esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Potential Chemical Biomarkers of P. mahaleb _Honey_  
One of the objectives of volatiles screening is to identify specific or nonspecific chemical biomarkers (particularly phytochemicals) of the honey's botanical origin. Compounds with high percentages are of particular interest, even if not specific and/or exclusive for the plant source since their different concentrations could be used for distinguishing among the honey types. Figure 1. presents selected structures of the most prominent volatile organic compounds used for characterization of _P. mahaleb_ honey.  
Screening of _P. mahaleb_ honey volatiles revealed the presence of the C\({}_{15}\)-norisoprenoid vomifoliol with high percentages in both extracts (Table 1) followed by minor percentages of C\({}_{9}\)- and C\({}_{13}\)-norisoprenoids, mainly in the headspace. Previous studies showed that the types and amounts of norisoprenoids, a class of carotenoid-derived compounds with 3,5,5-trimethylcyclohex-2-enic structures, are related to the honey's botanical origin, and have been proposed as chemical markers of honey floral source. Some norisoprenoids, such as \(\alpha\)-isophorone and dehydrovomifoliol, have been suggested by many authors as markers for heather honeys (_Calluna vulgaris_ L.) [11]. However, high percentages of vomifoliol can be considered characteristic for _P. mahaleb_ honey, but not exclusively, since we also found vomifoliol to be abundant in _Mentha_ spp. honey volatiles [17]. In addition, in our previous research [6], norisoprenoids were isolated by hydrodistillation with minor percentages in the flowers, leaves and bark of _P. mahaleb_, particularly C\({}_{13}\)-norisoprenoids (\(E\))-\(\beta\)-damascenone, (\(E\))-\(\alpha\)-ionone and (\(E\))-\(\beta\)-ionone, as well as the C\({}_{9}\)-norisoprenoid \(\alpha\)-isophorone.  
Coumarin (\(2H\)-chromen-2-one), found in the extracts and headspace (Tables 1 and 2), is another individual compound that can be highlighted in the scope of _P. mahaleb_ honey discrimination from other honey types. Coumarin is not common among honey volatile compounds, but it is found in the honeys of _Lavandula angustifolia_ Mill., _L. angustifolia_\(\times\)_latifolia_ and _L. hybrida_ Reverchon II, where it served, among others, as a freshness indicator [18,19]. A possible biosynthetic pathway of coumarin generation is lactonisation of _cis_-coumarinic acid liberated by the hydrolysis from the corresponding \(\beta\)-glucoside [19]. In addition, coumarin is a pleasantly fragment chemical compound found in different parts of _P. mahaleb_ plant [6]: bark (34.1%), flowers (2.3%), leaves (5.1%) and wood (4.5%).  
Although linalool derived isomers of lilac aldehyde and/or lilac alcohol were found in the headspace of _P. mahaleb_ honey, they are dominant volatiles from nodding thistle honey, citrus honey and _Paliurus spina-christi_ Mill. honey [20-22] and therefore cannot be considered specific.
## 4 Results and Discussion
#### 4.1.3 Running Speed of the HST
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Running**} & \multicolumn{5}{c}{\(\Delta\)\(\mathcal{C}\)} & \multicolumn{5}{c}{**Exit**} \\
**Speed (km/h)** & **Carriage** & \multicolumn{2}{c}{**Enter**} & \multicolumn{2}{c}{**Exit**} \\  & & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}}\) & \(\mathbf{C_{M}}\) & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}}\) & \(\mathbf{C_{M}}\) \\ \hline \multirow{4}{*}{200} & Head & 0.1320 & 0.1094 & 0.0168 & 0.1211 & 0.1090 & 0.0164 \\  & Middle & 0.0592 & 0.0420 & 0.0109 & 0.0551 & 0.0445 & 0.0104 \\  & Tail & 0.0477 & 0.0237 & 0.0123 & 0.0310 & 0.0122 & 0.0133 \\ \hline \multirow{4}{*}{250} & Head & 0.1003 & 0.0714 & 0.0135 & 0.0947 & 0.0640 & 0.0137 \\  & Middle & 0.0371 & 0.0567 & 0.0045 & 0.0357 & 0.0556 & 0.0040 \\  & Tail & 0.0265 & 0.0136 & 0.0084 & 0.0240 & 0.0079 & 0.0087 \\ \hline \multirow{4}{*}{300} & Head & 0.0824 & 0.0430 & 0.0117 & 0.0771 & 0.0396 & 0.0116 \\  & Middle & 0.0234 & 0.0566 & 0.0021 & 0.0233 & 0.0515 & 0.0021 \\  & Tail & 0.0211 & 0.0125 & 0.0042 & 0.0191 & 0.0113 & 0.0042 \\ \hline \multirow{4}{*}{350} & Head & 0.0701 & 0.0306 & 0.0106 & 0.0647 & 0.0267 & 0.0100 \\  & Middle & 0.0164 & 0.0485 & 0.0011 & 0.0153 & 0.0441 & 0.0012 \\ \cline{1-1}  & Tail & 0.0153 & 0.0211 & 0.0023 & 0.0174 & 0.0204 & 0.0026 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Amplitude of changes in aerodynamic load coefficients for the HST passes through the curved wind barrier at different running speeds. Figure 11: Influence of running speed on the aerodynamic load coefficients: (**a1–a3**) head car, (**b1–b3**) middle car, and (**c1–c3**) tail car.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 4 Model System
### Task Offloading Model
#### 4.5.1 Associated Edge Nodes Perform Tasks  
If the mobile terminal user offloads the task to its associated edge node and this edge node has service cache, then the execution delay consists of two parts: first, the transmission delay for the mobile terminal user to send the task to the edge node; second, the computation delay for the edge node to execute the task.  
