esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In their study, they employed a HPLC instrument equipped with an ultraviolet and evaporative light-scattering detector to determine the DPs of PMT. More recently, a new HPLC method for PMT degradation was developed and validated as per ICH guidelines using a design of experiments methodology equipped with Box-Behnken design by [PERSON] et al. [23]. As seen in all reported studies, in spite of the fact that HPLC is the most used technique to investigate the degradation and stability of drugs, it is not possible to perform the analysis in a single HPLC method. For this reason, mass spectroscopy is heavily used to complement HPLC for the characterization of degradation products. However, the MS-based techniques are expensive, time-consuming, and requires highly trained personnel. As an alternative, surface-enhanced Raman spectroscopy (SERS) has been gaining attention for drug analysis in recent years [28; 29; 30]. SERS has received growing interest in variety of areas due to its inherent merits such as high sensitivity down to a single molecule, intrinsic finger-printing property, and multiplexing features due to the narrow spectral bands. These features make SERS one of the most powerful techniques for non-destructive, in situ, in vitro and in vivo analysis of chemical and biological substances. A noble, gold or silver, metal nanostructured surface is  
Figure 1: Chemical structure of PMT. essential to achieve strong SERS activity. Although both noble metal surfaces are routinely used, AgNPs are preferred in some cases due to their higher SERS activity [31]. When colloidal metal nanoparticles are used as SERS substrates, the analyte of interest is simply mixed with the colloidal suspension and spotted on a suitable surface [32]. This study is the continuation of our effort to investigate the potential of SERS for the drug degradation profiles [28]. In our previous study, we monitored the degradation behavior of tofaitinib and methotrexate under hydrolytic, oxidative and thermal conditions using mesoporous Si@AgNPs as SERS substrate. We found that drug molecules and naked AgNPs surfaces might interact in the presence of HCl or H\({}_{2}\)O\({}_{2}\), and AgNP surfaces can result in undesired reactions due to their high chemical reaction [33]. We aimed the study at understanding the effects of naked AgNPs on PMT degradation with NaOH, HCl and H\({}_{2}\)O\({}_{2}\) exposure up to 48 h. This study will be first of its kind to date.
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 4 Prediction and Analysis
### Simulation Results Based on K-Means-QPSO-LSTM  
To verify the effectiveness of the K-means-QPSO-LSTM vehicle velocity prediction model proposed in this paper, a cyclic test scenario, the China Heavy-duty commercial vehicle Test Cycle-Tractor Trailer (CHTC-TT), which consists of three types of driving conditions, was used as the test scenario for verifying the prediction effect. The test cycle set is shown in Figure 15.  
As shown in Figure 16, the results of classifying the current driving condition type based on the past 10s of vehicle velocity are shown. The blue line represents the vehicle velocity, and the red line represents the type of driving condition. The sample used for clustering was the kinematic segment between two idle points, which is usually of a longer duration. The time horizon for collecting velocity information was 10 s, so the same kinematic segment will be divided into different types, especially in the time horizon near sudden acceleration or deceleration, where the fluctuation of the driving condition type is obvious.  
A simulation-based validation of the vehicle velocity prediction method based on kinematic segment identification in the typical cycle condition CHTC-TT is shown in Figure 17, where different vehicle velocity prediction methods are compared, and the prediction results are shown in Figure 17.  
As shown in Figure 17, compared with the vehicle velocity prediction method based on kinematic segment recognition, the prediction accuracy of the vehicle velocity methods with LSTM, RBF-LSTM, and CNN-LSTM was not ideal under various driving conditions such as idling, emergency acceleration, and emergency deceleration. Unlike the vehicle velocity prediction without kinematic segment recognition, the vehicle velocity prediction model based on K-means-QPSO-LSTM with driving condition type recognition, although unable to predict the sudden uncertainty of future driving information, could accurately identify the current driving condition based on the past driving feature parameters and update the driving condition type in real-time. This effectively solves the problem of fluctuating driving conditions. For the vehicle velocity prediction model with driving condition type recognition, the training samples of the sub-QPSO-LSTM were obtained  
Figure 16: Online driving condition type identification.  
