esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Surface Characterization
#### 2.3.4 Second Harmonic Generation Imaging (SHG)  
Second harmonic generation is a microscopy technique used to study nonlinear optical effects, often applied to observe the structure of molecules such as collagen. The scaffold materials were made into paraffin sections, and SHG signals were collected using two-photon confocal microscopy (TCS SP5, Leica, Wetzlar, Germany). The excitation wavelength was set to 880 nm to observe the collagen coupling on the scaffolds.  
3.5 Detection of Collagen Coating Content by High-Performance Liquid Chromatography Mass Spectrometry  
Utilizing HPLC-MS technology, we proceeded to assess the hydroxyproline content present on the surface of PET fibers subsequent to the application of a Type I collagen coating. Collagen is composed of 18 amino acids, of which hydroxproline is one of the characteristic amino acids of collagen. Its content in collagen is relatively constant, about 12%. Based on this, the content of collagen can be indirectly calculated by measuring the content of hydroxyproline.  
To detect the content of collagen coating on the scaffold samples, 10 mg of the samples were weighed and added to 6 M HCl at 110 \({}^{\circ}\)C for 24 h of hydrolysis. After cooling and drying, the samples were re-dissolved in 1 mL of ultrapure water. Hydroxyproline was used as the standard, and the concentration of hydroxyproline was detected by high-performance liquid chromatography (UltiMate 3000 RS, Thermo Fisher Scientific, Waltham, MA, USA) coupled with mass spectrometry (TSQ QUANTUM ACCESS MAX, Thermo Fisher Scientific, Waltham, MA, USA), which indirectly determined the content of collagen in the sample.  
The hydroxyproline concentration (\(A\)) was calculated by plotting data points on a standard curve with the area of the HPLC peak on the \(Y\)-axis and the hydroxyproline concentration on the \(X\)-axis. The amount of type I collagen (\(m\)) in the sample can be calculated using the formula below, where \(V\) represents the volume of the sample after post-hydrolysis resuspension, and \(D\) represents the dilution ratio of the sample.  
\[m=\frac{A\times V\times D}{12\%} \tag{1}\]
## 7 Discussion
### Data Quality, Quantity and Diversity  
[PERSON] et al. [61] show that there is a difference in brain morphology among different races and genetic backgrounds. In particular, the authors show that there are significant differences in cortical volume in various lobes of the brain between caucasian and Chinese cohorts. There is a possibility there are differences in the distribution of EAT in different ethnic, racial, and cultural groups. Additionally, the attenuation value of EAT in CTs can vary from patient to patient, and the distribution and volume vary depending on the patient's weight and overall health. To better understand the true performance of these methods, there is a need for larger datasets that include patients of different genders, races, geographical locations, health and fitness levels as well as pre-existing conditions.  
While some papers mentioned in this review use comparatively large datasets of \(100\) patients or more [7; 9; 25; 54], most validate their methods on smaller datasets, even as low as \(10\) patients. It is our opinion that using a dataset smaller than \(100\) patients is not sufficient to validate that a method generalizes well enough for medical use, neither on the same distribution of patients as in the dataset nor for patients of other backgrounds.  
Despite the availability of unlabeled cardiac CT scans (e.g., [62]), due to the time-consuming nature of manually labeling EAT, there is not much publicly available data with labeled EAT and PAT volumes.  
The one notable exception is [63], collected [PERSON] et al. [20]. This is a publicly available dataset of CT scans of 20 patients. This dataset is later used in many papers from this review [27; 36; 37; 38; 43; 44; 45; 46; 47; 49]. While using a common dataset means a more objective measure of the algorithm's performance, this dataset still only contains 20 patients from the same geographic area.  
With all this taken into account, it is clear there is a need for larger publicly available datasets of labeled EAT, especially CT scans. This can be addressed by a large-scale EAT quantification challenge, similar to [PERSON] et al. [62] but focusing on EAT segmentation and quantification. Aside from collecting more data from expert observers, new image augmentation methods and methods for the automatic generation of images could help. Using adversarial image generation methods could also provide artificially created data useful for training neural networks [64; 65].
## 4 Local Railway Level Crossing Case Study
### Severity Analysis of the Accident Scenarios  
Every event in the ETA structure influences the accident severity, varying the expected FWI (Fatalities and Weighted Injuries) for each accident scenario. The FWI is a commonly used synthetic meter of accidental consequences [39; 43], where major and minor injuries are expressed as a fraction of one fatality.  
