esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Model Performance Indicator Comparison  
To gain an in-depth understanding of the various performance effects of the lightweight neural network we proposed, this article conducted comparative experiments using lightweight neural network models LeViT, EfficientNetV2, MobileViTv2, EdgeViTs, EdgeNeXt, and AFFNet. The experimental comparison results are shown in Table 6.  
Table 6 shows that the proposed lightweight neural network's performance effects are significantly better than the lightweight neural network models LeViT, EfficientNetV2, MobileViTv2, EdgeViTs, EdgeNeXt, and AFFNet. Model performance index comparison experimental results show that the lightweight neural network model we proposed has the lowest number of parameters, the smallest number of operations, and the smallest occupied memory capacity compared to the above six models, and the model's overall performance is the best.  
To further understand the timing performance of our proposed model, we used the Android platform to conduct comparative experiments on the inference time of the Line model and lightweight neural network models such as LeViT, EfficientNetV2, MobileViTv2, EdgeViTs, EdgeNeXt, and AFFNet. The comparative experimental results are shown in Table 7.  
Among the hardware platforms used in our inference speed performance comparison experiments, the operating system is Android 11, the display chip is Mali G52 2 EE, the CPU is Allwinner A133, the running memory is 4 GB, and the memory capacity is 64 GB. As shown in Table 7, the proposed model has the fastest inference speed on the same hardware platform.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Basic Algorithm** & **Parameter** & **Number of** & **Memory Capacity** \\  & **Quantity/MB** & **Operations/G** & **Occupied/MB** \\ \hline LeViT [35] & 11.93 & 11.97 & 192.97 \\ EfficientNetV2 [33] & 11.78 & 11.39 & 188.85 \\ MobileViTv2 [36] & 11.25 & 11.26 & 186.78 \\ EdgeViTs [37] & 10.27 & 10.86 & 185.63 \\ EdgeNeXt [38] & 9.55 & 9.98 & 170.35 \\ AFFNet [39] & 8.98 & 6.59 & 160.79 \\ Linge & 7.63 & 5.91 & 155.28 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of model performance indicators with experimental results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Basic Algorithm** & **Inference Time (Seconds)/Photo** \\ \hline LeViT [35] & 5.38 \\ EfficientNetV2 [33] & 4.89 \\ MobileViTv2 [36] & 4.61 \\ EdgeViTs [37] & 4.57 \\ EdgeNeXt [38] & 4.15 \\ AFFNet [39] & 3.93 \\ Linge & 3.26 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model timing performance comparison experimental results.
# In Silico Modeling Study of Curcumin Diffusion and Cellular Growth
## 4 Conclusions  
In the present work, the integration and development of two models in Morpheus--a 2D fibroblast culture growth model and a curcumin diffusion model--were successfully achieved. Cell proliferation was controlled by [PERSON]'s cell cycle model [32], achieving a simple yet overall realistic representation of this biological process. Curcumin release was accomplished with two models, Korsmeyer-Peppas and [PERSON]'s hyperbolic, using the experimental values reported in [17], with a release ratio of 0.91. Notably, a scratch-wound assay was recreated to simulate curcumin's stimulative and inhibitory dose-dependent effects on wound healing, as described in numerous studies. The model was tested with six key concentrations--0.25, 0.5, 1, 5, 10, and 20 uM--and the final confluence of 447 initial seeded cells was compared to a control group.  
Our results were similar to those described in experimental reports, especially in vitro scratch-wound assays. The stimulative effect of low curcumin doses was successfully recreated as cell migration, and a higher confluence than the control was obtained. The inhibitory effect for doses higher than 10 uM was achieved to reduce migration and cell proliferation. Thus, this model could be used for research into curcumin's wound-healing effects as an auxiliary tool for experimental work. However, cell apoptosis was not implemented. This significant effect could be considered for future work to generate a more representative model.  
Similarly, the parameters could be adjusted to the fibroblast growth model by implementing an in vitro fibroblast proliferation assay and using the experimental results as the initial values. Likewise, conducting in vitro scratch-wound assays treated with different curcumin doses and evaluating their effects can help to increase understanding and define curcumin therapeutic doses. Regarding the integrated model, some processes could be automatized, e.g., in the current model, the probability of \(p_{1}\) and \(p_{2}\) must be manually changed depending on the simulated curcumin concentration.  
