esa-sceva/satcom-chunk-collection huggingface dataset:
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
## 2 Experiment Facility and Model
### Steady Base Flow  
The calculation of the stability equation requires a high-precision basic flow field. For the flat-plate boundary layer, the basic flow field can be obtained by calculating the compressible boundary layer equation [31]. Wall temperature, Prandtl number, and specific heat ratio are assumed to be constant. The viscosity coefficient adopts the Sutherland viscosity law.  
\[\begin{array}{l}(\rho\mu f^{\prime\prime})^{\prime}+\frac{1}{2}ff^{\prime \prime}=0\\ \frac{1}{2}c_{p}\cdot fg^{\prime}+\frac{1}{\text{Pr}}\cdot(\kappa\cdot g^{ \prime}\cdot\rho)+\rho\mu\cdot Ec\cdot(f^{\prime\prime})^{2}=0\\ \rho=\frac{1}{g}\end{array} \tag{13}\]  
In this paper, the laminar boundary layer flow should be obtained for LST analysis by solving the compressible boundary layer equation. For solving the differentia equations, the physical quantities are nondimensionalized by the free flow parameters and reference length. The dimensionless compressible boundary layer equations are as follows, where _Ec_, \(k\), and Pr are the Eckert number, thermal conductivity, and Prandtl number, respectively. Inthis work, we consider the boundary layer of a perfect gas with Prandtl number Pr = 0.71 and specific heat ratio \(\gamma\) = 1.4, and the viscosity law satisfies the [PERSON]'s law.  
The mean flow variables of the boundary layer at different unit Reynolds numbers are given in Figure 4. As the unit Reynolds number increases, the boundary layer thickness gradually diminishes.
## 2 Port Cranes Overview
### Evolution Timeline of Port Cranes
#### 2.2.1 Ancient to Medieval Periods (Pre-18 th Century)  
* Early Manual Lifting Devices (Pre-530 BC)--Long before the first written records, civilizations such as the Ancient Greeks, Egyptians, and those of the Mycenaean era had already employed basic pulley systems and winches. These simple yet effective mechanisms were crucial for constructing monumental structures and facilitating early port operation. For instance, the construction of massive structures in Tiryns, Mycenae, and the megalithic monuments of Mane in ancient Greece relied on such lifting techniques, although the exact crane-like devices used remain somewhat speculative [1].
* Written Records and Early Cranes (530 BC)--The first known written records of hoisting mechanisms in ports date back to 530 BC in Ephesus, Greece, in relation to the construction of the first Temple of Artemis. Although details are scarce, these records mark an important milestone in the documented history of port-related lifting technologies [1].
* Treadwheel Cranes (12 th-18 th Centuries)--By the 12 th century, Europe saw the emergence of wooden wheel cranes. These cranes, operated by humans walking on a large wheel, can lift up to five tons. They became staples in medieval ports, enabling the loading and unloading of goods, although their operations were labor-intensive and relatively slow. Their design, with wooden gears and ropes, was a significant technological step forward at the time but was limited in terms of mobility and the weight they could handle [2].
## 3 SHINe Core Building Blocks
### SpaceWire Endpoint  
The SpaceWire Endpoint plays a role similar to the SpaceFibre Endpoint and it is the building block providing a fully functional implementation of the SpaceWire standard. From the user perspective, the two kinds of Endpoints offer a similar interface. A SpaceWire Endpoint, whose architecture is shown in Figure 3, provides two input/output gates for the connection with the upper layer application and one input/output gate representing the physical connector. The connection with the application is split in two gates, one for the NChars transmission and reception and one for the TimeCodes, hence it is possible to use two different applications for the packet stream and the TimeCodes. Note that Interrupts are not currently supported in SHINe. The SpaceWire Endpoint internally instantiate a TimeCode Manager, which is responsible for:  
* keeping trace of the value of the next TimeCode to send;
* storing the value of the latest TimeCode received, in order to be able to validate (or discard) the next one. As specified in the standard, a TimeCode is valid when its value is equal to the value of the previous one plus one. Only if this condition is satisfied the application is notified about the reception of a new TimeCode.  
Following the same logic of the SpaceFibre Endpoint, an application must extend the _ToSpwApplication_ C++ abstract class in order to be connected to a SpaceWire Endpoint. In addition, in this case, several network related statistics are automatically collected by SHINe.  