The accident severity (N) is evaluated as the total number of people exposed to the accident (PAX) for a lethality factor (\(\lambda\)) function of the train speed (\(V\)) as shown in (2). The lethality factor represents the probability for each passenger to die from the accident.  
\[N=PAX\cdot\lambda(V)\ [FWI] \tag{2}\]  
The probability of being injured inside the train or inside the road vehicle can be very different. As presented in [PERSON] [39], the average loss of a 'train strikes road user' causes one major injury on the vehicle (0.1 FWI), where the passengers exposed are usually one or two, and two minor injuries on the train (0.01 FWI), where the total passenger are many dozen.  
For this reason, the accident lethality for the people inside the train needs to be evaluated separately from that of the people inside the road vehicle, as expressed in (3).  
\[N=PAX_{rail}\cdot\lambda_{rail}+PAX_{road}\cdot\lambda_{road}\ [FWI] \tag{3}\]  
To evaluate \(\lambda_{rail}\) and \(\lambda_{road}\) as a function of the train speed, different railway industry data sources were examined. In particular, the Rail Safety and Standards Board (RSSB) provides a wide database on the causes and consequences of railway accidents reported in the U.K. railway network [44]. For each accident reported, the type of level crossing, the train speed allowed, and the consequences reported among the passengers of the train and road vehicle were considered. Through the linear interpolation of the data obtained, the trend of the lethality factor shown in Figure 6 was obtained for the occupants of the railway vehicle (a) and the road vehicle (b), respectively.  
Figure 5: Event tree structure and scenarios probabilities of ‘Hazardous crossing of LC’.  
Three main groups of final accident scenarios can be distinguished:
* Scenarios S1 and S2: These are safe scenarios where no fatalities are expected. Consequently, the risk is considered null for all LCs and vehicle types;
* Scenarios S3, S4, S5, and S6: In these cases, the train driver perceives the hazard and begins braking. As a result, the consequences of the accident are either equal to or lower than the maximum possible, based on the train's braking dynamics;
* Scenarios S7, S8, S9, and S10: These involve an impact at the line speed, representing the most severe scenarios. The progression of Scenarios S3, S4, S5, and S6 depends on the sight distance (\(D_{s}\)) and the braking distance (\(s_{b}\)), specifically: 1. \(D_{s}\geq s_{b}\): the train is able to brake safely, avoiding a collision with the road vehicle;
2. \(D_{s}<s_{b}\): the train impacts the vehicle at a speed determined by the available deceleration within the given sight distance.
# Detecting and Responding to Concept Drift in Business Processes
## 3 Preliminaries
### Gamma Mixture Model  
Definition 10 (gamma distribution): The gamma distribution is a two-parameter (non-negative shape \(z\) and non-negative rate \(\lambda\)) family of continuous probability distributions, which is widely used for modelling non-negative distributions. The density function is  
\[g(x|z,\lambda)=\frac{\lambda^{z}}{\Gamma(z)}x^{z-1}e^{-\lambda x},x\geq 0 \tag{3}\]  
where \(\Gamma(z)\) is the gamma function that equals \((z-1)!\) for positive integer \(z\).  
Definition 11 (gamma mixture model, ga-mm): The ga-mm is a mixture of multiple gammas with mixing proportions that sum to one. It is extremely flexible to fit any shape of positive continuous distributions. The density of a ga-mm is  
\[f(x|\mathbf{\Theta})=\sum_{i=1}^{m}\alpha_{i}g(x|z_{i},\lambda_{i}) \tag{4}\]  
where \(\mathbf{\Theta}=(\alpha_{1},\cdots,\alpha_{m},z_{1},\cdots,z_{m},\lambda_{1}, \cdots,\lambda_{m})\). \(\alpha_{i},z_{i}\) and \(\lambda_{i},i=1,\cdots,m\) are the mixing proportion, shape and rate parameter of the ith gamma component, respectively.  
In the proposed HMDP method, gamma mixture models are integrated into the SMP model to characterize customers' duration data. The SMP is then applied as the bottom layer of the hierarchical MM. The top layer of the hierarchical MM is a high-level representation of customer patterns, derived from K-means. The detection/adaptation of concept drifts in customer transitions and their duration is then handled by an MDP.