**Author Contributions:** Conceptualization, R.P.-S., E.M.M.-M. and J.E.S.-H.; methodology, E.M.M.-M., J.E.S.-H., A.S.-H. and N.U.-A.; formal analysis, O.A. and F.R.-P.; investigation, E.M.M.-M., J.E.S.-H., O.A., A.S.-H., N.U.-A. and F.R.-P.; data curation, A.S.-H., N.U.-A., F.R.-P. and O.A.; writing--original draft preparation, E.M.M.-M., J.E.S.-H., A.S.-H., N.U.-A., O.A. and F.R.-P.; visualization, E.M.M.-M., J.E.S.-H. and O.A.; writing--review and editing, E.M.M.-M., J.E.S.-H., F.R.-P., O.A. and H.M.N.I.; supervision, R.P.-S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Index Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to their current utilization for future works involving the authors of this paper.  
The authors declare no conflict of interest.
## 1 Introduction
Despite their popularity in sleep apnea problems, a major limitation in classical rule-based threshold detectors is that they provide classification based on simple comparison for the features against experimentally derived thresholds while overlooking the statistical distributions for the input features as well as the output classes. Even more complex discriminative (black box) methods are based on learning a function that maps the features directly into decisions. There hasn't been much research considering probabilistic view of classification for sleep apnea detection. Gaussian Mixture Model (GMM) is a probabilistic machine learning framework that aims at providing a richer class of density models than single Gaussian using a finite weighted mixture of Gaussian densities. It is well known as a rich framework capable of characterizing any continuousdensity. This framework has also shown promising results in classification problems including noisy features [48]. Nevertheless, it hasn't been well evaluated for sleep apnea detection problems. The contribution of this paper is two fold. First, we develop a probability based classification approach for automated detection of sleep events using single oronasal airflow record. Second, we study the performance of the proposed approach over a large data set of 96 patients of different sleep apnea severity levels. Finally, we conduct a comprehensive evaluation and comparison of the proposed probabilistic framework against a rule-based classifier for the same input features as well as two previously published algorithms for apnea detection using airflow signal. This paper is organized as follows. Section 2 describes the data set, the proposed algorithm, classification methods, and evaluation metrics. Section 3 presents results for the proposed algorithm along with a detailed comparison with related works. Section 4 discusses the results and lessons learned and 5 summarizes conclusion of the paper.
# Effect of Tomato Juice and Different Drying Methods on Selected Properties of Courgette
## 2 Materials and Methods
### Drying Methods  
2.3.1. Convective Drying  
Drying was carried out using a convection dryer designed and built at the Institute of Agricultural Engineering (Wroclaw, Poland) [16]. The convection dryer consisted of six chimneys. Drying was performed at a temperature of 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C; the air flow rate was 1 m\(\cdot\)s\({}^{-1}\). Drying was carried out according to the methodology described by [PERSON] et al. [15] to obtain equilibrium moisture content. Drying time ranges from 130 to 240 min depending on the drying temperature and pretreatment process. The process was performed in three technological repetitions. The courgette was dried to equilibrium moisture content. The process parameters were determined based on our previous studies.  
2.3.2. Vacuum Drying  
Vacuum drying was performed in a laboratory dryer (Memmert, VO101, Schwabach, Germany) at 60 \({}^{\circ}\)C at a vacuum pressure of 10 kPa for 24 h. The process parameters were determined based on our previous studies [15].  
2.3.3. Freeze Drying  
Freeze drying was carried out in a 4.5 L FreeZone unit (Laboconco, Fort Scott, KS, USA). Samples were frozen at \(-\)20 \({}^{\circ}\)C. Drying was carried out according to the methodology described by [PERSON] et al. [15].  
2.3.4. Methodology  
Fresh and dried vegetables, without pretreatment and vacuum-impregnated, were subjected to the following tests.  
2.3.5. Water Activity (AW)  
Determination of water activity in fresh material and after the drying process was performed using an AquaLab 4 TE \(\pm\) 0.003 apparatus (AquaLab, Warsaw, Poland) maintaining a constant temperature (25 \({}^{\circ}\)C). The result was the average of three repetitions [13].  
2.3.6. Dry Matter (_DM_)  
Dry mass was determined using the vacuum method. The measurement was performed for 24 h at a pressure of 10 kPa and a temperature of 70 \({}^{\circ}\)C. Precisely weighed courgette samples of 0.5 g were subjected to the drying process. Drying was carried out using a pressure of 10 kPa at 70 \({}^{\circ}\)C for 24 h. The result was the average of three repetitions [17].  
Dry matter content was calculated according to the following equation:  
\[DM=1-\left(\frac{m_{t}-m_{d}}{m_{t}}\right)\cdot\text{100} \tag{1}\]  
where:  
_DM_ is the dry matter [%].  
