esa-sceva/satcom-chunk-collection huggingface dataset:
# BIM Application in Multi-Criteria Assessment of
## 1 Introduction  
Offsite construction is becoming more popular nowadays, and an increasing number of construction products are manufactured and controlled out of site. Although previous studies have shown that offsite production could improve construction quality and save time and money for buildings of any architectural style [1], the current implementation of BIM (building information modeling) in prefabricated construction is far from being perfect and correct [2]. BIM methodology with precise model information and quantity take-offs can be used in alternative evaluation of any construction elements or buildings, but it is very effective for use in precise design and time-consuming construction works such as suspended brickwork facade. BIM can be used effectively in brickwork facade projects, providing accurate models, detection of clashes, material takeoff, construction sequencing works, 3D visualization connected with workshop design, and other project documentation. Due to the complexity of brickwork facades as architectural products, BIM can implement the production data of support elements for brickwork and a brickwork architectural facade from a prepared BIM model. Knowing that the support elements of a brickwork facade can be manufactured based on a BIM model design, and that thereconstruction of the facade requires actual measurements on site, it is important to request and develop new manufacturing products.  
The main novelty of this research is its practical application of building information modeling (BIM) and multiple-criteria decision making (MCDM) for the effective evaluation of suspended brickwork facade production. This paper proposes a BIM-based production for reconstructing a brickwork facade using a BIM building construction (structural) model and applying survey data.  
This paper focuses on the Wroblewski Library of the Lithuanian Academy of Sciences in Vilnius (Lithuania). The library was built at the beginning of the previous century. Using old 2D paper design drawings of building, the precise site measurements and the application of the BIM methodology led to a new BIM model for building reconstruction with a detailed brickwork facade design. Then, a brickwork-facade BIM model was prepared to be compatible with heritage conservation requirements. By identifying the brickwork support elements, masonry brick for the facade manufacturing operations was calculated for trial production. After the success of the trial production from the BIM model, the MCDM (multiple-criteria decision making) methodology was applied and three types of installation of brick facades were analyzed, aiming to promote the most cost-effective production method.  
The structure of this paper consists of a literature review presented in Section 2; the research methodology is presented in Section 3; the case study and the research results are described in Section 4; the discussion is provided in Section 5; and, finally, the conclusions are presented in Section 6.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 3 Result and Discussion
### Heat Loss Suppression for Scalable SCE
#### 3.4.1 Energy Flow Analysis  
To achieve the heat loss suppression of the scalable SCE process, the energy flows distribution of absorber-emitter and water surface was obtained. As solar absorber temperature increase, the heat transfer process is initiated. For the solar-absorber, the energy flows mainly include energy input \(Q_{\text{in}}\), energy transferred to the water surface by heat  
Figure 10: (**a**) \(T_{\text{ae}}\), \(T_{\text{wat+}}\) and evaporation rate under different air layer thickness, with the same hole diameter (\(d\) = 1.1 mm) and hole area ratio (\(\varphi\) = 14%). (**b**) The temperature difference between the water surface and infrared emitter surface and the proportion of heat flow under different air layer thickness.  
Figure 9: \(T_{\text{ae}}\), \(T_{\text{wat+}}\) and evaporation rate under different hole diameters, with the same hole area ratio (\(\varphi\) = 20%) and air layer thickness (\(\delta\) = 2.0 mm).  
conduction \(Q_{\text{cond}}\) and heat radiation \(Q_{\text{rad}}\), the heat losses by convection \(Q_{\text{Lconv1}}\) and thermal radiation \(Q_{\text{Lrad}}\) from the absorber surface transmit to the ambient and the convective heat losses \(Q_{\text{Lconv2}}\) resulting in the heat transfer between the vapor and high-temperature absorber-emitter. For the water surface, the energy flows mainly include: \(Q_{\text{cond}}\), \(Q_{\text{rad}}\), latent heat of evaporation \(Q_{\text{v}}\) and the conduction heat loss \(Q_{\text{Lcond}}\) dissipated to the ambient through bulk water. For scalable SCE, the energy balance on solar absorber surface and water surface are shown in Equations (15) and (16).  