# Study of Root Canal Length Estimations by 3D Spatial Reproduction with Stereoscopic Vision
## 2 Materials and Methods
### Statistical Processing  
For the objective evaluations, the measured values of root canal length and its measurement time were aggregated, and normality was confirmed using the Kolmogorov-Smirnov test. In addition, intraclass correlation coefficients (ICCs) and their 95% confidence intervals were calculated to assess the intra-rater reliability. A Bland-Altman analysis was also performed to confirm agreement. A comparison of differences in scores and measurement time was then performed using one-way ANOVA, followed by the Bonferroni post hoc test.  
For the subjective assessments, the Likert scale and free-text responses from the feedback questionnaire administered after the first measurement with the SRD and 2D devices were analyzed. The free-text responses were classified using topic modeling through text mining.  
For the tooth model with the lowest standard deviation of measurement time, the correlation between the first measurement time with the 2D device and age, gender, frequency of 2D device use, and self-rated eye fatigue in the feedback questionnaire was analyzed using Spearman's rank correlation coefficient. Independent variables were used for age, gender, and the frequency of 2D device use. Age was coded as 0 for less than 30 years, 1 for 30-39 years, and 2 for 40 years and older. Gender was coded as 0 for female and 1 for male. The frequency of 2D device use was coded as 0 for first-time users, 1 for occasional users, 2 for monthly users, and 3 for weekly users. All statistical analyses were performed using IBM SPSS Statistics version 28.0, with a 5% significance level.
## 4 Data Processing
### Correction for the USV's Attitude and Magnetization  
The shipborne magnetic data are influenced by the vessel's magnetic characteristics and its attitude changes [5,28]. These influences complicate data interpretation and contribute to the more prevalent use of towed scalar magnetometers, despite the advantages of fluxgate magnetometers in acquiring vector data [6,9,29]. Several methodologies have been proposed to correct for the magnetic influences on fluxgate magnetometers mounted on ferromagnetic vehicles [24, 25, 26]. In this study, we compare and analyze three mathematical approaches to determine which method is more accurate for correcting fluxgate magnetometer data on the USV. All proposed equations fundamentally originate from the same formulation, as represented in Equation (1) [8]. The first equation delineates the direct transformation relationship between sensor measurements (\(H_{\text{obs}}\)) and the ambient magnetic field (\(H_{env}\))  
\[H_{\text{obs}}=A\cdot R\cdot H_{env}+H_{p}+H_{v} \tag{1}\]  
where A is a \(3\times 3\) transformation matrix reflecting the sum of Earth's magnetic field and the ship's induced magnetization susceptibility tensor, and R represents a rotation matrix for Roll, Pitch, and Yaw. \(H_{env}\) is the Earth's background magnetic field a three-component vector (\(3\times 1\)), \(H_{p}\) is a \(3\times 1\) vector representing the ship's permanent magnetization, and \(H_{v}\) is the ship's viscous magnetization (VM). The VM, commonly referred to as magnetic viscosity or magnetic aftereffect, occurs due to changes in magnetization direction caused by variations in vessel attitude and the Earth's magnetic field on vessels, which represents the magnetic field generated by eddy currents within the hull. Although viscous magnetization is typically weak and does not significantly affect data interpretation [8], it can accumulate during long-term vessel navigation and be visible as long-wavelength signals; consequently, it reduces the reliability and precision of high-resolution magnetic survey results [3; 9]. Therefore, to improve the accuracy of vector magnetic field measurements, it is very important to have a comprehensive understanding of the VM effect and develop hardware or software compensation strategies to reduce this effect.
## 2 Modeling and Optimization Method
### BSCN Model and Optimization
#### 2.2.3 Objective Function and Constraint Function  
To further study the structural parameters of the groove of the BSCN, the pressure drop formula of the BCT was taken as the objective function for the inlet section L1 and the outlet section L3. As shown in Figure 7, a pressure drop calculation formula of the BCT was obtained by selecting a circular groove element:  
\[\Delta\mathbf{p}=\Delta\mathbf{p}_{\mathbf{k}\mathrm{s}1}+\Delta\mathbf{p}_{\mathbf{\mathrm{s}} \mathrm{k}1} \tag{18}\]  
In this formula: \(\Delta\mathbf{p}\)--BCT pressure drop; \(\Delta\mathbf{p}_{\mathbf{\mathrm{s}}\mathrm{k}1}\)--shrinkage--increase BCT structure pressure drop; \(\Delta\mathbf{p}_{\mathbf{\mathrm{s}}\mathrm{k}1}\)--expansion-shrinkage BCT pressure drop.  
Figure 6: BSCN optimization flow chart.  