A SpaceWire Endpoint can instantiate either a SpaceWire Port or a FIFO Port, keeping the same interface towards the application. In the former case, a complete SpaceWire Codec is simulated in C++, while in the latter case a generic FIFO-like protocol takes place, with the same limitations already described for SpaceFibre. When the SpaceWire Port is used, the most remarkable functions are:  
* Simulation of the Initialisation phase, according to the initialisation state machine described in the standard;
* Flow Control Mechanism (FCTs);  
Figure 3: Internal architecture of a SpaceWire Endpoint.  
* Error detection, in case a character is received when not expected or it contains an invalid field;
* ESC (see [6] for details) character insertion when no data is available to be sent;  
Two adjacent SpaceWire Endpoints communicate exchanging OMNeT++ messages representing the characters sent by the Codecs. Note that, differently from SpaceFibre, in SpaceWire the different kinds of characters have different bit sizes. For example, an NChar is 10 bits long considering the Parity and Control bits, while an FCT is only 4 bits long, hence they take a different amount of time to be transmitted. The Endpoint is highly customizable through several parameters, such as transmission and reception data-path delay, initialisation link speed, buffers size, and others.
## 4 Results
### Crop-Mapping from Phenology and Validation  
Figure 8 shows the land cover types in Bole and Luntai mapped using phenology information extracted from the reconstructed time series of 30 m NDVI data. The results indicate that the cotton and corn plots were well classified. Moreover, the trees on the side of road were also extracted. Table 8 shows the accuracy evaluation using field survey data for Bole and Luntai. The results show that land cover types were mapped with an accuracy of higher than 90%.
## 3 Results and Discussion
### Oxide Surface Characterization
#### 3.1.3 Oxide Surface Chemistry  
The EDS-derived surface compositions from each oxide group are compiled in Table 2. The average Ca/P ratios for each oxide are provided in Figure 3. The NOJ CP oxides revealed 8.4 \(\pm\) 1.5 at.% Ca and 4.5 \(\pm\) 0.6 at.% P uptake, which yielded a Ca/P ratio averaging 1.90 \(\pm\) 0.10. In contrast, the NOJ MP oxides revealed higher dopant uptake levels for both Ca (10.8 \(\pm\) 0.2 at.%) and P (6.3 \(\pm\) 0.1 at.%), and a resulting Ca/P ratio averaging 1.72 \(\pm\) 0.0. Thus, both oxides produced using the juiced navel orange-based anodization processes exhibited statistically similar (\(p\) = 0.2) surface Ca/P ratios close to the known stoichiometric Ca/P ratio of hydroxyapatite (1.67). Surprisingly, the NOJ CP oxide also revealed approximately 0.05 at.% of Mg dopant incorporation. Since Mg-containing compounds were not intentionally added to the NOJ CP electrolyte, the juiced navel oranges were identified as the likely source of Mg. Mg was intentionally added in the form of magnesium phosphate to the NOJ MP electrolyte, and the resulting oxides showed an increased uptake level of approximately 1.4 at.% Mg. As mentioned previously, Ca, P, and Mg are each critical components of bone and play essential roles in bone regeneration [9, 10, 11,37]. These key bone minerals regulate osteoblast and osteoclast activity, promote matrix mineralization, and enhance the expression of key osteogenic markers [9, 10, 11, 37]. As shown in Table 2, very little evidence of titanium from the substrate alloy was identified in the EDS analyses. This result was most likely due to the thickness of the surface oxide anodization coatings.  
Figure 2: Optical microscopy and SEM images (left column—100\(\times\) optical images, middle column—SEM 5000\(\times\) micro-scale topography, right column—SEM 75,000\(\times\) nano-scale topography) for each anodized oxide. Oxide NOJ CP and NOJ MP revealed greyish-white patches on optical microscopy images. Micron and nano-scale SEM images for both the oxides showed complex topographies with different surface morphological appearances. NOJ CP oxides revealed petal-like surface morphology on nanoscale SEM images, while the NOJ MP oxides showed more rounded or nodular features. Both the oxides also showed micro-nano scale porosity.