\(m_{t}\) is the wet sample weight [g].  
\(m_{d}\) is the dry sample weight [g].
## 2 Materials and Methods
### PC MRI Acquisition and Vessel Segmentation  
PC MRI was performed using the scanning parameters in Table 1. The imaging slice was planned between C2 and C3 of the subject perpendicular to the vessels based on MR angiography. The magnitude and phase component (superior-inferior direction) of each PC MRI data were averaged across 10 cardiac phases retrospectively to create mean magnitude and phase images, which were used as the input for training the CNN network. As shown in Figure 2, the input data for the deep learning model was generated by concatenating the mean magnitude and phase components of the PC MRI data, resulting in a \(2\times 512\times 512\times 1\) tensor for each case.  
Figure 1: Flowchart showing the inclusion and exclusion criteria for this study. Overall, 153 subjects were enrolled; PC MRI data in 53 HC subjects and 16 Moyamoya patients were included in the baseline (pre-Diamox) condition; data in 54 HC subjects and 30 Moyamoya patients were included in the post-Diamox condition.  
Using the mean magnitude images of the PC MRI data, six vessels of interest (right internal carotid artery [RICA], left internal carotid artery [LICA], right vertebral artery [RVA], left vertebral artery [LVA], right external carotid artery [RECA], left external carotid artery [LECA]) were segmented manually using the FSLeyes software version 6.0.7 (FSL, Oxford, UK). These segmented images were considered as the output (ground truth) for training the CNN network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline TR/TE & ms & 12.4/4.6 \\ \hline Flip angle & degrees & 20 \\ \hline FOV & mm & \(180\times 180\) \\ \hline Matrix & & \(512\times 512\) \\ \hline Voxel size & mm & \(0.3516\times 0.3516\times 3\) \\ \hline Cardiac phases & & 10 \\ \hline Average time per cardiac phase & ms & 88 \\ \hline Slice thickness & mm & 3 \\ \hline Number of slices & & 1 \\ \hline Velocity encoding & cm/s & 100 \\ \hline Repeats & & 2 \\ \hline Scan duration & min & 1:30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scanning parameters for phase contrast MRI.  
Figure 2: Deep learning network designs. (**a**) Attention U-Net and (**b**) Nested U-Net architecture. These two architectures differ on their approach to improved performance by using attention gates and extra skip-connections. U-Net without attention and nested structures were also used.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
## 2 RCS of Chipless RFID Arrays
### RCS Modelling  
When a tag array is illuminated by a linearly polarised instantaneous E-field wave (\(E_{i}\)), a reflected wave (\(E_{r}\)) is observed at the receiver. The following mathematical formula gives the relationship between the RCS of the chipless tag, incident, and reflected waves [22]:  
\[\sigma_{\text{tag}}(f,\psi)=\lim_{d\to\infty}4\pi d^{2}\frac{|E_{r}(f,\psi)|^{ 2}}{|E_{i}(f,\psi)|^{2}} \tag{1}\]  
where \(\sigma_{\text{tag}}(f,\psi)\) is the RCS of the chipless tag, \(f\) is the frequency of the chipless tag, \(\psi\) is the transpose vector of the angles of incident and reflected waves, which equal to \(\left[\theta_{i},\phi_{i},\theta_{r},\phi_{r}\right]^{\text{T}}\). \(\theta_{i,r}\) is equal to the elevation, \(\phi_{i,r}\) is equal to the azimuth of the incident and reflected waves, and \(d\) is the distance between the chipless tag and the reader.  
When considering the scattering intensity of a chipless array in a particular direction, it is directly proportional to the physical size of the scattering array. Additionally, it depends on the interference between the different scattering element contributions on the array. Since the chipless array combines various unit cells, each with its unique reflection coefficient, the overall scattering pattern at a given frequency can be calculated similarly to an antenna array. Therefore, the monostatic RCS of the tag array is calculated by weighting  
Figure 1: RCS enhancement techniques.  
the normalised pattern towards the direction with the same RCS as a plate of equal area as the total scatterer [15]:  
\[\sigma_{\text{array}}(f,\psi)=\frac{4\pi A^{2}}{\lambda^{2}}\Big{(}\frac{|E_{ \text{r}}(f,\psi)|}{MN\cos(\theta_{r})}\Big{)}^{2} \tag{2}\]  
where \(A\) is the total geometric area of the array, \(\lambda\) is the wavelength, and \(M\) and \(N\) are the elements of the array in the x and y directions as shown in Figure 2.
