esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The PII\({}^{1/2}\)DD\({}^{1/2}\) Control Scheme
### \(Pl^{1/2}Dd^{1/2}\) Frequency Response
According to [PERSON]\({}^{\prime}\)s stability theorem [48], a fractional transfer function \(G(s)=Z(s)/P(s)\) of a linear time-invariant system with fractional commensurate order \(q\) is stable if and only if the following condition is satisfied in the \(\sigma\)-plane:  
\[|\arg(\sigma)|>q\frac{\pi}{2},\forall\sigma\in C,P(\sigma)=0 \tag{22}\]  
For \(q=1\) (integer-order systems), this theorem defines the well-known requirement of pole location in the complex plane: for stability, no pole must be in the right half plane, and the stability boundary is the imaginary axis. For \(q=1/2\), the region with \(|\arg(\sigma)|>\frac{\pi}{4}\) corresponds to the stable behaviour; moreover, it is possible to demonstrate that [2]:  
* the region with \(\frac{\pi}{4}<|\arg(\sigma)|<\frac{\pi}{4}\) corresponds to stable under-damped behaviour;
* the pair of lines with \(|\arg(\sigma)|=\frac{\pi}{2}\) correspond to stable over-damped behaviour;
* the region with \(\frac{\pi}{2}<|\arg(\sigma)|<\pi\) corresponds to stable hyper-damped behaviour;
* the negative real axis (\(|\arg(\sigma)|=\pi\)) corresponds to stable ultra-damped behaviour. Within the stability region, the time response is oscillatory if there are roots in the under-damped region. In the case of fractional order systems, the amount of damping cannot be quantified by only one dimensionless damping ratio, as for complex poles of integer-order systems; for example, in the implementation of the PII\({}^{1/2}\)DD\({}^{1/2}\) control scheme, damping is associated both to the derivative term (damping of integer order 1) and to the half-derivative term (damping of order 1/2). The two dimensionless damping ratios related to these damping terms and their effects are discussed in [36; 39]. The stability regions of fractional order systems with fractional commensurate order \(q=1/2\) are represented in Figure 3. This is a clear advantage of the PII\({}^{1/2}\)DD\({}^{1/2}\) over the PII\({}^{\lambda}\)D\({}^{\mu}\) scheme, since the PII\({}^{1/2}\)DD\({}^{1/2}\) in combination with IO plants always gives rise to systems with commensurate order \(1/2\), and therefore it is possible to use the map of Figure 3 to evaluate stability and type of behaviour of the closed-loop system. On the contrary, the determination of the stability conditions for non-commensurate order system is a more challenging problem [49].
## 3 Discussion of the Noise Results
### Effect of the Pore Coverage  
Figure 11 shows the \(\Delta\)SPL for structured porous trailing edges in which all three subregions, the two downstream subregions and the last downstream subregion are covered by pores of 0.6 mm from left to right, respectively. No consistent trend in noise reduction can be observed from these subfigures, partly due to the complex nature of airfoil tonal noise, as demonstrated in Section 3.1. Figure 12 plots the \(\Delta\)OASPL for structured porous trailing edges with different pore coverages. It is shown that the performance of OASPL reduction is slightly different for \(\alpha=0^{\circ}\) and \(\alpha=10^{\circ}\). Generally, larger OASPL reduction is observed for the configuration with smaller pore coverage at \(\alpha=0^{\circ}\), while better noise reduction is obtained for the porous edge with larger pore coverage at \(\alpha=10^{\circ}\). One of the mechanisms responsible for the noise reduction caused by porous edges [4,28,29] is that porous treatment allows for flow communication between the pressure and suction sides close to the airfoil trailing edge, and this subsequently influences the flow separation in the boundary layer. At an angle of attack of \(\alpha=10^{\circ}\), all of the three subregions have sufficient pressure differences to drive airflow passing through both sides of the airfoil. Therefore, porous edges with larger pore coverage provide greater noise reduction. On the other hand, due to the symmetry profile of the NACA 0012 airfoil and the resulting flow symmetry at \(\alpha=0^{\circ}\), a sufficient pressure difference will only occur in the area close to the trailing edge, especially at low free-stream velocities. Furthermore, in this situation, additional pores at the middle subregion and the upstream subregion will increase the risk of generating unwanted bluntness-induced noise [15, 16, 17]. The combined effect of these two factors results in more noise reduction for the porous edge with smaller pore coverage.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Configuration** & \(V_{\infty}\) **= 10 m/s** & \(V_{\infty}\) **= 20 m/s** & \(V_{\infty}\) **= 30 m/s** & \
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 3 Method
### Output Calculation
#### 3.5.1 Inter-Beat Interval Pre-Filtering  
Before calculating HR/HRV, the extracted inter-beat intervals (IBI) are filtered to remove noise caused by false positive/negative peak detections. First, all IBIs lying outside the range of 250 ms to 2000 ms are excluded (corresponding to the human heart rate range of 30 to 240 bpm). To further remove strong outliers from the signals, intervals farther than three standard deviations from the mean are removed.
# Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
## 3 H-Transformer
### Experimental Results
The error in Solar dataset predictions are in KWh, and in m/s for Wind datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Model Info**} & \multicolumn{4}{c}{**CV (RMSE)**} \\ \hline
**Approach** & **Model** & _Solar1_ & _Solar2_ & _Wind1_ & _Wind2_ & _Wind3_ \\ \hline \multirow{4}{*}{Single} & SARIMA [23] & 27.36\% & 32.29\% & 27.61\% & 19.01\% & 20.82\% \\  & RNN [33] & 22.65\% & 30.24\% & 24.20\% & 15.46\% & 18.26\% \\  & LSTM [34] & 22.93\% & 28.06\% & 24.34\% & 16.37\% & 18.36\% \\  & GRU [35] & 22.26\% & 29.53\% & 23.18\% & 14.48\% & 17.84\% \\  & Transformer [24] & 24.47\% & 47.03\% & 32.45\% & 15.46\% & 22.29\% \\ \hline \multirow{4}{*}{Hybrid} & SARIMA + RNN & 22.54\% & **24.15\%** & 21.71\% & 14.44\% & 17.63\% \\  & SARIMA + LSTM & 22.65\% & 28.16\% & **20.83\%** & 14.42\% & 17.57\% \\  & SARIMA + GRU & 24.44\% & 25.07\% & 21.99\% & 14.48\% & 17.57\% \\  & H-Transformer & **21.50\%** & 25.21\% & 21.24\% & **14.29\%** & **17.42\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CV(RMSE) performance metric of the H-Transformer, single and hybrid approaches for renewable time series. The best value for each time series is highlighted in bold. Figure 5: Comparison between the actual renewable energy time series (100 last points—blue line) and the forecasts of SARIMA (red line) and the H-Transformer (green line). Solar dataset predictions are measured in KWh, and Wind dataset predictions are measured in m/s. It is possible to note that for all data sets, the H-Transfomer forecast is closer to the actual series than the SARIMA outputs. The difference between SARIMA and the H-Transformer is especially relevant in estimating the time series' peaks and valleys, where reaching an accurate estimate is more challenging and relevant [11,14].
# Feasibility Study on Color Grading of Blue Iolite Based on GemDialogue Color Comparison Charts
## 3 Result and Discussion
### Color Grading of Color Charts
The chroma, \(C^{\star}\), of the second category is the highest in the 6 categories, indicating that its blue is the strongest and purest. Although the lightness is low, it has little influence on the color appearance. The mean values of hue angle, \(h^{\circ}\), of the third and fifth categories are similar, both have a hue angle between 240\({}^{\circ}\)and 300\({}^{\circ}\), belonging to the blue color. While the lightness, \(L^{\star}\), and chroma, \(C^{\star}\), of the two are different. The lightness, \(L^{\star}\), of the third category is higher than that of the fifth category, but the chroma is the lowest among the 6 categories, so the color of the third category belongs to the grayblue with low saturation. Although the saturation of the fifth category is slightly higher than that of the third category, due to the low lightness, it shows dark blue with obvious blackgray tone. The 6 th category has similar lightness and chroma, its color appearance is light blue to blue. For more accurate classification of the blue color charts, K-Means clustering analysis is performed again on categories 1 and 6 that have large color differences. The clustering results are shown in Table 4.  
