esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To date, _cBP-Tnet_ was the only deep learning method with automatic photoplethys-mogram feature extraction [5, 16, 17] to have both systolic (MAE: 4.32 mmHg) and diastolic (MAE: 2.18 mmHg) blood pressure acceptable in accordance to the Association for the Advancement of Medical Instrumentation (AAMI)'s international standards (<5 mmHg, >85 subjects) [20]. _cBP-Tnet_ was designed effectively as it is a single-channel model, taking into account recent deep learning methods for continuous non-invasive blood pressure monitoring [5, 16, 17], which were hybrid and/or complex in design and needed multiple models to operate. The _cBP-Tnet_ method efficiently takes 13.67% faster to train and still outputs better as well as AAMI-accepted results compared to recent studies [5] in the field. This establishes _cBP-Tnet_'s potential for integration into wearable and home-based healthcare devices, paving the way for more accessible, dependable continuous non-invasive blood pressure monitoring.  
Conceptualization, J.-J.H. and A.R.A.S.; methodology, A.A.P., J.-J.H. and A.R.A.S.; software, A.A.P.; validation, A.A.P., J.-J.H. and A.R.A.S.; formal analysis, A.A.P., J.-J.H. and A.R.A.S.; investigation, A.A.P., J.-J.H. and A.R.A.S.; resources, J.-J.H. and A.R.A.S.; data curation, A.A.P.; writing--original draft preparation, A.A.P.; writing--review and editing, J.-J.H. and A.R.A.S.; visualization, A.A.P.; supervision, J.-J.H. and A.R.A.S.; project administration, J.-J.H.; funding acquisition, J.-J.H. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully acknowledge the financial support from the National Science and Technology Council (NSTC), Taiwan, under Grant No. NSTC 113-2221-E-218-008-. This work was also supported by the Center for Intelligent Healthcare through the Higher Education Sprout Project, funded by the Ministry of Education, Taiwan.  
Not applicable.  
The public dataset used and code in this study was made available at [[https://github.com/apimentel-ECE/cBP-Tnet.git](https://github.com/apimentel-ECE/cBP-Tnet.git)]([https://github.com/apimentel-ECE/cBP-Tnet.git](https://github.com/apimentel-ECE/cBP-Tnet.git)) (accessed on 19 December 2024).  
The authors declare no conflicts of interest.
# Optimal Mixture Designs for Heavy Metal Encapsulation in Municipal Solid Waste Incineration Fly Ash
## 3 Results and Discussion  
### Compressive Strength Evolution  
The compressive strength evolution of the mixtures during 90 days of hydration is provided in Figure 2. Three replicate paste cubes were tested at each age. They generally showed less than 5% coefficient of variability in the values of the strength. The strengths increase over time, though the rates of increase and the values vary depend on the mixture design. The early age strength of the LB and GB groups is very low, with 3-day values lower than 10 MPa. However, these groups show a sharp increase in strength from 3 to 28 days and the strength keeps increasing from 28 to 90 days. In terms of the 3- and 28-day strength, C28B42 has the largest values of 46 MPa and 52 MPa, respectively. Apart from the LB and GB groups, mixtures B30M60 and B50M40 also show low early strength. Low early strengths occur when there are high amounts of MSWIFA or LFS in the mixtures; strength generally decreases as the amounts of MSWIFA or LFS increase. For MSWIFA, strength reductions are caused due to its inert nature (no amorphous content and no reaction in cementitious systems) in addition to the deleterious species in the ash (including chloride, sulfate, and metallic aluminum [15; 21]). The negative effects of LFS are likely due to the presence of mayenite which can cause flash set and affect sulfate balance, apart from impeding later age reactions and causing conversion phenomena [13; 20; 21; 25; 26].  
Mixtures with high amounts of cement and GGBFS gain the most strength in both short- and long-term hydration. As the amount of cement decreases and the amount of GGBFS increases, the strength first increases and then decreases at later ages. This suggests that there is an optimal balance between cement and GGBFS (42% cement and 28% GGBFS) for later age strength. This is because of the highly reactive nature of the GGBFS, which will continue to react in cementitious systems, even at later ages, which reduces the porosity and result in the strength increasing [10]. As the amount of LFS increases, the strength decreases at 90 days, when compared to mixtures with lower amounts of LFS. Gypsum does not appear to have an obvious impact on the strength evolution.