Figure 15: CHTC-TT-based test cycle set.  
by classifying the feature parameters, which are more representative, so the prediction accuracy was better even during the stable driving period. In summary, the vehicle velocity prediction method based on K-means-QPSO-LSTM with kinematic segment recognition improved the prediction accuracy in both changing and stable driving conditions.  
Table 8 shows different prediction results of four vehicle velocity prediction models under the input of the CHTC-TT test cycle. It can be seen that the vehicle velocity prediction method based on kinematic segment recognition with K-means-QPSO-LSTM had the best prediction performance.
## 1 Introduction  
Superconductors are remarkable materials that exhibit the extraordinary property of conducting electrical current with zero resistance. This unique characteristic has led to a wide range of applications. As an example, Magnetic Resonance Imaging (MRI) systems are used globally as a crucial medical tool for producing detailed images of internal organs and tissues. In the face of increasing energy demands driven by renewable energy sources and innovations such as solar cars, superconductors hold great potential for efficient energy transfer.  
The elimination of electrical resistance in superconductors significantly reduces energy wastage during current transmission from one location to another. However, a major limitation of existing superconductors is their reliance on extremely low temperatures, known as critical temperatures (\(T_{c}\)), to achieve zero resistance. Conventional superconductors described by the Bardeen-Cooper-Schrieffer (BCS) theory (the first microscopic theory of superconductivity since [PERSON]'s 1911 discovery of the phenomenon) [1; 2] typically exhibit transition temperatures of several Kelvins (i.e., approximately \(-270\) K) [3]. Recently discovered iron-based bulk superconductors demonstrate a highest critical temperature of approximately \(56\) Kelvin (i.e., at approximately \(-217\) degrees Celsius) [4]. However, these materials are far from being considered high-temperature superconductors (HTS), which are defined as materials that behave as superconductors at temperatures above \(77\) K (\(-196.2\) degrees Celsius), corresponding to the boiling point of liquid nitrogen. Cuprate superconductors, which are copper oxides combined with other metals, especially rare earth barium copper oxides such as yttrium barium copper oxide, constitute the main class of HTS. The highest critical temperature achieved by cuprates is around \(138\) K at ambient pressure and possibly \(164\) K under high pressure. Only cuprate superconductors [5] and some possible organic superconductors [6] show critical temperature values near or above the liquid nitrogen boiling temperature of \(-196.2\) degrees Celsius. The discovery of HTS has opened up new possibilities for applications that require coolants with low cost and that are easy to handle as well as higher magnetic fields and currents. Therefore, predicting the critical temperature (\(T_{c}\)) of superconductors has become a topic of great interest in the field of materials science [7].  
In this study, we leverage various machine learning techniques and propose a novel approach based on multivariate continued fractions to develop mathematical models capable of predicting the critical temperature of superconductors. Our models rely solely on the characterization of the chemical structure of the superconducting material by uncovering hidden information within. Accurate prediction of \(T_{c}\) for superconductors can greatly enhance our ability to harness the potential of superconductivity, ushering in a new era of possibilities in multiple fields.
## 3 The First Modal Inertial/Invariant Single Load Vectors Pushover Analysis Methods
### The Displacement Coefficient Method FEMA 356 [8], FEMA 440 [40], FEMA 273 [45] and ASCE 41 [7]  
The displacement coefficient method, initially used in FEMA 273 [45] and later incorporated in FEMA 356 [8], aims at improving the accuracy of nonlinear static procedures (NNPs) by directly estimating the values of the peak inelastic displacement using modifying factors. The displacement coefficient method, a simplified approach, is less accurate for irregular or nonlinear structures. The displacement coefficient method calculates displacement coefficients for each mode of vibration, allowing for better prediction of inelastic behavior and determination of the control-node/target displacement [38]. The DCM involves the calculation of displacement coefficients for each mode of vibration of the structure to estimate the displacement response [14].  
1. Carry out a pushover analysis of the multi-degree-of-freedom system (MDOF) similar to 3.1(1). At this step, the eigenvalue analysis of the system is conducted to establish the modal properties of the system.