Category 1 is further divided into three categories, whose color features are purple, light purple, and bluepurple, respectively. Category 6 is divided into two categories, with color characteristics of blue and light blue. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster Center** & **Average Value** & **Simulated Color** & **Amount** \\ \hline \multirow{4}{*}{\(1.1\)} & \(L^{\star}\) = 43.87 & \multirow{4}{*}{65} \\  & \(C^{\star}\) = 46.65 & & & \\  & \(h^{\circ}\) = 309.75 & & & \\ \hline \multirow{4}{*}{\(1.2\)} & \(L^{\star}\) = 72.30 & \multirow{4}{*}{20} \\  & \(C^{\star}\) = 29.98 & & & \\  & \(h^{\circ}\) = 302.50 & & & \\ \hline \multirow{4}{*}{\(1.3\)} & \(L^{\star}\) = 49.01 & \multirow{4}{*}{25} \\  & \(C^{\star}\) = 53.63 & & & \\ \cline{1-1}  & \(h^{\circ}\) = 285.98 & & & \\ \hline \multirow{4}{*}{\(6.1\)} & \(L^{\star}\) = 42.68 & \multirow{4}{*}{265} \\  & \(C^{\star}\) = 45.76 & & & \\ \cline{1-1}  & \(h^{\circ}\) = 269.40 & & & \\ \hline \multirow{4}{*}{\(6.2\)} & \(L^{\star}\) = 51.57 & \multirow{4}{*}{215} \\  & \(C^{\star}\) = 45.17 & & & \\ \cline{1-1}  & \(h^{\circ}\) = 253.26 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: K-Means cluster analysis results of categories 1 and 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean Square** & **df** & **F** & **P** \\ \hline \(L^{\star}\) & 8742.697 & 5 & 117.136 & 0.000 \\ \(C^{\star}\) & 14,597.743 & 5 & 181.593 & 0.000 \\ \(h^{\circ}\) & 22,951.498 & 5 & 231.236 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the one-way analysis. Based on the above analysis, the color characteristics and classification of 9 categories of color charts are summarized in Table 5.
## 1 Introduction
These assumptions model a deployable monitoring network where information is sent periodically towards a central facility. Our aim is to identify the schedule and policy that maximize the amount of information that can be received under a fairness constraint. The rest of the paper is organized as follows. Section 2 introduces the network model including the operation of the nodes and the collision constraints involved. Then, time scheduling and some related points are addressed, especially the optimal scheduling. A new graphic method to find the optimal scheduling is presented. Section 3 analyses the network performance for the two service policies considered: FIFO and Round-Robin. Firstly, numerical results for throughput are presented. Secondly, end-to-end packet delay is measured and improved by establishing specific initial conditions. Thirdly, the mean queue length of the nodes is calculated. Finally, concerning the fairness behavior, a discussion about the delivered packet distribution to the Gateway node is presented.