## 2 Related Work  
### Indoor AR Navigation Technologies  
According to [PERSON] et al.'s survey [18], AR has been explored as a method of spatial navigation, both indoors and outdoors, over the past two decades. Research interest has risen significantly since 2010, as the ubiquity of smartphones and similar handheld devices has lowered the technical knowledge threshold for AR application development. More than half of the 60 publications that can be applied in indoor environments use general mobile devices, while others use custom devices or dedicated AR devices like head-mount displays (HMD). [PERSON] et al. [19] proposed an indoor navigation developed on HoloLens, focusing on the use of virtual semantic landmarks. Their follow-up research introduces a general framework using mixed-reality technology for indoor navigation assistance for user studies [20], which is mainly designed for HMD. However, we argue that the current ubiquity of hand-held devices like smartphones will continue to exist, and it still takes time for HMD to achieve the same popularity. Thus, we chose smartphones as our main target device for this research.  
Positioning and view alignment are two of the essential components in AR navigation. Technologies used for positioning in indoor navigation mainly include Wi-Fi, Bluetooth, inertial sensors, and cameras [8; 9; 10; 11; 12]. View alignment overlays directions and other visualized information for navigation on the view of the real world using cameras and sensors. The two components are often developed separately. For example, ARBIN, developed by [PERSON] et al. [21], is designed to enhance navigation in large indoor spaces like hospitals, airports, and train stations, with AR used for display directions and navigation clues, while positioning depends on Bluetooth Low Energy (BLE) beacons. However, such approachesdepend on additional infrastructures installed in the building. The cost and accessibility of the infrastructure can hinder the practical application of indoor AR navigation.  
Faced with the challenge, infrastructure-free indoor navigation technologies are being developed. [PERSON] et al. [22] proposed a solution based on a fusion of smartphone sensors, considering texting and pocket-carrying modes, but AR applications are not in the scope of this research, and smartphone cameras are not utilized. As cameras can be used for both positioning and view alignment, it is natural to consider utilizing the HHD's camera as the main sensor (often associated with other built-in sensors) for low-cost solutions. QR code-based AR applications [3] can be considered a typical example of such solutions, and the image marker is not necessarily restricted to QR codes. [PERSON] et al. developed an indoor navigation system with mobile phone cameras by detecting unobtrusive fiduciary markers [23] before the emergence of contemporary smartphones. However, setting up and maintaining additional markers like QR codes can still be labor-intensive and not purely infrastructure-free, which is pursued by this research.
# Effect of Fuel Mass Flow at the End of Injection on Cavitation and Gas Ingestion in the Nozzle
## 5 Conclusions  
In this paper, a high-speed framing camera was applied to capture fuel flow images in the nozzle consisting of a single straight orifice and a cone-shaped sac. In addition, a two-phase three-component numerical model was proposed, and the geometric size, initial conditions, and boundary conditions of the experimental nozzle were completely copied into the numerical model. The flow field of the EOI and AI inside the nozzle was calculated by OpenFOAM. Furthermore, because it is difficult to calculate the movement of the needle valve in OpenFOAM, the method of dynamic mass flux was used to replace  
Figure 12: Q = 2 \(\times\) 10\({}^{9}\) iso-surface, streamline, and \(\alpha\) = 0.25 vapor iso-surface in the nozzle at different mass fluxes.  
needle valve movement to guarantee the rationality of the calculation result, which was consistent with the experimental results. Furthermore, the effects of different mass fluxes on gas ingestion and cavitation at the EOI and AI were analyzed. Some conclusions were as follows:  
1. Cavitation and gas ingestion occurred sequentially in the nozzle, starting with the orifice and finishing in the sac. The sac played a major role in affecting the total amount of ingestion gas and cavitation. The maximum fuel inertial outflow and cavitation in the nozzle were crucial in determining the total fuel outflow.
2. The pressure difference between the pressure of the cavitation bubble and that of atmospheric pressure caused the cavitation collapse after the EOI, further generating external air to flow back into the nozzle. In addition, the gas ingestion bubbles in the nozzle began to fuse under the action of the surface tension after the EOI, and finally, its shape became regular.