Figure 7: BCT schematic diagram.  
The example Formula (18) was further simplified to the objective function:  
\[\begin{split}&\Delta\mathbf{p}=\left(3.583-2.565\cdot e^{\frac{-w}{7.891 \cdot(d+2.h)}}+15.46\cdot e^{\frac{-s}{(d+2.h)}}+0.73\cdot e^{\frac{-s}{(d+2.h)} }\right).\\ &\left\{\rho\cdot\left\{3-5\cdot\frac{d^{2}}{\left(d+2.h\right)^{ 2}}+2\cdot\left[\frac{d^{2}}{\left(d+2.h\right)^{2}}\right]^{2}\right\}\cdot \frac{v_{2}^{2}}{2}\right\}\end{split} \tag{19}\]  
In these formulas: \(\mathbf{d}\)--trail diameter (m); \(\mathbf{w}\)--groove width (m); \(\mathbf{s}\)--slot pitch (m); \(\mathbf{h}\)--groove depth (m); \(\rho\)--liquid density (kg/m\({}^{3}\)); \(v_{\mathbf{d}}\)--flow rate at the inlet (m/s).  
Combined with the constraint conditions of Formula (18), the following constraint function was obtained:  
\[\left\{\begin{array}{l}0.04\leqslant\frac{s}{d+2.h}\leqslant 0.2\\ 0.15\leqslant\frac{w}{d+2.h}\leqslant 0.37\end{array}\right. \tag{20}\]  
In this formula: \(\mathbf{d}\)--trail diameter (m); \(\mathbf{w}\)--groove width (m); \(\mathbf{s}\)--slot pitch (m); \(\mathbf{h}\)--groove depth (m).
## 3 Survey of Techniques for Updating
### Dissemination
#### 3.1.7 Crossbow in-Network Programming (XNP)  
XNP [33] provides a single-hop, in-network programming facility for TinyOS. A special Boot-loader must be resident in a reserved section of program memory, and the XNP protocol module must be wired into an application. A host PC application loads the image, via a base station more running TOSBase (this acts as a serial-to-wireless bridge) to one (mote-id specific) or many (group-id specific) nodes within direct radio range of the base. The image is sent in capsules, one per packet; there is a fixed time delay between packet transmissions. In unicast mode, XNP checks delivery for each capsule; in broadcast mode, missing packets are handled, after the full image download hascompleted, using a follow-up query request (nodes respond with a list of missing capsules). During download, other applications are halted, and the program is loaded into external memory. When a reboot command is issued (via the host program), the boot-loader copies the program from external to program memory, and finally jumps to its start to activate the update.  
XNP is a very limited one-hop update mechanism, essentially replaced by MNP. Except for the explicit differentiation of dissemination and activation, it contains no specific features useful in an autonomous update environment.
# Characterization of Nutrient Disorders of _Camabis sativa_
## 2 Materials and Methods
### Micronutrient Disorders
#### 3.2.5 Copper  
Copper deficiency manifested very late in the growth of the plants, only displaying symptomatology after 9 weeks. Initially, the plants developed slight stunting in their growth habits when compared to the control. This stunting was accompanied by a slight distortion of the newer and expanding leaves, especially at the leaflet base. The base of the leaflets was narrower and displayed slight yellowing (Figure 5). In advanced symptoms, the new and expanding leaves displayed a more pronounced basal narrowing and distortion and started to exhibit marginal interveinal chlorosis (Figure 5). In the most advanced stages, the whole leaf displayed a fine interveinal chlorosis, and the leaf margin distorted, slightly curling in and downward. The new and expanding leaves also displayed a slight decrease in turgidity (Figure 5).  
Foliar Cu concentrations were 70% lower in plants subjected to Cu-deficient conditions. Deficiency treated plants contained 1.41 mg\(\cdot\)kg\({}^{-1}\) Cu, while the control plants contained 4.65 mg\(\cdot\)kg\({}^{-1}\) (Table 4). Foliar Cu concentrations in control plants were slightly below the published survey range of 5-7.1 mg\(\cdot\)kg\({}^{-1}\) Cu ([PERSON] et al., [9]). Plants grown in Cu-deficient conditions produced 45% less biomass when compared to the control (Table 3).