## 2 Natural Neighbour Radial Point Interpolation Method
### Material Homogenisation Technique
Obtain the RVE's homogenised stress (\(\bar{\epsilon}_{100}\), \(\bar{\epsilon}_{010}\) and \(\bar{\epsilon}_{001}\)) and strain (\(\bar{\epsilon}_{100}\), \(\bar{\epsilon}_{010}\) and \(\bar{\epsilon}_{001}\)) using Equations (39) and (40), respectively. 7. Using Equation (44), establish a system of \(3\times 4\) equations: \[\left\{ \begin{array}{l}\bar{\sigma}_{100}=\mathbf{c}_{[4\times 4]}\cdot \bar{\epsilon}_{100}\Rightarrow\begin{cases}\sigma_{xx}^{[100]}\\ \sigma_{yy}^{[100]}\\ \sigma_{zz}^{[100]}\\ \sigma_{xy}^{[100]}\\ \sigma_{xy}^{[100]}\end{cases}=\begin{bmatrix}C_{11}&C_{12}&C_{13}&0\\ C_{21}&C_{22}&C_{23}&0\\ C_{31}&C_{32}&C_{33}&0\\ 0&0&0&C_{44}\end{bmatrix}.\begin{cases}\varepsilon_{xx}^{[100]}\\ \varepsilon_{yy}^{[100]}\\ \gamma_{xy}^{[100]}\end{cases}\\ \bar{\sigma}_{010}=\mathbf{c}_{[4\times 4]}\cdot\bar{\epsilon}_{010} \Rightarrow\begin{cases}\sigma_{xx}^{[101]}\\ \sigma_{xy}^{[1010]}\\ \sigma_{zz}^{[100]}\\ \tau_{xy}^{[100]}\end{cases}=\begin{bmatrix}C_{11}&C_{12}&C_{13}&0\\ C_{21}&C_{22}&C_{23}&0\\ 0&0&C_{44}\end{bmatrix}.\begin{cases}\varepsilon_{xx}^{[101]}\\ \varepsilon_{yy}^{[101]}\\ 0\\ \tau_{xy}^{[100]}\end{cases}\end{array} \right.\] (51) Three of these twelve equations are linear dependent and lead to the same result: \(\tau=C_{44}\cdot\gamma_{xy}\), allowing us to obtain: \(G_{12}=C_{44}\). The remaining nine components \(C_{ij}\), with \(\{i,j\}=1,2,3\), are obtained solving the other nine equations. 8. After obtaining all \(C_{ij}\), it is possible to use Equation (45) to obtain the elastic mechanical properties: \[\left\{ \begin{array}{l}E1=\frac{\zeta}{C_{22}C_{33}-(C_{23})^{2}}\\ E2=\frac{\zeta}{C_{11}C_{33}-(C_{13})^{2}}\\ E3=\frac{\zeta}{C_{11}C_{22}-(C_{12})^{2}}\\ G_{12}=C_{44}\end{array} \right.\quad,\quad\left\{ \begin{array}{l}\
u_{12}= \frac{C_{12}C_{33}-C_{11}C_{23}}{C_{22}C_{33}-(C_{23})^{2}}\\ \
u_{13}=\frac{C_{13}C_{22}-C_{11}C_{23}}{C_{22}C_{33}-(C_{23})^{2}}\\ \
u_{23}=\frac{C_{23}C_{11}-C_{21}C_{13}}{C_{11}C_{33}-(C_{13})^{2}}\end{array} \right.\] (52) being \[\zeta=C_{11}C_{22}C_{33}+2C_{23}C_{13}C_{12}-C_{11}(C_{23})^{2}-C_{22}(C_{13} )^{2}-C_{33}(C_{12})^{2}\] (53)  
Since the homogenised mechanical properties will be estimated using the 2D plane strain assumptions, it is necessary to pre-estimate the elasticity modulus in the transversal direction (notice that in Equation (51) \(C_{33}\) is always multiplied by zero). Thus, the rule of mixtures (ROM) is used [35],  
\[E_{3}^{ROM}=\sum_{i=1}^{n}v_{f}^{(i)}E_{3}^{(i)} \tag{54}\]  
where \(n\) is the number of fractions of material (in this case, there are only two fractions: material and absence of material, i.e., the void), \(v_{f}^{(i)}\) is the volume fraction of fraction \(i\), and \(E_{3}^{(i)}\) is the [PERSON]'s modulus in material direction 3 of fraction \(i\).
