient oxygen concentrations [99]. Hydrocarbon-containing fuels are pyrolyzed in a high-temperature and anoxic environment in the cylinder, which will generate soot precursor materials as well as unsaturated hydrocarbons, polyacetylene, and polycyclic aromatic hydrocarbons. The small-molecule carbon-hydrogen phase reactants form a large-molecular aromatic structure through the addition of free radicals, and they serve as a soot core in the early stage of combustion [98]. In the later stage of combustion, the particle nucleus continues to physically and chemically adsorb dehydrogenated gaseous hydrocarbons, which greatly increases the quality of the particle [100]. Particles also combine and collide to form larger spherical or chain-like structures, and soot is formed during subsequent oxidation. Subsequently, the semi-volatile or low-volatile substances such as unburned hydrocarbons, sulfates, and moisture condensed on the surface due to the decrease in ambient temperature and formed PM [98]. Excessive PM emissions can affect human health. They can cause cough and wheezing, and worsen various respiratory and cardiopulmonary diseases, and even cause death [101].  
Most studies show that fuels containing a certain proportion of biodiesel can effectively reduce PM generation. [PERSON] et al. investigated the effects of biodiesel derived from waste cooking oil on PM emissions in a six-cylinder turbocharged diesel engine with a high-pressure common rail injection system in compliance with a 13-mode European stationary cycle (ESC). They found that there was significant reductions in PM emissions with biodiesel blends relative to those of the reference diesel at all modes, a maximum of 84% PM reduction was observed with the biodiesel blends, as shown in Figure 11[102]. Many researchers reached a similar conclusion for PM [54; 56; 90; 103]. The higher oxygen content resulting in less oxygen demand is considered to be an important factor, which will easily promote more stable and complete combustion of fuels in oil-rich areas [104; 105]. In addition, there are fewer aromatic compounds and very few sulfur atoms, which form soot precursors, so less PM can be generated [83; 104; 106]. Biodiesel is mainly composed of long-chain hydrocarbon groups, which will lead to a higher cetane number [107].  
Figure 10. Effects of canola biodiesel blends on CO emissions (adapted with permission from [91], Elsevier, 2017).