2. The following relationship is then used to determine the target displacement. \[\delta_{t}=C_{0}C_{1}C_{2}C_{3}S_{a}\frac{T_{e}^{2}}{4 TI^{2}}g,\] (12) In their work, [PERSON] et al. [38] clearly defined all the associated parameters.  
Figure 4 depicts the displacement coefficient method in a graphical manner.  
Figure 4: Graphical representation of the DCM procedure [8]. Image based on [40].  
\(K_{i}\) is the initial or elastic lateral stiffness of a building, reflecting its stiffness under small, elastic deformations. \(K_{\textit{eff}}\), the effective lateral stiffness, is calculated at a point where the base shear force is 60% of the building's effective yield strength, derived from the bilinear representation of the capacity curve, indicating the building's behavior under larger forces.
## 4 Conclusions
### Implications of the Research
The sensitivity analysis and multi-objective optimization ideas adopted in this study have high reference value for discussing a series of core problems in the academic research level, such as the fault pattern recognition method of a multi-state system, optimizing the maintenance time interval, and balancing safety and economy. Although this study proposed a new method system integrating distance evaluation feature dimension reduction, Weibull proportional failure rate modeling, and deep belief network (DBN) learning in the field of marine oil system reliability analysis and maintenance optimization, and verified its certain advantages in theoretical modeling and performance indicators through numerical experiments, it must be admitted that this method still has obvious limitations in several aspects, which urgently need to be further carefully improved in future research. From the perspective of the scalability of the method, although the DBN has strong nonlinear modeling ability and hierarchical feature abstraction ability, its computational complexity will increase significantly when dealing with larger scale systems, heterogeneous device state evolution characteristics, and high-frequency real-time data streams, which may lead to a long model training time and poor convergence stability. It is even difficult to deploy in the edge computing environment with limited hardware resources. In addition, the assumption of distance evaluation based on the feature dimension reduction method is that the distance metric with good discrimination in feature space is representative in system health status classification. However, this assumption may not always hold in complex practical scenarios where multi-source heterogeneous signals coexist, which may cause the loss of key fault features in the process of information compression. Furthermore, it affects the accuracy of the overall reliability assessment. At the level of practical engineering application, the proposed method faces practical challenges such as a strong dependence between data acquisition and labels, and difficulty in accurately quantifying system parameters. Although this study considers the failure law evolution of equipment under different environmental conditions, due to the highly nonlinear environmental disturbance and the lack of prior statistical distribution in real sailing conditions, the environmental parameter modeling faces great uncertainty in actual deployment, which can easily cause misjudgment and false alarms. In addition, the deep learning model highly depends on the quality of historical data samples and the accuracy of labeling. However, in the shipping industry, a large number of operation and maintenance data are missing, lagging, or not standardized, which weakens the generalization ability and robustness of the model.
## 2 Materials and Methods
### Measurements
The normalized difference vegetation index (NDVI) was measured using a high-resolution spectrometer (QE65 Pro, Ocean Optics Inc., Dunedin, FL, USA), which was installed on the tower platform at a height of 10 m above the canopy. The spectrometer had a spectral range of 650-805 nm.
## 3 Results and Discussion
### Soil Classification of Characteristic Sand Sample  
The sandy soil sample was extracted during SPT testing. The depth collected was 3 m. The sand is composed of 30% medium sand by weight under Brazilian standard NBR 6502 [39]. The coefficient of curvature and uniformity were measured as 2.49 and 15.83, respectively, the effective diameter \(D_{10}\) = 0.008 mm, and the mean particle diameter (\(D_{50}\)) was calculated as 0.10 mm. The specific gravity of sand particles was measured as 2.72. The soil is classified as well-graded sand (SW) in agreement with the Unified Soil Classification System [40]. Scanning electron microscopy (SEM), Energy Dispersive X-ray (EDX) spectrometry, and X-ray fluorescence spectroscopy (FRX) with element detection were carried out on the sand samples. Figures 13 and 14 show the SEM and SEM-EDX positions and elements detection results.  
Figure 12: Soil Classification Profile of Experimental Site based on SPT-T and CPTu tests.  