\[T_{ch1}=T_{n,m}^{tran,d}+T_{n,m}^{comp,e} \tag{12}\]  
Figure 3: Schematic diagram of mobile edge computing task unloading process.  
The energy consumption corresponding to the two operation delays is  
\[E_{c\lambda 1}=E_{n,m}^{tran,d}+E_{n,m}^{comp,e} \tag{13}\]  
If the mobile terminal user offloads the task to its associated edge node and this edge node does not have service cache, then the execution delay consists of three parts: (1) the transmission delay for the mobile terminal user to upload the task to the edge node; (2) the transmission delay for the associated edge node to download service cache from the central cloud; (3) the computation delay for the associated edge node to execute the task.  
\[T_{c\lambda 3}=T_{n,m}^{tran,d}+T_{n,m}^{comp,e}+T_{m}^{tran,e} \tag{14}\]  
The energy consumption corresponding to the three operation delays is  
\[E_{c\lambda 3}=E_{n,m}^{tran,d}+E_{n,m}^{comp,e}+E_{c}^{tran,e} \tag{15}\]
## 1 Introduction
### The Use of Binaural-Based Spatial Audio  
Primarily, the use of virtual reality to treat anxiety and other psychiatric disorders often relies on the accurate visual representation of the feared stimuli, with sound playing an accompanying and lesser role [43; 44]. However, spatial sound can have direct impact on immersion and presence felt within a virtual environment. These are one of the essential psychological functions associated with triggering anxiety in virtual reality exposure therapy (VRET) [45].  
Although 3D sound is important in creating an immersive virtual experience, there is limited research with mixed results investigating its capability to induce feelings of anxiety. [PERSON] et al. [46] compared the effects of different audio rendering techniques over headphones and measured the participants self-reported levels of anxiety towards a virtual wasp. The experiment exposed individuals to the sound of the wasp flying around an indoor VR space. Results showed that compared to mono, stereo and 5.1 surround, 3D audio generated significantly higher levels of presence and anxiety.  
In addition, [PERSON] [47] developed a soundscape exposure therapy that made use of CBT techniques and higher order ambisonics rendered over multiple loudspeaker arrays. The program was built not to tackle a specific phobia but rather aimed at those who experience general feelings of anxiety. The produced soundscapes did not aim to be a realistic reconstruction of an auditory environment. Instead they were designed to be hyperreal, with the use of strange spatial panning and universally anxiety-eliciting sounds. During exposure sessions, participants experienced feelings of anxiety and stress. However, following the intervention program they became habituated to the physical symptoms of anxiety they experienced in everyday life.  
Within virtual reality applications, the realistic rendering of three-dimensional auditory environments via headphones is known as binaural-based spatial audio. This is achieved through filters which simulate the free field acoustic path from a sound source to the ear canal. These filters are known as Head Related Transfer Functions (HRTFs) [48]. HRTFs contain direction dependant information such as interaural differences in time and level and spectral shaping caused by the torso, head and pinnae [48]. Binaural rendering can subsequently be achieved via the convolution of a monaural anechoic audio signal with the appropriate HRTF for a given point in space for each ear [49; 50].
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 4 Analysis of Algorithm Results
### Objective Evaluation Indicators
#### 4.3.4 Information Entropy  
Information entropy (_E_) is used to measure the information richness of an image, which is greater than zero. The larger the value of \(E\), the more information and details are present in the image. However, a large value of \(E\) also indicates significant noise in the image. It is calculated as follows:  
\[E=-\sum_{i=0}^{255}p_{i}\log p_{i} \tag{26}\]  
where \(p_{i}\) denotes the proportion of pixels with gray value \(i\) in the image.