# OpenBIM: An Enabling Solution for Information Interoperability
## 5 Research Gaps and Future Directions
In order to take full advantage of the potential of ontology, further development and utilization of ontology should be carried out from the following aspects: (1) the development and (re-)use of inter-connected ontologies including upper- and domain-level ontologies; (2) a library of modelling alternatives to satisfy different representational needs; and (3) more effective software environments which can support different phases in the ontology-based software lifecycle. Integration of BIM and GIS  
Based on the developed IFC and CityGML semantic models, users can flexibly choose the corresponding BIM-GIS integration methods according to the specific problems, including extracting data from one system to another, merging data using semantic models or integrating data of both models by a third-party platform [85]. However, the above integration technologies still have the following defects: (1) when converting IFC to CityGML, the model information mismatch between BIM and GIS will lead to geometric information mismatch. Because the huge and complex IFC data stored in BIM models has various attributes and geometric expressions, most of the information representing the attributes and associations between features in IFC may disturb the geometric mapping process between IFC and CityGML. (2) when transforming data between IFC and CityGML, semantic loss of features may occur. For instance, the outer and inner walls of a real building become the same object after conversion. Therefore, the future research should focus on improving the accuracy of BIM-GIS data integration, and can be carried out from the following directions: (1) interoperability, transformation process and detailed information processing of these two kinds of data should be focused. First, the upgrade of IFC and CityGML semantic models is needed to improve the accuracy and matching degree of semantic information; second, in addition to consider one-way information transformation from IFC to CityGML, future work will focus on the two-way transformation of model information. (2) when using the third-party platform for data integration, it is necessary to ensure the consistency of these two semantic models and the accuracy of data mapping. Data can be classified based on data hierarchy, and the integrated data from both models using a third-party platform can be developed in the direction of customer-orientation, information sharing, and simplified operations. Besides, an open multi-party data sharing platform can be developed for future use. (3) in order to obtain optimal solutions and planning decisions using BIM and GIS data, it is necessary to ensure the accuracy and diversity of the original data. The process of data measurement, collection and statistical analysis should be comprehensive and careful, not only the macro geographic information, but also the detailed information of the internal space of the building is needed.
## 6 Discussion  
The results presented in Section 5 highlight the best- and worst-performing supervised ML approaches for both the linear and the non-linear wear-state classes that were investigated. The SVM-based algorithm approach that employed one-third octave band-based features was found to yield the best performance for the linear wear-state classes. This was the case for both STFT- and EA-based features, achieving scores of J = 59.5% with MAE = 0.13 for \(O[m,k]\) using SVM (Coarse G) and J = 62.5% with MAE = 0.12 for \(EA_{O}\) using SVM (Linear), respectively, as shown in Tables 4 and 5, for the linear wear-state classification. Again, for the non-linear wear-state classification the SVM (Medium G) based algorithm performed extremely well using the \(O[m,k]\) STFT features by achieving scores of J = 73.6% with MAE = 0.10 as shown in Table 6. Using the same octave band feature set, the \(k\)-NN (Cubic) approach had comparable performance coming in at slightly less accuracy at J = 73.2% with the same MAE = 0.10, as shown in Table 6. In the case of the EA features for non-linear wear-state classification, the SVM (Linear) achieved J = 73.1% with MAE = 0.09, see as shown in Table 7. However, the \(k\)-NN (Cosine) had superior performance using the spectral-based EA features, \(\mathcal{F}\Big{\{}EA_{O}\Big{\}}\), achieving J = 74.3% with MAE = 0.08. This was the best performance achieved for all the ML approaches over this entire investigation, with the best classification accuracy and the lowest MAE. In order to better analyse and interpret the results more closely, confusion matrices are presented for the best-performing approaches for both the linear and non-linear wear-state classification approaches investigated. Accordingly, these confusion matrices correspond to the approaches that have their values highlighted in bold font in Tables 4-7 as discussed previously. At the class level, these confusion matrices enable the classification results to be examined, and they allow the MAE to be quantified and better appreciated--for instance, regarding how many samples from class 1 were incorrectly classified as class 5. The confusion matrices shown in Figure 7 allow us to see the classification performance for each class by observing the percentage score along the diagonal. It is noted that the vast majority of classification inaccuracy (MAE) tends to be predicting the neighbouring class, which is significant, while this information was captured collectively for the entire class set using the MAE metric for ML approaches; however, these confusion matrices offer the granularity to identify which specific classes were the most challenging to estimate.
