esa-sceva/satcom-chunk-collection huggingface dataset:
### Research Questions and Objectives  
In real-world architecture, residential buildings, with which residents interact frequently and for extended periods, often lack proper color planning [49]. This deficiency negatively impacts residents' psychological well-being in their daily lives and commutes [4], reducing the overall living experience. Recently, an increasing number of scholars have focused on the impact of architectural color on users' emotions, leading to more in-depth research in this area. In China, the study of architectural color is still in an exploratory phase, with most research concentrating on monochromatic buildings. These studies typically involve organizing architectural colors in various ways and collecting user feedback. However, the prevalence of buildings with poorly planned combined color schemes is starting to disrupt the overall aesthetic tone of cities [4], diminishing residents' quality of life. There is an urgent need for systematic theoretical support to guide the reorganization and renovation of color combinations in urban architecture.  
This study involved on-site visits to old residential buildings in the Yangpu District of Shanghai, analyzing the relationship between facade color elements, eye-tracking metrics, and subjective evaluations. The aim was to explore the potential application of eye-tracking metrics in studying architectural color combinations, with the objective of developing an analytical framework for the visual perception of multicolored residential buildings by integrating eye-tracking data with subjective evaluation metrics. Using two-tone buildings as experimental subjects, this study quantitatively analyzes the relationship between color element differences and visual quality. Additionally, through eye-tracking experiments, the study explores how differences in color elements impact visual quality, expressing these effects more precisely through mathematical models. The study raises the following questions:  
1. Can eye-tracking metrics serve as new indicators for evaluating architectural color combinations?
2. How can the relationship between architectural color combinations and visual quality be quantitatively analyzed? What is the relationship between the two?
3. What common characteristics are shared by color combinations that receive high visual quality evaluations?
## 3 Structural Design and Analysis of QZS Vibration Isolation Systems
### The Analysis of the Compression of the Leaf Spring  
Given the elastic modulus \(E\), width \(b\), length \(l_{0}\), and thickness \(h\) of the LS, we can proceed to calculate the compression amount through the calculation of axial compression potential energy.  
The bending strain energy \(U_{c}\) of the beam can be computed using the deflection:  
\[U_{c}=\frac{EI}{2}\int{(\frac{d^{2}w}{dx^{2}})}^{2}dx \tag{7}\]  
\(I\) is the moment of inertia of the beam. For a beam fixed at both ends, its post-buckling deflection curve (blue curve in Figure 7) is as follows [39]:  
\[w_{1}=\frac{\delta_{2}}{2}(1-\cos\frac{2\pi x}{l_{0}-u_{x}}) \tag{8}\]  
Substitute Equation (8) into Equation (7) and integrate as follows:  
\[U_{c}=\frac{EI{\delta_{2}}^{2}\pi^{4}}{(l_{0}-u_{x})^{3}} \tag{9}\]  
The derivative of the potential energy \(U_{c}\) with respect to the compression \(u_{x}\) yields an approximate value of \(F\) (the exact value is the sum of the \(U_{c}\) and \(U_{d}\) terms in Equation (3), but the \(U_{d}\) term is several orders of magnitude smaller than the \(U_{c}\) term; here, \(F\) can be used as an exact value), but Equation (9) still contains the unknown \(\delta_{2}\). After the beam undergoes flexural deformation, its length \(L\) is calculated using the curve length formula:  
\[\begin{split}& L=\int_{0}^{l_{0}-u_{x}}\sqrt{1+\left(\frac{dw_{1}}{ dx}\right)^{2}}dx\\ &=\int_{0}^{l_{0}-u_{x}}\sqrt{1+\left(\frac{\pi\delta_{2}}{l_{0}- u_{x}}\sin(\frac{2\pi x}{l_{0}-u_{x}})\right)^{2}}dx\end{split} \tag{10}\]  
The integral can be approximated as follows [37]:  
\[\begin{split}& L=\int_{0}^{l_{0}-u_{x}}1+\frac{1}{2}\bigg{(} \frac{\pi\delta_{2}}{l_{0}-u_{x}}\sin(\frac{2\pi x}{l_{0}-u_{x}})\bigg{)}^{2} dx\\ &=\frac{\delta_{2}^{2}\pi^{2}}{4(l_{0}-u_{x})}+l_{0}-u_{x}\approx l _{0}\end{split} \tag{11}\]  
The beam length \(L\) after compression buckling can be approximated to the initial length \(l_{0}\), as the shortening due to pre-tension is several orders of magnitude lower than \(l_{0}\). According to Equation (11), the relationship between \(\delta_{2}\) and compression \(u_{x}\) is derived as follows:  
\[\delta_{2}=\sqrt{\frac{4u_{x}(l_{0}-u_{x})}{\pi^{2}}} \tag{12}\]Substituting Equation (12) into Equation (9) and differentiating gives the following:  
\[U_{c}=\frac{4 EI\pi^{2}}{\left(l_{0}-u_{x}\right)^{2}}u_{x} \tag{13}\]  
\[\frac{\partial U_{c}}{\partial u_{x}}=4 EI\pi^{2}\Big{[}(l_{0}-u_{x})^{-2}+2(l_{0 }-u_{x})^{-3}\Big{]}\approx F \tag{14}\]  
Figure 9 shows the relationship between the compression displacement u\({}_{\mathrm{x}}\) and the \(F\) of compliant LSs calculated using Equation (14), compared with finite element simulation results. It is evident that the compression stiffness after buckling may decrease by several orders of magnitude compared to before buckling. This suggests that using buckled LSs as negative stiffness elements has good tolerance to errors in compression displacement, as \(F\) is not highly sensitive to compression displacement errors.