3. The cavitation in the sac was mainly annular, and the orifice was mainly bulk cavitation. The cavitation in the sac concentrated on the nozzle inlet and developed downward. This was attributed to the fact that larger fuel mass increased the amount of gas ingestion and cavitation, and resulted in a longer deformation and fusion time for ingestion bubbles.  
Conceptualization, H.W.; Funding acquisition, H.W.; Investigation, Y.J.; Methodology, J.M.; Writing--Original draft, J.M.; Writing--Review & editing, Y.J. 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, grant number 5146009.  
Not applicable.  
The data presented in this study are available in article.  
The authors declare no conflict of interest.
# Analysis and Prediction of Universities' Buildings' Renovation Costs Using a Regression Model
## 5 Conclusions  
One of the most important tasks of building owners and building managers is to keep the buildings in a proper technical condition. This, in turn, requires defining a building maintenance strategy, including necessary repairs, refurbishments, and upgrades. All these activities are associated with incurring costs. Rational renovation management is particularly important in the case of public buildings, where legal regulations regulate the proper spending of funds.  
In this article, a model based on regression analysis, that will calculate the predicted annual renovation costs in educational buildings, was created. The model was built based on the results of research carried out in two stages. In the first stage, 17 buildings of 1 of the universities in Cracow were analyzed. The second phase was more detailed, and the final database was wider and covered 55 buildings, located in Cracow and Lodz. To predict future cost expenditure, a cumulative year-on-year bivariate linear regression model was calculated, and the Statistica software was used. The trend line equations, which allow to predict the future cost expenditures in the accurate short term, have been determined. The developed model can be used by owners and managers of public buildings in the proper planning of expenses related to the maintenance of facilities. The limitation of the model is that it can only be applied to educational buildings.  
The authors plan to further expand the database, which will allow for confirmation and possible modification of the regression model. Further testing of the model will make it possible to determine the necessary modifications needed to adapt the model to other types of public buildings and not just educational ones.  
Conceptualization, E.P.; methodology, E.P.; validation, J.G.; resources, J.G.; writing--original draft preparation, J.G.; writing--review and editing, E.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 8: Specification of values for annual renovation cost prediction and annual renovation cost predictions for an exemplary building.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 4 Hardware Evaluation for Iterative Decoding of Product Codes
### Hardware Evaluation Platform  
In this subsection, the structure of the hardware evaluation platform is illustrated in Figure 9. All the source bits, AWGN, and error recorder module are implemented in field programmable gate array (FPGA). In the following, the encoder and decoder architecture on the hardware platform are reported, respectively. For the LDPC decoder, the offset min-sum (OMS) algorithm is used and the fixed-point quantization bit width is six for prior information and extrinsic information. Using this hardware evaluation platform, on one hand, we can accelerate the performance simulation. On the other hand, we can evaluate the effects of fixed-point implementation of the decoding algorithms. Therefore, we can quickly obtain more practical results for applications.  
Figure 8: Iterative decoding performance of the product code with LDPC (576, 288) and Hamming (1023, 1013).
## 3 Results and Discussion
### Isotopic Results
To emphasize the isotopic difference between wild and cultivated berries, Figure 3 shows the groups of blueberries that grew in a natural environment versus those coming from conventional production. In Romania, in the Transylvanian region, where the investigated samples were collected, wild blueberries grow naturally in spontaneous flora, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Berry Type**} & \multicolumn{3}{c}{\(\mathbf{\delta^{2}H_{VSMOW}}\) **(\%)**} & \multicolumn{3}{c}{\(\mathbf{\delta^{18}O_{VSMOW}}\) **(\%)**} \\ \cline{2-7}  & **Min** & **Max** & **Mean** & **Min** & **Max** & **Mean** \\ \hline