## 3 Numerical Examples
### Moving-Deformed Bubble  
The rising process of a bubble between electrodes in liquid nitrogen is calculated by COMSOL Multiphysics 5.1 provided by COMSOL AB. Under the action of buoyancy and electromagnetic force, the bubble gradually floats up and changes into an ellipsoid. Its volume will change during this process figure [16; 17; 18].  
The trajectory of the bubble in liquid nitrogen is calculated in COMSOL Multiphysics 5.1 with the Fluid Flow and Electrostatics modules. As shown in Figure 11, the entire solution domain is 10 mm \(\times\) 10 mm \(\times\) 10 mm. There is a 1.5 mm radius bubble in the center. The liquid material is liquid nitrogen and the bubble material is nitrogen. The temperature is 77 K. The upper and lower plates are set to No Flow, with 20,000 V voltage added to the top and grounded to the bottom. The initial liquid flow rate is 0. Add gravity along the \(Z\) direction. The movement and deformation of the bubble at different times are shown in Figure 12.  
The coordinates of the points on the gas-liquid interface at different times calculated by the software are exported. According to the position of the gas-liquid interface at different times, the electric field calculation model is established, and the potential distribution is calculated by CFEM and improved XFEM, respectively.  
Figure 11: Numerical simulation model of bubble rising in liquid nitrogen.  
Figure 12: (a) Bubble at 0 s; (b) Bubble at 0.025 s; (c) Bubble at 0.035 s.  
Figure 13 shows mesh grids for three different times in CFEM and one mesh grid for improved XFEM.  
CFEM is limited by its characteristics. With the change of bubble shape and position, the mesh generation process is repeated at different times. XFEM is an excellent meshless computing method, which can omit the steps of repeated meshing. The same mesh grid can be used in different states, greatly reducing the calculation time cost.  
The comparison of node number and elements number of the model under CFEM and XFEM is shown in Table 10. CFEM meshed three times in three moments, while XFEM only meshed once.  
Figure 14 shows the potential distribution on slices parallel to the XZ plane through the center of the sphere. In a total of 3167 nodes, the potential calculation results at 3 moments are compared. The comparison results are shown in Table 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Node Number** & **Element Number** \\ \hline CFEM 0 s & 3218 & 16,876 \\ CFEM 0.025 s & 3214 & 16,818 \\ CFEM 0.035 s & 3328 & 17,516 \\ Total of CFEM & 9760 & 51,210 \\ XFEM & 3167 & 16,553 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Contrast of number of nodes and elements.  
Figure 13: (**a**) Mesh grid of CFEM at 0 s; (**b**) Mesh grid of CFEM at 0.025 s; (**c**) Mesh grid of CFEM at 0.035 s; (**d**) Mesh grid of XFEM for all times.
## 4 ASK Modulation and Demodulation
### ASK Modulation for Wireless Telemetry and Endoscopic Implants  
In wireless capsule endoscopic systems, the data transmission from inside to outside the human body is high to achieve high quality medical images, so for quality internal biomedical imaging, [PERSON] _et al_. [39] proposed a pseudo differential stacked class-A power amplifier based on phase lock loop (PLL) and RF conversion circuit. In the system, a 20 MHz, 0.25 \(\upmu\)m CMOS, 1 Mbps data rate, 3.62 mm\({}^{2}\) chip area of 3.17 mA power consumption ASK modulator is used to generate the low frequency carrier by a voltage controlled oscillator and convert the low frequency ASK signal into the 2.4 GHz industrial scientific and medical (ISM) band. However, the sizes of the demodulators are still not up to the level needed. Therefore, after a detailed review of the demodulators like CMOS current mode [40], wireless capsule endoscopic application [39], neuro-stimulus chip for retinal prosthetics, neuromuscular, cochlear devices [36,41-44] and nano-mechanics based C-reactive protein detection [45], [PERSON] _et al_. [46] proposed a self-sampling demodulator without passive elements consisting of a pulse shaper, voltage scalar, level contractor and self-sampler. Again in 2009, [PERSON] _et al_. [47] designed an ASK demodulator for wireless telemetry biomedical applications, which supports data rates up to 300 kbps and detects envelopes with a modulation index of 1% or more. The circuit design includes a single-to-differential OTA, a current mode full-wave rectifier, a log-domain peak detector, a variable-gain amplifier and a comparator.