## A Charrelation Matrix-Based Blind Adaptive Detector for DS-CDMA Systems  
**[PERSON] * and [PERSON]**  
National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-135-1810-3691.  
Academic Editor: [PERSON]  
_Received: 1 July 2015 / Accepted: 10 August 2015 / Published: 14 August 2015_  
**Abstract:** In this paper, a blind adaptive detector is proposed for blind separation of user signals and blind estimation of spreading sequences in DS-CDMA systems. The blind separation scheme exploits a charrelation matrix for simple computation and effective extraction of information from observation signal samples. The system model of DS-CDMA signals is modeled as a blind separation framework. The unknown user information and spreading sequence of DS-CDMA systems can be estimated only from the sampled observation signals. Theoretical analysis and simulation results show that the improved performance of the proposed algorithm in comparison with the existing conventional algorithms used in DS-CDMA systems. Especially, the proposed scheme is suitable for when the number of observation samples is less and the signal to noise ratio (SNR) is low.  
**Keywords:** blind source separation; DS-CDMA; charrelation matrix; joint diagonalization
## 3 Results and Discussion
### Process Simulation  
The simulation result obtained using SuperPro Designer(r) v9.0 showed that fermentation time plays a significant role in determining the flowrate of products coming out of the fermenter when the kinetic parameters in Table 4 are applied. Figure 5 shows that the fermenter's output flowrate of bioethanol, glucose and yeast varied according to the fermentation time. The glucose flowrate decreased with increasing fermentation time as it was used as the substrate for bioethanol production. On the other hand, the bioethanol and yeast flowrates increased with increasing fermentation time, as they were both produced during fermentation. The glucose was completely consumed when the fermentation time was set to 192 h. At this time, the flowrate of produced bioethanol was at a maximum. Thus, the fermentation time for this hand sanitiser plant was set to 192 h to obtain the maximum bioethanol yield with the minimum batch time.  
Figure 4: Arrhenius plot for SSF to determine \(\mu\) max (**a**), \(k\)s (**b**) and \(kd\) (**c**).  
When the fermentation time was set to 192 h, the flowrate of produced bioethanol was 681 kg per batch, with a concentration of 3.4%. The plate and frame filtration could separate solid and liquid fractions of fermentation broth, resulting in an increased bioethanol concentration of 3.5% in the liquid output stream. The first distillation process in the beer column increased the bioethanol concentration to 79.3% with the remaining water. The second distillation process in the rectifying column finally increased the bioethanol concentration to 96.2%, as required by the WHO for use as a hand sanitiser raw material. At this point, the flowrate of bioethanol decreased to 651 kg due to some losses in the previous separation processes. Bioethanol was then mixed with the other raw materials, resulting in a final hand sanitiser product with a composition of 80.2% ethanol, 18.3% water, 1.4% glycerol and 0.1% hydrogen peroxide, as required by WHO standards. The overall results showed that this hand sanitiser plant design could produce 812.7 kg of hand sanitiser from 2000 kg of OPEFB for every batch.  
The recipe scheduling information feature in SuperPro Designer(r) v9.0 for this hand sanitiser plant showed that the batch time and minimum cycle time (excluding equipment shared across batches and auxiliary equipment) were 219 and 194 h, respectively, with a total of 40 batches per year. This plant could, therefore, produce 32,506.16 kg of the total flow of hand sanitiser as the main product per year.
# Multi-View Masked Autoencoder for General Image Representation
## 4 Experiments
### Ablation Studies  
We ablated studies on the main properties of our framework, which included two different target views and contrastive losses. The ImageNet-1K top-1 accuracy was used for evaluation. Note that when two main properties were removed, the model was the same as the baseline, the MAE. For a fair comparison, all of the models were trained at the same setting: fine-tuning after pre-training for 200 epochs, with a batch size of 512.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mask Ratio** & **Accuracy** & **Loss Weight** & **Accuracy** & **Decoder Depth** & **Accuracy** \\ \hline
50\% & 79.22 & 0.1 & 78.10 & 1 & 74.89 \\
75\% & 79.09 & 0.5 & 78.09 & 2 & 78.56 \\
80\% & **79.25** & 1 & 78.09 & 4 & **80.03** \\
90\% & 79.23 & 1.5 & 79.31 & 8 & **80.03** \\
95\% & 78.30 & 2.0 & **79.64** & 12 & 79.11 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fine-tuning results on different masking ratios, loss weights, and decoder depths. Best results are shown in bold.  