The sandy soil contained quartz and mica minerals that were in concordance with XRD analysis. As shown in Figure 13, the sand particles are not uniform and have angularity. Backscattered-electron scanning electron microscopy (BSE-SEM) shows the detection of silicon, aluminum, and iron. BSE-SEM imaging is a valuable technique for materials characterization because it provides information about the homogeneity of the material in the analyzed sand specimens and is, therefore, an essential technique in modern SEM. Besides, XFR analysis detected 89.5% silicon, 5.42% aluminum, 1.23% iron, and other minor elements (i.e., K and Mg) by weight. Figure 14 presents the SEM-EDX analysis of the sand sample. Within the five analyzed positions, it can be analyzed that the soil presents the same chemical composition as that detected by the BSE-SEM and XRD.  
When evaluating the behavior of a granular material under an external load, the active contacts between the grains will support these loads. Defining the active contacts supporting this load becomes more complicated when fine particles are present among larger particles. [PERSON] et al. [41] identified three microstructural conditions for analyzing the behavior of granular materials with fines. In the first situation, the larger particles prevail over the smaller ones, which, depending on their quantity, can fill the spaces between the larger particles without contributing to the support of external loads. They may partially support the load or even separate the coarse particles. In the second condition, the fine particles are so high that they can separate the larger particles. Finally, in the last condition, the two types of soils form separate layers.  
Figure 13: SEM micrographs of the sand sample collected.
# Development of a CFD-Based Wind Turbine Rotor Optimization Tool in Considering Wake Effects
## 3 Results and Discussion
### Horns Rev I Wind Farm Case  
The Horns Rev I offshore wind farm is in the eastern North Sea near Denmark. Horns Rev wind farm consists of 80 Vestas V80 2 MW wind turbines within an area of about 20 km\({}^{2}\), as shown in Figure 8. The wind turbines are arranged in a regular array of 8 by 10 turbines which is very suitable to the study of multiple wake effects. The wind turbines are installed with an internal spacing along the main directions of 7D [53]. The wind turbines have a hub height of 70 m, a rotor diameter of D = 80 m, and a rated rotational speed of 18 rpm. The detailed pitch angle, rotational speed and blade geometry data are provided from the reference paper [54]. The 2 MW V80 wind turbine blade is composed of NACA63-series airfoils between the blade tip and the mid-span, and the FFA W3-series airfoils are used between the mid-span towards the blade inboard part. To validate the aerodynamic accuracy, the results from the present simulations and from the reference LES [54; 26] simulations are compared. The inflow wind speed is 5.33 m/s, and turbulence intensity is 9.4% which are selected for fair comparison with some LES results.  
Figure 7: Comparisons between the measured and simulated wake data of the two wind turbines at down-stream positions of 2.5D, 4.0D and 7.5D. (**a**) 2.5D; (**b**) 4D; (**c**) 7.5D.  
The mesh topology used for this study is the same as in the previous simulations. The input airfoil data are now replaced to represent the Horns Rev wind turbines. The wind turbines under investigation are depicted in Figure 8 along the 270\({}^{\circ}\) directions. The wake profiles are shown in Figure 9 at downstream positions of 3D, 5D, 7D and 10D behind the first wind turbine where LES data are also available. General good agreements with LES are clearly seen from the results except very near wake. The wake expansion is well captured as well as the maximum velocity deficit. For the 7D and 10D cases, a slight over prediction of velocity deficit is seen from the current simulation. This indicates that the wake recovery at far downstream is slower using the present approach.  
Figure 8: Layout of Horns Rev I wind farm.  
Figure 9: Comparisons between the LES and RANS lateral profiles normalized wake velocity for single wind turbine at down-stream positions of 3D, 5D, 7D and 10D. (**a**) 3D; (**b**) 5D; (**c**) 7D; (**d**) 10D.  
The CFD simulation of the full column was carried out by extending the computational domain with a total number of 40 blocks. The resulted stream-wise velocity contours are shown in Figure 10 where the flow around wind turbine cluster can be observed.  
In Figure 11, information about the power and thrust of the present wind turbines are also shown. Good agreements are seen from the power and thrust curves, which proves the model accuracy in a wide range of operational conditions.