**Raspberry** & \(-49.0\) & \(-31.6\) & \(-38.9\) & \(-4.8\) & \(0.4\) & \(-1.8\) \\
**Seaberry** & \(-38.2\) & \(-24.1\) & \(-31.7\) & \(-3.5\) & \(4.3\) & \(0.1\) \\
**Blackberry** & \(-36.2\) & \(-18.8\) & \(-29.4\) & \(-2.3\) & \(3.5\) & \(0.7\) \\ Cranberry & \(-37.2\) & \(-19.5\) & \(-28.7\) & \(-3.2\) & \(0.8\) & \(1.0\) \\
**Blueberry** & \(-34.3\) & \(-17.0\) & \(-25.3\) & \(-1.2\) & \(4.9\) & \(2.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Isotopic signature of \({}^{2}\)H and \({}^{18}\)O for studied samples. Figure 2: The \({}^{13}\)C signature of the investigated berries. in forested areas, and exposed rock outcroppings found on hills or mountains, without irrigation, cultivation, or use of additional fertilizers. For these reasons, wild blueberries have a more intense flavor and are much smaller in size than cultivated ones [7]. In contrast, cultivated blueberries contain much more water (to keep roots moist throughout the growing season) than do the wild berries, preferring acidic soil and full sun [36]. It can be observed that one blueberry sample from the cultivated group fits the isotopic signature of the wild blueberries group. This sample, as indicated on the label, comes from a farm located at the foothills of the Fagaras Mountains, Breaza village (45\({}^{\circ}\)42\({}^{\prime}\)20\({}^{\prime\prime}\) N 24\({}^{\circ}\)53\({}^{\prime}\)00\({}^{\prime\prime}\) E), Brasov County, at 610 m altitude (Figure 3). In this region, the vegetation is characterized by an altitudinal layering, well expressed, but with obvious local contrasts, determined by the lithological, edaphic, and topo climatic particularities [37]. For this cultivated sample, the natural environmental conditions overlapped with those specific to a farmed blueberry (cool, humid climate and can be cultivated up to altitudes of 600-800 m; supports low temperatures down to \(-\)25 \({}^{\circ}\)C in winter, preferring well-drained soils, without excess moisture) [38]. Additionally, in Figure 3, it can be observed that another blueberry sample, sold as \"wild\", overlaps with the cultivated blueberry group. According to the data on the label, this sample was collected on the same day and location as its \"neighbor\" sample on the graph, having similar isotopic values. Thus, it can be considered that for this sample there is a false declaration of the growing regime.
# A Method for Ferulic Acid Production from Rice Bran Oil Soapstock Using a Homogenous System
## 2 Materials and Methods
### Homogeneous Hydrolysis of \(\gamma\)-oryzanol
#### 2.3.2 Hydrolysis Process by Heating Method  
The \(\gamma\)-oryzanol solutions of concentration 48, 80, and 96 mg/mL were prepared by dissolving 4.8, 8.0, and 9.6 g, respectively, of \(\gamma\)-oryzanol in 100 mL of EtOAc. The aqueous KOH solutions of concentration 240, 384, 400, 480 mg/mL were prepared by dissolving 12, 19.2, 20, 24 g, respectively, of KOH in an aqueous solution (50 mL).  
The effects of \(\gamma\)-oryzanol concentrations on the yield of FA were explored when the ratio of KOH/ \(\gamma\)-oryzanol was \(10/1\) (\(wt/wt\)). \(\gamma\)-oryzanol solutions of various concentrations (48, 80, and 96 mg/mL, 16 mL) were added to EtOH (80 mL) in a reaction glass vessel as shown in Figure 1a. The vessel has two necks and flat base bottom (inner diameter of 6.5 cm, 21.5 cm in height, and thickness of 2 mm) [27]. The vessel was placed in a water bath controlled at 75 \({}^{\circ}\)C using a SDT-04P thermal controller (Sansyo, Osaka, Japan), and held for more than 5 min. Aqueous KOH solutions of various concentrations (240, 400, and 480 mg/mL, 32 mL) were added to the vessel to prepare solutions with different initial concentrations of \(\gamma\)-oryzanol in the mixture (6, 10 and 12 mg/mL). After being dispersed uniformly, the vessel was held in the water bath without stirring and without acceleration by ultrasonic irradiation (hereinafter called the heating method).  
In order to investigate the effect of ratio of KOH/ \(\gamma\)-oryzanol (KO ratio) on the FA yield, the \(\gamma\)-oryzanol concentration in the reaction mixture was 12 mg/mL, and the concentration of KOH solution was varied as follows. A \(\gamma\)-oryzanol solution (96 mg/mL, 16 mL) was added to ethanol (80 mL) in a reaction vessel. After mixing, the vessel was placed in a water bath controlled at the temperature of 75 \({}^{\circ}\)C and held for more than 5 min. Aqueous KOH solutions of concentrations (240, 384, and 480 mg/mL, 32 mL), corresponding to KO ratios of 5/1, 8/1, and 10/1, were added.  