# Graphene-Based Materials for the Separator Functionalization of Lithium-Ion/Metal/Sulfur Batteries
## 2 Preparation Methods of Graphene-Based Materials
### Others
#### 2.3.1 Micromechanical Cleavage  
Micromechanical cleavage was proposed by [PERSON] et al. in 2004 to prepare high-quality graphene by physically or chemically breaking the van der Waals forces between graphite layers and exfoliating the graphite flakes (Figure 4A) [12]. This method involves etching a groove onto the surface of highly oriented pyrolytic graphite (HOPG) with an oxygen iso-ion beam and pressing it onto a SiO\({}_{2}\)/Si substrate with a photoresist. After roasting, excess graphite flakes are repeatedly peeled off with transparent tape, and the remaining graphite flakes on the Si wafer are soaked in an acetone solution and ultrasonically cleaned. Most of the thicker flakes are removed, leaving graphene flakes less than 10 nm thick. In 2005, [PERSON] et al. used this method to prepare monolayer graphene, demonstrating that monolayer graphene is able to exist independently [66] and successfully prepared suspended planar monolayer graphene with a folded structure supported by metal in 2007 [67]. Due to its low yield, the scheme is only suitable for graphene preparation in the laboratory and not for large-scale production and application.
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 5 Conclusions  
This paper aims to evaluate the quantitative impact of ASR on load-bearing capacity because ASR has become an issue in the maintenance and management of social infrastructure. In this study, we first proposed a concrete mesoscale model to reproduce ASR degradation, identified the material model, and reproduced its mechanical behavior based on uniaxial compression tests of cylindrical specimens. Furthermore, an RC beam model using the mesoscale model was developed as a fundamental study, and the effects of various ASR degradation factors on load-bearing performance were examined. The following is a summary of the conclusions obtained from this study.  
1. A mesoscale model consisting of three phases, namely, coarse aggregate, mortar paste, and ITZ, was proposed, which can take into account the coupling between water movement and cracks specific to ASR.
2. The material parameters were identified based on uniaxial compression tests of cylindrical specimens subjected to expansion and deterioration, and it was shown that the proposed model can reproduce the changes in compressive properties due to expansion and cracking.
3. A mesoscale model of an RC beam was constructed using a rebar mesh that reproduced the shape of the nodes and ribs in detail. A coupled analysis of moisture movement and expansion using this model clarified the effects of moisture movement and expansion cracks.
4. Although the initial stiffness of the flexural load-bearing capacity of ASR-deteriorated RC beams decreased, no decrease in load-bearing capacity was observed, and it was shown that there is a possibility that it may actually improve. This is thought to be due to the effects of the analysis model, such as the bond between the rebar and concrete, as well as the effects of chemical prestress due to expansion.
* Even in cases where the amount of cracking was small, a decrease in maximum load and toughness was observed, and it is considered inappropriate for maintenance to evaluate the degree of decrease in load-carrying capacity simply based on the amount of cracking.  