Table 6 shows the results of the ablation experiments. Among all of the methods, ours, with two different target views and contrastive losses, performed the best, thus showing 1.23%, 4.76%, and 2.61% performance gains, respectively, compared to the other methods. When any of the components was removed, it was shown to be less effective or showed only marginal performance increments compared to the baseline. According to these results, we deduce that each component benefits mutually in learning rich image representation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Accuracy** \\ \hline Ours & **79.09** \\ Ours w/o two different targets & 77.86 \\ Ours w/o contrastive loss & 74.33 \\ Baseline [23] & 76.48 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation experiment results. Best result is shown in bold.  
Figure 8: Visualization of reconstruction of different decoder depths for qualitative evaluation. (**a**) is the original input image, (**b**) is the masked image, (**c**) and (**d**) feature the predicted image with 4-layer and 8-layer, respectively.
# A Robust Dual-Platform GMTI Method against Nonuniform Clutter
## 5 Conclusions  
The traditional CCM estimation is based on the training samples near the CUT. In heterogeneous environments, such as clutter with strong pollution, junction zone between hot and cold clutter, and clutter with nonuniform power, the training samples are nonuniform, which degrades the detection performance of moving targets. In this paper, a high-precision CCM estimation method based on a dual-platform radar system was proposed, which can directly calculate the CCM of the CUT using the former platform SAR images without moving targets in the same area, instead of using training samples for the estimation. The experimental results based on simulation data proved the effectiveness of this method.  
Conceptualization, M.Z. and G.J.; methodology, M.Z.; software, M.Z., Z.H. and L.L.; validation, M.Z., G.J. and L.L.; formal analysis, M.Z.; investigation, M.Z.; resources, M.Z., G.J. and Z.H.; writing--original draft preparation, M.Z.; writing--review and editing, M.Z., G.J., L.L. and Z.H.; visualization, M.Z.; supervision, L.L.; project administration, G.J.; funding acquisition, G.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant no. 62101568.  
The authors declare no conflict of interest.
# GIS-Based Solar Radiation Mapping, Site Evaluation, and Potential Assessment: A Review
## 2 Solar Radiation Mapping Using GISs
### Spatial Solar Radiation Mapping Using Interpolation Methods  
GIS maps facilitate the assessment of radiation at various locations and times without measurement equipment. Because solar data are not available for all potential sites, there have been some attempts to estimate the spatial distributions of solar radiation or other parameters.  
Probable relationships among the radiation-related parameters can be used to map the global solar radiation at sites where data were not available [11]. The interpolation method based on inverse distance weight or spatial autocorrelation is a very useful tool for solar radiation mapping. The interpolation methods have been widely used in many studies in various countries [12; 13]. These methods have the advantage of predicting the entire area of the solar radiation in the form of grids based on the data of the stations where observations are made. In addition, solar radiation mapping based on satellite image can be performed using the certified method and it can be verified through observation data. For example, a solar atlas for Pakistan was made using Meteosat-7 satellite data by validating it with ground measurements based on the ESMAP approach. To adapt the radiation data to the measured datasets, the impacts of different aerosol models were evaluated [14].
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 3 MEC of Square Inductor
### Inductor Calculation Method  
Since the core reluctance is a function of the branch flux, the mesh method is used to iteratively calculate the MEC branch flux until convergence, which is described in [20; 21]. The inductance can be obtained by the magnetic flux linkage method and the incremental inductance [22] can be calculated by using the differential method in Equation (10).  
\[L_{inc}=\frac{\sum\lambda_{n,i}-\sum\lambda_{n,i-1}}{\Delta i}=\frac{\sum \Phi_{n,i}\cdot N_{n,i}-\sum\Phi_{n,i-1}\cdot N_{n,i-1}}{\Delta i}, \tag{10}\]  
where \(\lambda_{n,i}\) is the flux linkage of branch \(n\) at current \(i\), and \(\lambda_{n,i-1}\) is the flux linkage of branch \(n\) at current \(i-1\).