\[Q_{\text{in}}=Q_{\text{cond}}+Q_{\text{rad}}+Q_{\text{Lconv1}}+Q_{\text{Lconv2} }+Q_{\text{Lrad}} \tag{15}\]  
\[Q_{\text{cond}}+Q_{\text{rad}}=Q_{\text{v}}+Q_{\text{Lcond}} \tag{16}\]  
Figure 11 shows the energy flows distribution of absorber-emitter and water surface during heat transfer in scalable SCE process. Two key conclusions could be derived from the results. The first one is that two major heat-loss contributors can be identified: heat dissipated to the ambient through the bulk water \(Q_{\text{Lcond}}\) (16.2%) and heat losses by convection \(Q_{\text{Lconv1}}\) from the absorber surface transmit to the ambient. The other is that the obtained heat of the water surface is mainly comes from the heat conduction (37.1%) rather than the radiation (17.4%) from the infrared emitter. The above analysis indicates that the evaporation rate of the scalable SCE could be effectively enhanced by proper ways to suppress the heat loss of \(Q_{\text{Lcond}}\) and \(Q_{\text{Lconv1}}\) or improve the efficiency of heat conduction in air layer.
## 4 Solution Method
### Black-Box Modeling of UAV Travel Time on an Arc  
In the context of the routing problem, determining the time required for a UAV to traverse an arc \((i,j)\) is key for ensuring route feasibility. This calculation involves multiple interacting factors, including UAV speed, wind conditions, payload weight, and arc geometry. These factors interact in a nonlinear and context-dependent manner. Due to this complexity, we treat this calculation as a 'black box' (emulation of reality), whose exact behavior is not directly accessible by the algorithm but is based on the following principles.  
The UAV's initial speed \(v_{\text{drone}}\) is reduced as a function of the payload weight \(w_{\text{payload}}\). This reduction follows a nonlinear relationship proportional to the square of the payload weight:  
\[v_{\text{adjusted}}=v_{\text{drone}}-(k\cdot w_{\text{payload}}^{2}) \tag{7}\]  
where \(k\) is a coefficient that captures the impact of weight on speed.  
Figure 6: Interaction between the A* and the neural network predictor.  
Wind conditions, both in magnitude and direction \((v_{\text{wind}},\theta_{\text{wind}})\), significantly affect the UAV's effective speed when traversing an arc \((i,j)\). These effects are modeled by considering the vector components of both the wind and the UAV:  
\[v_{\text{resultant}}=\sqrt{(v_{\text{drone},x}+v_{\text{wind},x})^{2}+(v_{\text{ drone},y}+v_{\text{wind},y})^{2}} \tag{8}\]  
where  
\[v_{\text{drone},x} =v_{\text{adjusted}}\cdot\cos(\theta_{\text{path}}) \tag{9}\] \[v_{\text{drone},y} =v_{\text{adjusted}}\cdot\sin(\theta_{\text{path}}) \tag{10}\]  
and  
\[v_{\text{wind},x} =v_{\text{wind}}\cdot\cos(\theta_{\text{wind}}) \tag{11}\] \[v_{\text{wind},y} =v_{\text{wind}}\cdot\sin(\theta_{\text{wind}}) \tag{12}\]  
The arc \((i,j)\) is decomposed into smaller segments, and for each segment, the Euclidean distance and required time are computed based on the resultant ground speed.  
\[t_{\text{segment}}=\frac{d_{\text{segment}}}{v_{\text{resultant}}} \tag{13}\]  
The total travel time for an arc is the sum of the travel times for all its segments.  
\[T_{ij}=\sum_{k}t_{\text{segment},k} \tag{14}\]
## 3 Data and Methods
### Model Optimization  
For the SVR algorithm, the penalty parameter and kernel parameter both directly affect the prediction error. Researchers have studied SVR regression and put forward some suggestions, but many contradictory opinions still exist. In this study, to reduce the SVR prediction error, the grid search (GS) method [24], Bayesian optimization algorithm (BOA) [25; 26; 27], and genetic algorithm (GA) [28; 29] were used to optimize the penalty and kernel parameters, after which the optimized SVR algorithm was used to train and predict the relevant data. In this study, RBF was specified as the kernel function in Python, the scope of the penalty coefficient was [0.1, 100], and the range of the kernel parameter was [0.01, 1].