# Satellite Requirements for Observation of Close Proximity Celestial Bodies +
## 2 Methodology for Orbit and Communication Resources Selection
### Required Number of Satellites  
The required number of satellites for continuous communication depends on their footprint, which is the area in which the satellite is able to provide link availability, and this region is inside the coverage area. The coverage area is important because footprint can usually be steered inside this area to cover an specific spot. Coverage \(S\) is defined as the maximum area from which a satellite can be visible [7], and it can be calculated as  
\[S=2\pi R^{2}(1-\cos(\alpha_{max})) \tag{2}\]  
where the central angle \(\alpha_{max}\) is given by  
\[\alpha=\cos^{-1}\left(\frac{R}{R+h}\right) \tag{3}\]  
Figure 2 shows the coverage that can be obtained by a satellite orbiting the moon as a function of its altitude. The coverage is shown in km\({}^{2}\) and as percentage of the lunar surface. Low orbiting altitudes, e.g., from tens of kilometers, are possible in the moon due to the almost inexistent atmosphere, which in the case of being denser, would cause drag forces and faster orbital decaying in the satellites. In addition, orbital perturbations are mainly caused by the solar pressure radiation and the gravitational influence of Earth, as well as the nonsphericity of the Moon [8].  
Figure 1: Orbit inclination and coverage.  
Figure 2: Satellite coverage area as function of a orbit altitude for the (**a**) Moon and (**b**) Mars.
# Binary Mixes of Self-Compacting Concrete with Municipal Solid Waste Incinerator Bottom Ash
## 3 Results and Discussion
### Mechanical Performance
#### 3.4.3. Secant Modulus of Elasticity  
The secant modulus of elasticity was performed on cylindrical specimens of 150 mm in diameter and 300 mm in height. These specimens were cured in a wet chamber at a temperature of \(20\pm 2\)\({}^{\circ}\)C until testing age according to LNEC E 397 [30]. After curing for 28 days, all mixes containing MIBA had remarkably lower moduli of elasticity as shown in Figure 5 (left). Mixes C20 BA, C30 BA and C40 BA showed a decline of around 47% with respect to the RC, whilst mix C50 BA a decrease of more than 70% when compared to the RC. Contrary to expectations, there was a slight decrease in the secant modulus of elasticity from 28 to 91 days. The continuous hydration of cement and pozzolanic reactions of FA and MIBA would be expected to result in the generation of further products of hydration and densification of the microstructure, thereby leading to a higher modulus of elasticity. After 91 days, mixes containing 20%, 30% and 40% MIBA showed a decline of about 52%, whereas the 50% MIBA specimens showed a 78% reduction with respect to the RC. The overall decline in performance is mostly due to the expansion of the concrete throughout its setting period, prompted by the oxidization of Al from MIBA [47, 48]. The generation of \(\mathrm{H_{2}}\) gas, which became entrapped within the mix increased the specimens' volume as well as the porosity.  
[PERSON] [49], when studying concrete containing MIBA as a substitute for fine aggregate (values ranging from 5% to 80%), observed a reduction, albeit less significant, in the modulus of elasticity between these mixtures and the control mix. [PERSON] et al. [50] observed a decline in the compressive strength and modulus of elasticity with the use of zinc-rich bottom ash. This metal also presents a similar oxidizatio  
Figure 5: Modulus of elasticity (\(\mathrm{E_{cm}}\)) at 28 and 91 days (**left**); relationship between \(\mathrm{E_{cm}}\) and \(\mathrm{f_{cm}}\) at 28 and 91 days (**right**).  
to OH\({}^{-}\) ions in the concrete mix. It is possible that part of the expansion of the specimens in the present study may have been due to the presence of zinc particles in the MIBA.  
The curve in Figure 5 (right) represents the theoretical secant modulus of elasticity according to EC2 [39], which has been calculated using Equation (6). These values can be compared with those obtained experimentally as shown in Table 7. Despite the unexpected lower values of the 91-day modulus of elasticity, the relationship between this property and the compressive strength was within expectable ranges.  
\[E_{cm}=22\bigg{(}\frac{f_{cm}}{10}\bigg{)}^{0.3} \tag{6}\]