## 6 Conclusions  
Two broadband spectral analysis algorithms with an ultra-high-frequency resolution, namely, the IWOLA + FFT algorithm and the IWOLA + WOLA algorithm, are proposed in this paper. The IWOLA + FFT algorithm was constructed by combining an IWOLA filter bank with FFT, in addition to a specific decimation for the output data from the IWOLA filter bank and the data selection for the output data from the FFT. The algorithm disassembles an ultra-large-scale FFT process into two-stage processes. Hence, two small-scale FFTs are required, which can achieve flexibility for the system implementation. This implies that if the algorithm is implemented with hardware such as an FPGA, it is not necessary to design an ultra-large point FFT. The algorithm reduces the subsequent processing units by one set for real-valued signals by shifting the centre frequency of every sub-band of the WOLA filter bank by half of the frequency bin, conserves hardware resources, and decreases the implementation complexity according to the identity of each sub-band of the IWOLA filter bank. The IWOLA + FFT algorithm demonstrates a comparable spectral analysis performance to that of the direct FFT algorithm and eliminates the overlap interference between the adjacent channels within the entire Nyquist frequency bandwidth. To improve the channelised performance, the IWOLA + WOLA algorithm was further proposed and analysed, which includes the second-stage WOLA filter bank and accumulation module instead of the second-stage FFT. The IWOLA + WOLA algorithm demonstrates the same function as the IWOLA + FFT algorithm, with a superior spectral analysis performance. By changing the algorithm parameters \(K\), \(D\), \(D_{2}\), and \(K_{1}\), the spectral analysis can be readily performed with an ultra-high-frequency resolution and satisfactory performance. Future research will be focused on design implementation and optimization in a FPGA, and the deployment of the proposed schemes at an observatory.  
Funding acquisition, C.Y. and Q.M.; Methodology, X.W., J.H. and C.W.; Project administration, Q.M.; Writing--original draft, X.W. and T.W. 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, grant number U1731120, 61801153, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China, grant number 17 KJB160001, the CCIT Key Laboratory of Industrial IoT, grant number KYPT201803Z, the Changzhou Key Laboratory of High Technology, grant number CM20183004, the State Key Laboratory of Millimetre Waves, grant number K202012, the high-end research and study funding project for professional leaders of Jiangsu higher vocational colleges, grant number 2018 GRGDYX011, and the Natural Science Foundation of Zhejiang province, grant number LQY20F010001. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
## 2 Thermo-Coupled \(J_{2}\)-Flow Model with Evolving Elastic Potential
### Normality Rule with Thermo-Induced Plastic Flow  
The plastic part \(\mathbf{D}^{p}\) in the decomposition Equation (1) is prescribed by the following normality rule [43; 44; 45]:  
\[\mathbf{D}^{p}=\frac{\tilde{\kappa}}{\tilde{h}}\left(\frac{\partial f}{\partial \tilde{\mathbf{\tau}}}\cdot\overset{\circ}{\tilde{\mathbf{\tau}}}\log+\frac{\partial f }{\partial T}\dot{T}\right)\frac{\partial f}{\partial\tilde{\mathbf{\tau}}}\,, \tag{5}\]  
where the yield function \(f\) is of von Mises type:  
\[f=\frac{1}{2}|\tilde{\mathbf{\tau}}-\mathbf{\alpha}|^{2}-\frac{1}{3}r^{2}\,. \tag{6}\]In the above, the back stress \(\mathbf{\alpha}\) is traceless and introduced to characterize the anisotropic or kinematic hardening induced by plastic flow and the stress limit \(r\) represents the isotropic hardening behavior and, generally, relies on both the effective plastic work \(\tilde{\kappa}\) and the temperature \(T\), viz.,  
\[r=r(\tilde{\kappa},T)\,. \tag{7}\]  
In addition, the plastic indicator \(\xi\), takes values \(1\) and \(0\) for the loading and unloading cases [43; 44; 45], namely,  
\[\xi=\begin{cases}1&\text{for}\quad\left[f=0,\ \frac{1}{h}\left(\frac{\partial f}{ \partial\tilde{\kappa}}:\!\frac{\partial}{\partial\tilde{\kappa}}\!\log+\frac {\partial f}{\partial T}\!\tilde{T}\right)\geq 0\right]\,,\\ 0&\text{for}\quad f<0\quad\text{or}\quad\left[f=0,\ \frac{\partial f}{ \partial\tilde{\kappa}}:\!\frac{\partial}{\partial\tilde{\kappa}}\!\log+\frac {\partial f}{\partial T}\!\tilde{T}\leq 0\right]\,.\end{cases} \tag{8}\]  
The plastic modulus \(\hat{h}\) will be given later on.  