## 3 Results
### Validation of CBH Using Ground-Based Ka-Band MMCR Measurements  
The ground-based Ka-band MMCR measurements from four stations in China, including Hongyuan, Yichang, Taiyuan, and Jiuquan, are used to evaluate the performance of AHI CBH estimates across the diurnal cycle. The geolocations and elevations of the four stations are provided in Table 4. The MMCR measurements are compared with the average of the AHI CBH estimates of the four pixels closest to the MMCR. Figure 9 shows four examples with diverse cloud types in November 2022. Our CBH estimates typically agree with those determined by MMCR, especially when the CTH differences are small. Moreover, the performance of CBH retrievals varies depending on cloud types and vertical structures. The retrieval algorithm performs better for low clouds compared to high and middle clouds. It should be noted that although MMCR provides high temporal and vertical resolution cloud information for validation, its CBH measurements may still be affected by radar signal attenuation, especially in the presence of precipitation or thick hydrometeors [33]. Compared with the existing algorithms that only apply to daytime satellite observations, the proposed algorithm demonstrates the capability of estimating CBH under both daytime and nighttime conditions. This advancement significantly enhances the temporal continuity of satellite-based CBH products and provides a more comprehensive representation of cloud vertical structures for weather forecasting applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station Name** & **Latitude** & **Longitude** & **Elevation** \\ \hline Hongyuan & 32.80\({}^{\circ}\)N & 102.55\({}^{\circ}\)E & 3493 m \\ Yichang & 30.73\({}^{\circ}\)N & 111.30\({}^{\circ}\)E & 134 m \\ Taiyuan & 37.78\({}^{\circ}\)N & 112.55\({}^{\circ}\)E & 780 m \\ Jiuquan & 39.77\({}^{\circ}\)N & 98.48\({}^{\circ}\)E & 1478 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Geolocations and elevations of the four ground-based Ka-band millimeter-wave cloud radar stations.  
Figure 8: Panels (**a,b**) show the pixel number distribution of CTH and CGT detected by the Earth-CARE CPR. Panels (**c,d**) show the bias of the AHI CBH retrievals with constrains of EarthCARE CTH and CGT.  
Figure 9: _Cont._
## 5 Conclusions  
The results obtained in this research show the capacity of landfill leachate as a source of nutrients to produce algal biomass with specific metabolites. In this case, both strains were able to grow in media with up to 5% (\(v/v\)) leachate with a high concentration of biomass and removal of 80% (\(w/w\)) of NO\({}_{3}\), and 50% (\(w/w\)) of PO\({}_{4}\). However, higher levels of leachate (10% \(v/v\)) proved to be toxic for the strains. Regarding the carbon source, the results show that _Chloella_ sp. and _Scenedesmus_ sp were able to increase the final concentration of biomass using both carbon sources, with sodium bicarbonate (NaHCO\({}_{3}\)) being the carbon source that achieved the most significant increase in final biomass content (up to 1.23 g\(\cdot\)dm\({}^{-3}\)), with a control of 0.68 g\(\cdot\)dm\({}^{-3}\)) in _Chloella_ sp. On the other hand, the most significant production of metabolites such as carotenoids occurred when Na\({}_{2}\)CO\({}_{3}\) was used in both strains at a concentration of 0.8 g\(\cdot\)dm\({}^{-3}\). In the case of lipids, NaHCO\({}_{3}\) in _Scenedesmus_ sp obtained the highest values at 1.2 g\(\cdot\)dm\({}^{-3}\), while in _Chloella_ sp. Na\({}_{2}\)CO\({}_{3}\) at 1.2 g\(\cdot\)dm\({}^{-3}\) achieved the highest lipid production. Also, the highest production was observed for obtaining carbohydrates and proteins when using NaHCO\({}_{3}\) at 1.2 g\(\cdot\)dm\({}^{-3}\). For carbohydrates, both strains used NaHCO\({}_{3}\) at that concentration, while for proteins, only _Scenedesmus_ sp. achieved the highest value.  