## 4 Conclusions  
The presence of three fatty acids (palmitic P, stearic S, and oleic O) constituting triacylglycer-glycerols SOS, POP, POS, POO, and SOO was observed in all the samples. The presence of linoleic acid (L) was also found, which formed triacylglycerols such as PLP and PLS. The researched chocolates were characterized by a diverse composition of fatty acids. Chocolates in which the addition of fat other than cocoa butter was declared achieved results similar to other products in terms of fatty acid profiles. All the chocolates contained linoleic acid. Saturated fatty acids were also detected: myristic, pentadecanoic, margaric, monounsaturated: cis-10-pentadecanoic, palmitoleic, margaroleic, eicosenic, and polyunsaturated fatty acids: \(\alpha\)-linolenic and dihomo-\(\gamma\)-linolenic at levels below 1%, and arachidonic acid, the content of which did not exceed 1.5%. This allowed us to confirm that the products were not adulterated with the addition of vegetable fat that had not been declared on the packaging. A high content of polyunsaturated fatty acids in the internal sn-2 position of TAG was recorded in the researched chocolates. This arrangement of fatty acids helps to slow down the oxidation process.  
All the obtained DSC melting curves of the fats were characterized by the presence of two endothermic peaks. Additional peaks appeared in the case of chocolates 2, 6, 7, and 8. These peaks, appearing at negative temperatures, may be caused by the melting of low-melting triacylglycerols (with a high content of unsaturated fatty acids). The differences between the melting curves for the obtained dark chocolate fats may have resulted from the presence of less-stable polymorphic forms of cocoa butter.  
Figure 11: Dendrogram (**a**) and PCA (**b**) based on the TG values.  
Based on the shape of the TG and DTG curves, it could be possible to indicate the adulteration of chocolates. The obtained results allowed us to indicate the differences between the content of the cocoa butter, sugar, and cocoa liquor in the studied chocolate samples.  
Conceptualization, E.O.-L.; methodology, E.O.-L., I.P., R.B. and A.G.; investigation, M.W.-W., A.G., I.P. and E.G.; formal analysis, E.O.-L., A.G., M.W.-W., I.P., R.B. and A.S.; writing--original draft preparation, E.O.-L. and A.G.; writing--review and editing, E.O.-L., A.G. and M.W.-W. All authors have read and agreed to the published version of the manuscript.  
The study was financially supported by sources of the Ministry of Education and Science within funds of the Institute of Food Sciences of Warsaw University of Life Sciences (WULS), for scientific research.  
Not applicable.  
The data generated or analysed during this study are available from the corresponding author on reasonable request.  
The authors would like to thank [PERSON] for her help in carrying out the analyses and technical support.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Linear Regression Model for Reliability Prediction
The adequacy of the model is assessed via the residual analysis presented in Figure 12. In the upper-left graph, the residual normal distribution hypothesis is evaluated, whereas a similar visual analysis is also depicted in the histogram graph in the lower-left graph. In both situations, the residuals of the model are well represented by a normal distribution, with a \(p\) value of 0.27 in the Anderson-Darling normality test, which fails to reject H\({}_{0}\): the residuals follow a normal distribution. In the upper-right graph of the adjusted value as a function of the residual and in the graph of the observed values as a function of the residuals (lower-right), a reasonably random behavior is observed, with only an apparent trend of lower fit with lower reliability. The model's fit with respect to the reliability values R(t), as well as its confidence intervals, is presented in Figure 13. Good adherence to the regression model is observed, which uses only sensor readings to predict the reliability of ceramic plates independently of their operating time. The confidence intervals of some values may contain a fitted reliability higher than 100%, which has no meaning in real-world contexts. Figure 12: Residual analysis of the regression model. Figure 13: R(t) and adjusted values with 95% confidence intervals (CIs). Y-axis ranges from 30% to 120% for improved visualization only. To obtain the remaining useful life, a 70% reliability trigger is obtained at 4822 h of operation. For each instant in time, reliability is calculated from the sensor readings, and the corresponding time of operation is obtained. The remaining useful life is obtained by simply subtracting 4822 h from this result. The equation is expressed as follows:  
\[RUL(t)=4822+13,178\times\ln[R(t)]\]  
Alternatively, it can be replaced with a linear regression model:  
\[RUL(t)=4822+13,178\times ln[-558+8.01\;PT101(t)-1.61\;KPV325(t)-82.0\;PT329(t)+1 073\;PT129(t)]\]