## 4 Case Study: Searching Physical Models in MATLAB Simulink
#### Analysis of Results
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Level of “Goodness”** & **Precision** & **Recall** \\ \hline Acceptable & \(\geq\)20\% & \(\geq\)60\% \\ Good & \(\geq\)30\% & \(\geq\)70\% \\ Excellent & \(\geq\)50\% & \(\geq\)80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Goodness levels for the precision and recall metrics [36]. \begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Precision** & **Recall** & **F1** \\ \hline Q1 & 1.000 & 1.000 & 1.000 \\ Q2 & 1.000 & 1.000 & 1.000 \\ Q3 & 1.000 & 1.000 & 1.000 \\ Q4 & 1.000 & 0.941 & 0.970 \\ Q5 & 1.000 & 0.800 & 0.889 \\ Q6 & 1.000 & 0.842 & 0.914 \\ Q7 & 0.821 & 0.788 & 0.804 \\ Q8 & 1.000 & 1.000 & 1.000 \\ Q9 & 1.000 & 0.941 & 0.970 \\ Q10 & 0.375 & 0.462 & 0.423 \\ Q11 & 1.000 & 1.000 & 1.000 \\ Q12 & 1.000 & 1.000 & 1.000 \\ Q13 & 1.000 & 0.889 & 0.941 \\ Q14 & 1.000 & 1.000 & 1.000 \\ Q15 & 1.000 & 0.750 & 0.857 \\ Q16 & 1.000 & 1.000 & 1.000 \\ Q17 & 0.682 & 0.621 & 0.650 \\ Q18 & 1.000 & 0.933 & 0.965 \\ Q19 & 1.000 & 1.000 & 1.000 \\ Q20 & 1.000 & 0.571 & 0.727 \\ Q21 & 1.000 & 1.000 & 1.000 \\ Q22 & 1.000 & 1.000 & 1.000 \\ Q23 & 1.000 & 0.889 & 0.941 \\ Q24 & 1.000 & 1.000 & 1.000 \\ Q25 & 1.000 & 0.857 & 0.923 \\ Q26 & 1.000 & 0.889 & 0.941 \\ Q27 & 0.835 & 0.723 & 0.775 \\ Q28 & 1.000 & 0.873 & 0.932 \\ Q29 & 1.000 & 0.922 & 0.959 \\ Q30 & 1.000 & 1.000 & 1.000 \\ \hline Avg & 0.925 & 0.865 & 0.884 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Precision, recall, and F1 metrics for each query. embedding, our method retrieves models with a higher level of precision and recall than the work presented in [25]. The proposed approach has the potential to save time and resources by allowing engineers to quickly identify and reuse relevant physical models. The results for the precision, recall, and F1 metrics for each query, shown in Table 3, provide valuable insights that can help to address the two questions presented in this case study. (1) It is evident that the implemented semantic model search engine utilising pattern matching, vector representations, and cosine similarity is able to successfully retrieve relevant models for the majority of queries. (2) Moreover, the search engine demonstrates excellent accuracy, providing relevant and suitable results for the majority of the executed queries. However, while the results are encouraging, we acknowledge that more work is needed to further improve the performance of the algorithm. In particular, we recommend conducting experiments in larger settings that incorporate real user needs, such as queries and large physical interconnected models. Testing the proposed approach with a wider range of use cases and user requirements will make it possible to better understand how it performs in real-world situations as well as to identify opportunities for further refinement.
# Optimization of Sensor Positions and Orientations for Multiple Load Case Scenarios
## 2 Materials and Methods
### Optimization Problem Formulation  
Let the structural domain be denoted by \(\mathbf{\Omega}\) and the sensor placement region be \(\mathbf{\Omega_{s}}\subseteq\mathbf{\Omega}\), as shown in Figure 1. The goal is to optimally place and orient a limited number of sensors within \(\mathbf{\Omega_{s}}\) such that the difference between the maximum structural response and sensor-detected response is minimized under all load cases.
# Application of Laser-Ultrasonics for Evaluating Textures and Anisotropy
###### Abstract  
Various approaches are reviewed for determining elastic anisotropy and its coupling to crystallographic texture, with special reference to ultrasonic measurements. Two new methods are described for measuring the anisotropy of P-wave velocity using laser-ultrasonics. Making measurements across the diameter of a cylindrical specimen as it is rotated makes it possible to maintain a very constant known path length. This permits extremely accurate measurements with a precision of better than 0.01%. Results on 316 stainless steel in different conditions are compared with calculated values obtained from EBSD textures together with measured densities and crystalline coefficients from the literature. Excellent agreement is obtained when applying the Hill geometrical average procedure. A similar approach is adopted to measure the variation of wave velocity in a martensitic steel, after tempering at a range of temperatures. Changes in the anisotropy associated with thermal softening are discussed. The second method uses Galvano mirrors to steer the generating laser to different positions over a sheet surface, allowing wave velocities to be determined along different directions in the anisotropic material.  
laser-ultrasonics; texture; anisotropy; stainless steel; martensite +
Footnote †: journal: Applied Sciences  
19 September 2022  
Accepted: 16 October 2022  
19 October 2022