Conceptualization, H.T.; methodology, T.K., D.Y. and H.T.; formal analysis, T.K. and H.T.; investigation, T.K.; writing--original draft preparation, T.K.; writing--review and editing, H.T.; visualization, H.T. All authors have read and agreed to the published version of the manuscript.  
This study was supported financially in part by a Grant-in-Aid for Scientific Research grant 24K00970 (Grant-in-Aid for Scientific Research (B)) from the Japanese Society for the Promotion of Science (JSPS).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
Author [PERSON] was employed by the company Kawasaki Heavy Industries, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be constructed as a potential conflict of interest.
## 3 Epidemiology and Diagnosis
### Incidence Associated with Surgical Intervention  
#### 3.2.1 Tooth Extraction  
Tooth extraction in patients undergoing antiresorptive medication therapy, such as BPs and DS, has been closely scrutinized due to the associated risk of MRONJ. Recent systematic reviews shed light on the incidence rates and risk factors tied to MRONJ following tooth extractions, underscoring the complex interplay between medication use, dental procedures, and patient characteristics [14; 15].  
A review delving into the impact of oral BP therapy on tooth extraction procedures highlighted an increased risk of developing MRONJ as a notable complication following such dental procedures [14]. The study identified the duration of BP treatment as a pivotal factor in the incidence of MRONJ, particularly emphasizing a higher prevalence in the posterior region of the mandible [14]. Patients with a history of BP treatment exceeding three years warrant careful monitoring and management due to the elevated risk [35]. This heightened risk can be attributed to the significantly higher bone turnover rate in the mandible compared to other skeletal areas like the tibia [36], a factor that results in a greater accumulation of BPs in the mandibular bone [37]. The review further underscored the importance of a comprehensive understanding of procedural details, including the selection of grafting materials, adherence to meticulous surgical protocols, and an awareness of expected success rates, as key components in mitigating the risk of MRONJ following tooth extraction procedures [14].  
Complementing these findings, another review specifically focused on cancer patients treated with high-dose BP and DS [15]. This review reported a varying MRONJ incidence rate of 11% to 50% after tooth extractions, emphasizing the significant heterogeneity in the collected data [15]. Key risk factors identified included teeth affected by pre-extraction inflammation [38] and additional osteotomy during the surgical procedure [15]. The study also highlighted the lack of statistically significant associations between MRONJ incidence and factors such as age, gender, and tobacco and alcohol use, emphasizing the need for a nuanced understanding of individual patient profiles when assessing MRONJ risk [15].  
Both reviews converge on the critical insight that while tooth extraction is a pivotal trigger for MRONJ, the condition's manifestation is significantly influenced by the presence of pre-existing inflammation, the specifics of the surgical procedure, and the duration of antiresorptive medication use [14; 15; 16]. Clinicians are thus urged to approach tooth extractions in patients on antiresorptive therapy with heightened vigilance, tailoring their strategies to the nuanced risk profiles presented by individual patients. The reviews collectively stress the importance of an integrated approach--combining precise surgical techniques, thorough patient education, and vigilant postoperative monitoring--to effectively manage and mitigate the risks associated with MRONJ in this sensitive patient cohort.
## 4 Simulation Results and Discussion
### CPNRA Simulator Execution
By applying the firing time strategy of Section 3.1.2 we find that the process of resource allocation will start for \(\tau_{2}\) at time \(50+\delta\) (the maximum of the two time stamps in positions \(p_{1}\) and \(p_{2}\)). For the second request, when the monitor sees the request \([1,1,0]\), it places one green token in \(p_{3}\) as seen in Figure 6f using the transition \(t_{p3}\). Now, there are two transitions enabled, but because the condition  
\[\text{G3: (IF token\_Id.ToRR = [0,1,x])}\] (Guard.3)  
does not hold, \(t_{3}\) will not fire. However \(t_{1}\) can fire and this will place a token in \(p_{4}\) (see Figure 6g). Now, the newly generated red-black token proceeds as explained before for \(\tau_{1}\), until it reaches \(p_{2}\), where it places a black token \(\tau_{8}=<3,75>\). This will be used for the firing of \(t_{3}\) next. Now, the DSQ request has been fulfilled and the execution of \(t_{4}\) has updated the ToRRs for both the green and red tokens to [0,1,0]. Now, transition \(t_{1}\) is not enabled because of G1, but now \(t_{3}\) can fire since the condition  
\[\text{G3: (IF token\_Id.ToRR = [0,1,x])}\] (Guard.3)  