Conceptualization, S.J.B. and A.F.B.-S.; methodology, J.B.G.-M., M.S.H.-O. and J.J.O.-B.; software, A.F.B.-S. and A.Z.; validation, N.A.U.-S. and G.L.L.-B.; formal analysis, J.J.O.-B., M.S.H.-O. and A.Z.; investigation, A J.J.O.-B. and M.S.H.-O.; resources, A.F.B.-S. and S.J.B.; data curation, A.Z.; writing\(-\)original draft preparation, J.B.G.-M.; writing\(-\)review and editing, A.F.B.-S.; visualization, G.L.L.-B.; supervision, N.A.U.-S.; project administration, A.F.B.-S. and S.J.B.; funding acquisition, A.F.B.-S. and S.J.B. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Newton-Caldas Fund Institutional Links, with the project \"ALLALLCOLOR: BIO-PLATFORM FOR THE SUSTANABLE PRODUCTION OF CYANOBACTERIAL-BASED COLOURS AND FINE CHEMICALS\" ID 527624805.  
Not applicable.  
We would like to express our sincere gratitude to Universidad Francisco de Paula Santander (Colombia) for providing the equipment for this research and the Colombian Ministry of Science Technology and Innovation MINCIENCIAS for the support to national Ph.D. Doctorates through the [PERSON] scholarship program.  
The authors declare no conflict of interest.
## 4 NBI Suppression Algorithm
### Cyclic Cascaded High Order Complex-Coefficient Matrix Adaptive Notch Filter
Through the aforementioned construction, the cyclic cascaded high order adaptive notch filter has the following excellent properties: (1) all of the weight coefficients are sought simultaneously as depicted in the left column in Figure 4, alleviating the associated polynomial deflation; (2) a priori information or constraints on the zero-pole locations are easily incorporated into the estimation procedure; (3) the iterations utilizing the gradient-descent algorithm have been proven to be relatively effective with a minimum computational complexity; and (4) the imperfect knowledge of the practical ultra-wide band channel has tiny effects on the proposed narrow band interferences suppression since the interference components will be cleaned off rather than be recovered.
# Thorium Recovery with Crown Ether-Polymer Composite Membranes
## 3 Results and Discussion
### The Process Performance of the Prepared Membranes
#### 3.2.2 The Retention over the pH and the Concentration of the Thorium Ion Solution  
At a working pressure of 8 atmospheres and an agitator rotation speed of 200 rpm, the retention variation according to pH was followed for the two membranes, at concentrations of \(10^{-4}\) mol/L and \(10^{-5}\) mol/L thorium nitrate in ultrapure water (Figure 10).  
In all cases studied, the retention increases with the increase in pH. In the case of the sPEEK membrane, the retention is between 85% and 90% with a uniform increase over the entire pH range. The retention values for the sPEEK membrane are higher for the \(10^{-5}\) mol/L concentration solution compared to the \(10^{-4}\) mol/L concentration solution.  
The thorium retention for the ABCE-sPEEK composite membrane varies between 90% and 98%, being higher for the \(10^{-5}\) mol/L concentration solution than for the \(10^{-4}\) mol/L concentration solution, over the entire pH range. In the case of the ABCE-sPEEK composite membrane, for both thorium concentrations studied, a jump in retention was found at pH 2. This observation is correlated with the acidity constant of sPEEK, which is between 2.0 and 2.2 (Table 2), but also with the presence of the crown ether in the form of the ammonium ion. A plausible explanation would be that the acidic, molecular form of sPEEK (R-SO\({}_{3}\)H) passes into the basic, anionic form (R-SO\({}_{3}\)\({}^{-}\)) that interacts more strongly with the thorium  
Figure 9: Dependence of permeate flux (J) on: (a) working pressure; (b) stirring regime (rpm).  
tion. At the same time, the crown ether specifically complexes the thorium ion, causing an increase in the retention of the ABCE-sPEEK composite membrane compared to the sPEEK membrane.  