In addition, the effect of the cosolvent ratio was examined by varying the ratio of EtOH/EtOAc (\(v/v\)) (EH/EA ratio) from 2/1 to 9/1 while maintaining the KO ratio 10/1 and initial concentration of \(\gamma\)-oryzanol 12 mg/mL.  
For determination of the reaction products, a 100-\(\mu\)L aliquot of mixture was taken out and mixed with concentrated acetic acid (100 \(\mu\)L) to terminate the reaction every 30 min. The mixed solution was diluted to 1.5 mL with ethanol and an aliquot (10 \(\mu\)L) was subjected to HPLC analysis. The reaction was monitored for 4 h.
# Influence of the Surface Texture Parameters of Asphalt
## 3 Results and Discussion
### Optical Reflection Characterization of Field Asphalt Pavement Based on Texture Parameters
#### 3.2.1 Modeling of the Influence of Single-Texture Index Factors on Light-Reflection Characteristics  
Considering the functional relationship between the texture index and the retroreflection coefficient of asphalt-mixture specimens before and after the indoor rutting test, and  
Figure 8: Correspondence between texture index and R\({}_{\text{L}}\). (**a\({}_{1}\)–g\({}_{1}\)**) Correlation between S1, D\({}_{1}\), S\({}_{2}\), D\({}_{2}\), \(\Delta\)q, R\({}_{\text{sk}}\), R\({}_{\text{ku}}\), and R\({}_{\text{L}}\) for rutted specimens before rolling. (**a\({}_{2}\)–g\({}_{2}\)**) Correlation between S1, D\({}_{1}\), S\({}_{2}\), D\({}_{2}\), \(\Delta\)q, R\({}_{\text{sk}}\), R\({}_{\text{ku}}\), and R\({}_{\text{L}}\) for rutted specimens after rolling.  
Figure 9: Correspondence between texture index and R\({}_{\text{L}}\).  
based on the macroscopic and microscopic texture parameters and retroreflection coefficient obtained from the eight onsite asphalt-pavement tests, a quadratic polynomial relationship model between individual texture parameters; the retroreflection coefficient was constructed to analyze the effect of individual texture index factors on the light reflectance characteristics. Specific results are shown in Figure 10.  
Figure 10: _Cont._  
Figure 10 shows that the macrotexture surface area S\({}_{1}\), microtexture surface area S\({}_{2}\), macrotexture distribution density D\({}_{1}\), microtexture distribution density D\({}_{2}\), root mean square slope \(\Delta\)q, skewness R\({}_{\rm{sk}}\), and kurtosis R\({}_{\rm{ku}}\) for a series of macroscopic and microscopic texture parameters of the actual asphalt pavement and retroreflection coefficient R\({}_{\rm{L}}\) are all quadratic polynomial correlations, and the R\({}^{2}\) of the constructed single-factor influence model reaches more than 0.90. The results show that the retroreflection coefficient can be effectively predicted based on the texture parameters of asphalt pavement. Specifically, the correlation models of S\({}_{1}\), S\({}_{2}\), D\({}_{1}\), D\({}_{2}\), and R\({}_{\rm{L}}\) all have R\({}^{2}\) above 0.95, indicating the effective prediction of the light-reflection characteristics.  
.2.2 Modeling of the Influence of Multitexture Index Factors on the Light-Reflection Characteristics  
To further investigate the influence of the asphalt-pavement texture indices on the reverse reflection coefficient, we deeply analyzed and mined all of the macro- and micro-texture parameters and reverse reflection coefficients collected from asphalt pavement in the field and the retroreflection coefficient R\({}_{\rm{L}}\) with the help of Weka 3.8.5 version software and constructed linear and nonlinear models between the two. When the absolute value of the correlation coefficient is greater than or equal to 0.8, it can be considered that the linear correlation between the two variables is high.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 2 Cubesat Specifications and Constraints
### Mechanical Subsystem  
The Mechanical subsystem provides structural support for the entire satellite. The materials used must have high strength and very low outgassing properties. High-quality aluminum alloys are often used. It includes the supporting frame of the nano-satellite, and supporting fixtures for various subsystems and fasteners. The structure and other mechanical components should be designed to support the entire satellite to withstand the different launch environment loads such as the quasi-static loads and the different vibrations.