now holds. Thus, the fair resource allocation policy can be implemented and a green-black token will be placed to \(p_{6}\) (see Figure 6h). Then \(t_{5}\) is enabled and, as before, a token with infinite time stamp will be placed to \(p_{3}\) while \(p_{5}\) will get a black token, which indicates the beginning of processing for the next request (see Figures 6i,j). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Job\_Id** & **Priority** & **Arrival\_Time** & **Service\_Time** & **Type\_of\_Resources\_Requested** \\ \hline
1 & 1 & 20 & 30 & [1,0,0] \\
2 & 1 & 20 & 25 & [1,1,0] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An execution example. Now, if a third token appears with requests [1,1,1], the same form of execution will follow, but with some differences:  
1. The monitor will place a red, a green, and a blue token to \(p_{1}\), \(p_{3}\), and \(p_{8}\) respectively, with initial ToRR=[1,1,1]. 2. Transition \(t_{1}\) will have priority over \(t_{3}\) and \(t_{6}\) until the DSQ request is fulfilled. Then, a token with infinite time stamp will be placed to \(p_{1}\). 3. Transition \(t_{3}\) will have priority over \(t_{6}\) until the \(Q_{2}\) request is fulfilled. Then, a token with infinite time stamp will be placed to \(p_{3}\). 4. Transition \(t_{6}\) will have the lowest priority. When the \(Q_{3}\) request is fulfilled, a token with infinite time stamp will be placed to \(p_{8}\). Finally, if a job requires extra resources, it makes the request and the monitor treats it as a new token, so that it keeps a fair policy in the way the tasks take turns for requesting resources.
## 5 Conclusions and Future Work  
This paper proposed a comprehensive and physically grounded modeling framework for estimating the energy consumption of UAV-based last-mile delivery missions integrated with a public transportation system. Unlike traditional models based solely on empirical correlations or geometric approximations, the proposed approach leverages a force-based energy formulation derived from the Fundamental Principle of Dynamics, corrected by aerodynamic factors obtained via CFD simulations.  
The methodology was applied to a real-world case study in the city of Belfort (France), involving five bus lines, four operational scenarios, and ten payload configurations. Results confirm the model's ability to capture the linear dependency between payload mass and energy consumption, while also highlighting the significant impact of realistic constraints such as delivery time windows, bus schedule synchronization, and environmental disturbances. Notably, the most constrained scenario (early return strategy) can lead to up to 26% more energy consumption than the ideal baseline.  
The modular structure of the model enables scenario-aware planning, accurate energy budgeting, and battery dimensioning for urban drone logistics. It also paves the way for integration into real-time mission feasibility tools or embedded estimation modules.
## 2 Materials and Methods
### Test Methods  
In order to calculate the FRF experimentally, it is necessary to measure the experimental values of the impact and response energy. The configuration of the experimental equipment for the FRF measurement is shown in Figure 2: (a) The impact hammer (PCB 086C03) (PCB Piezotronics, Inc., Depew, NY, USA) measures the impact energy in the test specimen; (b) the accelerometer (PCB 208C05) (PCB Piezotronics, Inc., Depew, NY, USA) measures responses that depend on the test specimen; (c) signal conditioning (PCB 482C16) (PCB Piezotronics, Inc., Depew, NY, USA); and (d) DAQ (NI PXIe-6366) (National Instruments Co., Austin, TX, USA) are employed to collect data from the test apparatus.  
The measurement program stores data at a sampling rate of 500 kS/sec using NI LabVIEW SignalExpress (National Instruments Co., Austin, TX, USA). Here, because the stored data are values in the time domain, it is transformed into the frequency domain using the MATLAB signal process toolbox (MathWorks, Inc., Natick, MA, USA), and the FRF is obtained using Equation (1). Through the obtained FRF, the natural frequency and FRF waveform are analyzed according to the damage types of porcelain insulators.
## 4 The Developed Context Model
### Relevant Aspects of the Context Model
#### 4.5.4 The contexts have relationships among them  
These relationships are based on bridge rules. They link different contexts when the knowledge inferred in one context has influence in the knowledge of another context [12]. The bridge rules allow the mapping of multiple WSN's contexts [26]. For instance, to compute the network coverage area (network context) is necessary to know the position of the nodes (node context). On the other hand, for security reasons only authorised systems (organisational context) are allowed to access to certain sensing functionalities (sensing context). The fixed/mobile contexts (node context) could be inferred from the GPS or accelerometer data (sensing context). Furthermore, any node interaction must be validated by the security and privacy restriction (organisational context).
# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 2 Related Work
[PERSON] et al. [39] investigated the relationship between demographic, biochemical, and lifestyle factors with vitamin D concentration in healthy premenopausal Chinese women. The study analyzed 593 women, using data including 35 variables (age, hemoglobin, and cholesterol levels), identifying age as the most significant factor in vitamin D concentration. The algorithms employed Random Forest, XGBoost, and Elastic Net. The results showed that the Random Forest algorithm obtained an RMSE of 6.9913 and SMAPE of 0.277, while the Elastic Net algorithm obtained an RMSE of 6.9325 and SMAPE of 0.2721. Finally, [PERSON] et al. [40] evaluated vitamin C deficiency in bariatric and metabolic surgery patients. They conducted a study with data from 5946 patients, of whom 187 had measured vitamin C levels. They used BayesNet and Random Forest models to create predictive models, which achieved up to 70% accuracy in identifying vitamin C deficiency. Thus, according to the previously mentioned research, an enormous amount of clinical data is collected daily in the medical field to study hypovitaminosis D; processing these large datasets presents a manual challenge with traditional approaches such as Linear Regression models. Thus, the main objectives of this research are as follows: (i) Create a secure private database with sociodemographic and clinical information about vitamin D levels. (ii) Identify the main variables associated with vitamin D deficiency levels and several risk factors. (iii) Evaluate the performance of the most effective ML models in vitamin D prediction deficiency levels based on statistical metrics. (iv) Develop an intuitive web GUI tool that lets users predict and diagnose vitamin D deficiency levels in real time. The application includes three main modules and functionalities: patients (general information, habits, patient history), predictions (uses machine learning algorithms to estimate the risk of vitamin D deficiency, graphs to visualize risk probability, risk factors analyzed using bars and progress), and system.
## 5 Applications to Aerodynamic Design Optimization of Wing Shapes
### Parameterization Strategy  
Airfoil parameterization is a crucial step in aerodynamic optimization, and its accuracy determines the accuracy and reliability of the optimized airfoil. The commonly used parameterization methods are the free-form deformation (FFD) technique [34], Bezier curves [35], the class/shape transformation (CST) method [36], etc. In this work, a four-order CST parameterization method is used to control the airfoil shape, and the parametric expressions of the upper and lower surface curves are defined in Equations (20) and (21).  
The design variables are the leading-edge radius \(R_{lc}\) of the airfoil, the inclination angles \(b_{i}\) and \(b_{i}^{\prime}\) of the upper and lower surface curves at the trailing edge and the upper and lower surface shape function control parameters \(\beta\) and \(\beta^{\prime}\). For a total of nine airfoil design parameters, with the reference geometry being the RAE2822 airfoil, the design parameters and corresponding constraint ranges are shown in Table 8.  
\[\frac{z}{\hat{c}}=\sqrt{\frac{x}{\hat{c}}}(1-\frac{x}{\hat{c}})\bigg{\{}\sqrt{ \frac{2R_{lc}}{c}}\big{(}1-\frac{x}{\hat{c}}\big{)}^{n}+\tan\beta\big{(} \frac{x}{\hat{c}}\big{)}^{n}+\sum_{i=1}^{n-1}\Big{[}b_{i}\frac{n!}{i!(n-i)!} \big{(}\frac{x}{\hat{c}}\big{)}^{i}\big{(}1-\frac{x}{\hat{c}}\big{)}^{n-i!} \Big{]}\bigg{\}} \tag{20}\]  
\[\frac{z}{\hat{c}}=\sqrt{\frac{x}{\hat{c}}}(1-\frac{x}{\hat{c}})\bigg{\{}-\sqrt{ \frac{2R_{lc}}{c}}(1-\frac{x}{\hat{c}})^{n}+\tan\beta^{\prime}\big{(}\frac{x} {\hat{c}}\big{)}^{n}-\sum_{i=1}^{n-1}\Big{[}b_{i}^{\prime}\frac{n!}{i!(n-i)!} \big{(}\frac{x}{\hat{c}}\big{)}^{i}\big{(}1-\frac{x}{\hat{c}}\big{)}^{n-i!} \Big{]}\bigg{\}} \tag{21}\]