The previous considerations justify the superior retention of the ABCE-sPEEK composite membrane compared to the sPEEK membrane. Theoretically, for the ABCE-sPEEK composite membrane, which is more porous (Figure 4a) and has a higher flow than the less porous sPEEK membrane (Figure 3a), it would have been expected to present a lower retention.
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 2 Materials and Methods
### Multi-Stage Hierarchical Temporal Refinement  
We can utilize a hierarchical temporal refinement scheme to calculate the time of closest approach with high accuracy. The procedure involves minimizing the scalar distance function \(d(t)\) over a search interval. At each stage, we establish a uniform time grid \(\{Nt_{0},t_{1},t_{2},\ldots,t\}\) with a fixed step size \(\Delta t\), evaluate the relative distance \(d(t_{i})\), and identify the global minimum on that grid [19].  
\[t_{\text{TCA}}=\text{argmin}_{t_{i}}\left\|\overset{\rightarrow}{r}_{2}(t_{i} )-\overset{\rightarrow}{r}_{1}(t_{i})\right\| \tag{11}\]  
Let the initial interval be \([t_{\text{start}},t_{\text{end}}]\) spanning 120 h with step size \(\Delta t\) = 30 s.  
The minimum is computed over  
\[\{t_{i}=t_{\text{start}}+i\cdot\Delta t\}_{i=0}^{N} \tag{12}\]  
The result from Equation (1), denoted \(t_{\text{TCA}}^{(1)}\) is used to define a new interval:  
\[\left[t_{\text{TCA}}^{(1)}-10\text{mints},\ t_{\text{TCA}}^{(1)}+10\text{mints}\right] \tag{13}\]  
with \(\Delta t=1\)s, from which \(t_{\text{TCA}}^{(2)}\) is extracted as the global minimum of \(d(t)\) as defined in Equation (11). From \(t_{\text{TCA}}^{(2)}\), a final refinement is carried out using  
\[\left[t_{\text{TCA}}^{(3)}-3\text{s},\ t_{\text{TCA}}^{(3)}+3\text{s}\right] \tag{14}\]  
with \(\Delta t\) = 0.0001 s. This produces the final time of closest approach \(t_{\text{TCA}}^{(4)}\).  
The minimization is performed using brute-force enumeration over uniformly spaced time steps:  
\[t_{TCA}=\underset{(t\in\tau)\mathcal{T}}{\text{argmin}}d(t) \tag{15}\]  
where \(\tau\) is the time grid for that stage. Each stage involves brute-force evaluation of the distance function at uniformly spaced time steps, without interpolation or gradient-based optimization, to avoid local minima caused by SGP4's discrete nature.
## 3 Data Quality Analysis 2 (DQA2): Satellite Clock Correction Difference When Ephemeris Updates  
The satellite clock correction which calculates from the ephemeris must also be analyzed. Figure 7 shows that different ephemeris will broadcast different clock corrections for one satellite. Equation (1) presents the calculation of the satellite clock correction. The \(\mathrm{a_{f0}}\), \(\mathrm{a_{f1}}\) and \(\mathrm{a_{f2}}\) are the polynomial coefficients given in the ephemeris data; t-\(\mathrm{t_{oc}}\) presents the time transmit from the satellite to the user. The \(\mathrm{\Delta t_{r}}\) is the relativistic correction term shown in Equation (2). The F, e and A are also given in the ephemeris data. The calculation of \(\mathrm{E_{k}}\) is defined in the ICD [2,4]. The coefficient of the orbit parameters are not the same value due to the fact that the coordination systems of GPS and BDS are different. However, both systems use the same equation to calculate the satellite clock correction.  