## 8 Numerical Simulation and Experimental Testing
### Experimental Testing and Analysis
#### 8.2.3 Fault-Tolerance Test of the Unmanned-Ship Positioning Algorithm
This was mainly because the confidence factor of multi-sensor positioning data is associated with hierarchical sampling in the TCPF algorithm, so when using the TCPF algorithm to filter and fuse ZigBee-GPS/BDS multi-sensor positioning data, the sensor positioning data with a large \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Index** & **TCPF-Z + G** & **TCPF-Z + B** & **BPF-Z + G/B** & **TCPF-Z + G/B** \\ \hline Average positioning error & 2.558 & 2.606 & 2.532 & 1.828 \\ Maximum positioning error & 3.438 & 4.013 & 3.663 & 2.676 \\ RMSE & 2.541 & 2.648 & 2.588 & 1.860 \\ Std & 0.368 & 0.605 & 0.381 & 0.332 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data positioning test results in section A.  
Figure 8: Trajectory fusion results of the unmanned ship based on different algorithms in the A/B river: (**a**) section A positioning trajectory; (**b**) section B positioning trajectory. confidence factor will be fused and sampled first, which improves the sampling efficiency of the TCPF algorithm. Because the confidence factor reflects the credibility of the data, the larger the confidence factor was, the higher the reliability of the data sample was, which further improved the reference value of the sensor-information fusion sample, and further improved the fusion filtering accuracy of the TCPF algorithm for multi-input data, and finally achieved the accurate positioning of the unmanned ship. The calculations show that, compared with the other three algorithms, the average positioning error, RMSE and Std performances of the unmanned ship, based on the TCPF algorithm during navigation in river A, decreased by 22.0%, 20.0%, and 17.0%, respectively, and the average positioning error, RMSE, and Std in river B decreased by 18.0%, 19.0%, and 18.0%, respectively. The three performances of the unmanned ship in the whole experimental river decreased by an average of 20.0%, 19.5%, and 17.5%, respectively, which fully demonstrates that the TCPF algorithm has a better optimization performance and fault-tolerant performance compared with the basic particle-filter algorithm, and improves the ship's positioning accuracy under the interference of the unmanned-ship environment.
_Applied_  
_sciences_  
Numerical Investigation of Energy Saving Characteristic in Building Roof Coupled with PCM Using Lattice Boltzmann Method with Economic Analysis  
**[PERSON] 1**, [PERSON] 1 and [PERSON] 2,*(c)  
1 School of Management, Xi'an University of Architecture and Technology, Xi'an 710055, Shaarxi, China; [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (X.D.)  
2 Key Laboratory of Thermo-Fluid Science and Engineering of MOE, School of Energy and Power Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaarxi, China  
* Correspondence: [EMAIL_ADDRESS]  
Received: 10 September 2018; Accepted: 25 September 2018; Published: 26 September 2018  
Featured Application: The current work has potential applications in economically designing green buildings in the summer hot and winter cold region of China.  
**Abstract:** Due to their characteristics of high energy storage density and a nearly constant melting temperature, phase change materials (PCMs) could be inserted into the roof of green buildings in order to reduce the energy consumption and ameliorate the room thermal comfort. In this paper, an enthalpy based multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) was developed to calculate the transient phase change conjugate heat transfer with solar radiation inside the green building's PCM roof in the hot summer and cold winter areas of China. The effect of the PCM melting temperature on the variation of the roof internal temperature was investigated and the energy saving characteristic of the PCM roof under an intermittent energy utilization condition was also analyzed by comparing with the performance of the roof filled with sensible insulating materials (SIMs). Then, the life cycle incremental costs and incremental benefits of a PCM roof and SIM roof were studied by using the comprehensive incremental benefit model so that the green building roof could be economically evaluated. The results indicate that a temperature rise inside the roof during summer cooling time could be delayed due to the latent heat of the PCMs. It was also found that the melting temperature and the thickness of the PCM layer should be chosen appropriately for enhancing the energy saving amount of a PCM roof. Based on this, the PCM roof could have a better energy saving capability than the SIM roof. During the winter heating time, as the environment temperature and the room temperature are both below the PCM melting temperature, the PCM roof does not have a latent heat characteristic so that it performs like a SIM roof. Furthermore, due to the high price of PCMs, the incremental cost of green building is increased, which makes the PCM roof have a negative comprehensive incremental benefit. Under this circumstance, developing PCMs with a low price and stable chemical properties is a key scientific bottleneck for a wider application of PCM roofs in the architecture engineering field.  
**Keywords:** hot summer and cold winter area; PCM roof; comprehensive incremental benefit; conjugate phase change heat transfer; lattice Boltzmann method