The back stress \(\mathbf{\alpha}\) is governed by the evolution equation below:  
\[\begin{cases}\overset{\alpha}{\mathbf{\alpha}}\log=c\mathbf{D}^{p}\,,\\ c=c(\varphi,\tilde{\kappa},T)\,.\end{cases} \tag{9}\]  
In the above, \(\varphi\) is the magnitude of the deviatoric stress, i.e.,  
\[\varphi=\sqrt{1.5\text{tr}\tilde{\kappa}^{2}}\,. \tag{10}\]  
Throughout, the effective plastic work \(\tilde{\kappa}\) is given by  
\[\hat{\kappa}=(\tilde{\mathbf{\tau}}-\mathbf{\alpha}):\mathbf{D}^{p}\,. \tag{11}\]  
The flow rule Equation (5) with the loading-unloading conditions Equation (8) introduces plastic flow induced by changing temperature and, in particular, this is the case in the absence of stress. This fact will play an essential role in modeling the strain recovery effect at heating, as will be explained in Section 5.
# Change of Lip Curvature Through Extraction and Non-Extraction Orthodontic Treatment
## 4 Discussion  
The influence of the first premolar extraction on lip position has been explored extensively, yielding inconsistent findings. The results of this study were compared with findings from the qualified studies included in the systematic review, which are summarized in Table 5. [PERSON] et al. [17] compared 15 patients undergoing four first premolar extractions (UL-E plane: \(-\)1.61 \(\pm\) 1.62 mm, LL-E plane: \(-\)3.13 \(\pm\) 1.97 mm) to 17 non-extraction cases (UL-E plane: \(-\)0.07 \(\pm\) 0.89 mm, LL-E plane: \(-\)0.15 \(\pm\) 0.70 mm) in Class I and II female patients, finding significant retraction of both upper and lower lips in the extraction group. Similarly, [PERSON] [18] observed marked retraction of both lips in 30 patients who underwent four first premolar extractions (UL-E plane: \(-\)2.75 \(\pm\) 1.5 mm, LL-E plane: \(-\)3.34 \(\pm\) 1.75 mm) compared to 32 non-extraction cases (UL-E plane: \(-\)0.68 \(\pm\) 1.89 mm, LL-E plane: 0.67 \(\pm\) 2.24 mm) in borderline Class I patients. [PERSON] et al. [19] evaluated 13 extraction cases (UL-E plane: \(-\)1.0 \(\pm\) 1.9 mm, LL-E plane: \(-\)2.6 \(\pm\) 1.9 mm) and 12 non-extraction cases (UL-E plane: \(-\)0.9 \(\pm\) 2.4 mm, LL-E plane: \(-\)0.4 \(\pm\) 3.4 mm) in Chinese borderline patients, finding significant retraction in the lower lip but no notable difference in upper lip retraction between groups. Contrastingly, [PERSON] et al. [20] reported no significant differences in lip position between 20 Class I patients undergoing four first premolar extractions (LL-E plane: \(-\)0.2 \(\pm\) 3.7 mm) and non-extraction cases (LL-E plane: \(-\)0.05 \(\pm\) 1.9 mm). These conflicting results underscore the variability in treatment outcomes depending on patient characteristics and study methodologies. A recent systematic review concluded that both the upper and lower lips were retracted relative to the E-plane following the extraction of four first premolars [21]. However, due to reported ethnic differences and other potential confounding factors, such as age, gender, and initial lip protrusion, no meta-analysis was conducted on these outcomes. Recently, overcoming CBCT-based three-dimensional (3D) analysis, which provides less defined images of soft tissue [22], reports have emerged about the use of face scanners to acquire and analyze more detailed three-dimensional digital information of soft tissue. [PERSON] et al. [23] observed that orthodontic treatment involving extractions led to significant retraction of both lips, with the lower lip retracting more substantially (\(-\)1.46 \(\pm\) 0.7 mm) compared to the upper lip (\(-\)0.93 \(\pm\) 0.78 mm). Conversely, non-extraction treatment caused a slight protrusion of the upper lip (0.55 \(\pm\) 1.04 mm) and the lower lip (0.42 \(\pm\) 1.21 mm). These results indicated that extraction tended to retract the lips, whereas non-extraction tended to maintain or increase lip fullness. Another study reported similar trends but