Figure 6: The broadcasted BDS URA index values for one week.  
Figure 7: Schematic diagram of DQA2.  
Figures 8 and 9 show the clock correction difference as calculated from the different ephemeris data for the GPS and BDS, respectively. For all of the GPS satellites, the clock correction difference is under 0.5 m, and the standard deviation for each satellite is no more than 0.2 m. On the other hand, all the BDS satellite clock correction exhibit unstable changes when the ephemeris updates. The statistical results for all of the BDS satellite are shown in Table 4. The maximum clock correction difference for all BDS satellites is over 6 m, and the maximum value for each constellation is over one meter. This sudden change in the satellite clock correction will also influence user positioning when a BDS user receives a new ephemeris. This information shows the time synchronization for the BDS satellite still needs to be improved.  
If a BDS user wants to reduce satellite clock error, the user can followed the Wide Area Differential Global Navigation Satellite System (WADGNSS) to estimate a more accurate clock correction [12].
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 3 Novel Monomers Synthesized from Renewable Materials
### Grafting Acrylate  
Acryloyl chloride (AC) is a highly reactive monomer and usually used in esterification. The reaction activity of AC with alcohols is much higher than that of anhydride/acid. Cardanol is a phenolic lipid obtained from anacardi acid which is the main component of cashew nutshell liquid, a byproduct of cashew nut processing. Cardanol consists of different saturated and unsaturated long-chain phenols. The unsaturated functionality endows it potentiality to perform free-radical polymerization. The phenolic hydroxyl groups could be used for introducing more C=C bonds. The rigid structure of cardanol would effectively enhance the thermal stability of thermosets. [PERSON] et al. (2014) synthesized cardanyl acrylate (CA) from cardanol via reacting with AC for the modification of a castor oil-based polyfunctional polyurethane acrylate resin (Figure 3a). The \(T_{g}\), thermal stability, hardness, and hydrophobicity of the resins were greatly enhanced by the incorporation of CA. Therefore, CA can replace petroleum-based monomers to produce biobased coatings with special properties such as high biobased content and low shrinkage. [PERSON] et al. (2015) successfully synthesized a new polyfunctional acrylate monomer (COPERAA) from castor oil-based monoglyceride and AC (Figure 3b). The COPERA was used as a RD for AESO UV-curing materials. Compared to petroleum-based TMPTA, COPERAa endowed superior biobased content and volume shrinkage of the resulting AESO resins. AESO/COPERAA resins had lower hardness, thermal stability than AESO/TMPTA resins due to the longer and softer fatty acid chains. Similar designs have been reported by [PERSON] et al. (2014, 2015); they selected AC-grafted betulin as a comonomer for preparing AESO coatings with preferable performance (Figure 3c).  
Figure 3: Synthesis routes of (**a**) CA, (**b**) COPERAa and (**c**) acrylate betulin from cardanol, castor oil and betulin with AC (2014, 2015, 2015, 2016).
## 6 Results and Discussion
### Model Developed with Average Data Excluding Outliers
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{(**a**)} \\ \hline
**Model** & **MSE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline Random forest & 0.175 & 0.418 & 0.303 & 0.887 \\ \hline Neural network & 0.495 & 0.704 & 0.534 & 0.679 \\ \hline Gradient boosting & 0.027 & 0.166 & 0.094 & 0.982 \\ \hline AdaBoost & 0.041 & 0.204 & 0.101 & 0.973 \\ \hline \multicolumn{3}{c}{(**b**)} \\ \hline
**Model** & **MSE** & **RMSE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline Random forest & 0.145 & 0.381 & 0.288 & 0.893 \\ \hline Neural network & 0.367 & 0.606 & 0.493 & 0.730 \\ \hline Gradient boosting & 0.115 & 0.339 & 0.280 & 0.915 \\ \hline AdaBoost & 0.168 & 0.410 & 0.304 & 0.876 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: (**a**). Result of ML models with training average data excluding outliers. (**b**). Result of ML models with testing average data excluding outliers.