## 3 Results and Discussion
### Impacts of Falling Dust on Soil
The quantity of dust collected (ton ha\({}^{-1}\)) in relation to dust deposition over a range of dates and locations (S1 to S20) is shown in Figure 8. In most places, there is a general rise in dust deposition from 20 June to 20 July. S11 rises from 0.00930 to 0.01776 ton ha\({}^{-1}\), and S1 rises from 0.00890 to 0.01347 ton ha\({}^{-1}\). According to this pattern, there may be higher dust deposition in the mid-summer (late June to July) because of stronger winds, drier weather, or agricultural practices that expose more soil to wind erosion. Numerous places exhibit a decrease in dust deposition after 20 July. For instance, on 20 July, S1 falls to 0.01347 ton ha\({}^{-1}\), and on 30 August, it is reduced to 0.00494 ton ha\({}^{-1}\). Reductions in wind speeds or increases in humidity, which lessen the ability of winds to carry and deposit dust, could be linked to this drop in weather patterns. With the greatest dust deposition values, S11 stands out; on 10 July, it peaked at 0.02056 ton ha\({}^{-1}\), indicating a substantial vulnerability to dust deposition in this location. Similar to S1, S6, and S16, these places likewise show significant levels of dust deposition, indicating that wind-blown dust accumulation is more common in these areas. When compared to the greater deposition zones, locations like S3, S4, and S5 typically exhibit lower dust deposition values. S5, for instance, peaks on 20 July at 0.00795 ton ha\({}^{-1}\), a considerable decrease from the highest values in S11 or S1. These areas might have protective factors, such as vegetation cover, soil type, or topography, that mitigate dust deposition.
## 2 Analysis of the Working Mechanism of the Buried Waterstop  
### The Working State of the Buried Waterstop  
The ring joints of mountain tunnels are generally divided into two categories: deformation joints and construction joints, and the buried waterstop belt is set along the ring in the middle of the lining thickness, as shown in Figure 1. The following two kinds of deformation often occur in the tunnel: (1) longitudinal uneven settlement due to uneven distribution of the stratum and surrounding load changes and (2) longitudinal telescopic settlement due to the temperature difference, concrete shrinkage and creep, and vehicle load.  
The deformation joint is set mainly to improve the uneven force deformation of the tunnel, which is generally divided into settlement joints and telescopic joints, and the width should be 20 to 30 mm. The specification stipulates that the maximum allowable settlement difference of the deformation joint for settlement shall not be greater than 30 mm [25].  
Figure 1: Schematic diagram of the waterproof structure of the mountain tunnel joints. (**a**) Deformation joint, (**b**) Construction joint.  
There is no clear provision on the maximum allowable telescopic value of the deformation joint, which can be calculated by referring to the available literature [26; 27] and taking into consideration unfavorable conditions and a certain safety factor. The maximum elongation amount of the deformation joint after rounding is 20 mm, and the maximum shortening amount is 10 mm.  
In summary, when working normally, the buried waterstop at the deformation joint will be in an uneven settlement or telescopic state with the lining deformation. The maximum settlement value is 30 mm, the maximum elongation value is 20 mm, and the maximum compression value is 10 mm.
## 2 Results
### Cell Viability of 3T3-L1 Cells  
To evaluate the potential cytotoxic effect of the SH and UA extracts on 3T3-L1 cells, the extracts were prepared at concentrations of 62.5, 125, 250, 500, and 1000 \(\upmu\)g/mL and used to treat 3T3-L1 cells for 48 h. Treatments with either extract at a concentration of 1000 \(\upmu\)g/mL resulted in a cell viability of 95%, whereas, at 500 \(\upmu\)g/mL, cell viability was 100% (Figure 1). Thus, we confirmed that neither extract exhibited cytotoxicity at concentrations lower than 500 \(\upmu\)g/mL. For subsequent analyses, the highest concentration used was 500 \(\upmu\)g/mL.  
\begin{table}
\begin{tabular}{c c c} \hline  & _S. horneri_ & _U. australis_ \\ \hline Total phenolic content & 95.5 \(\pm\) 1.4 & 62.4 \(\pm\) 1.8 \\ (mg GAE/g extract) & 19.4 \(\pm\) 1.0 & 20.9 \(\pm\) 1.2 \\ \hline Data are expressed as the mean \(\pm\) standard deviation (n = 3). & \\ \end{tabular}
\end{table}
Table 1: Total phenol and flavonoid contents in _S. horneri_ and _U. australis_ ethanol extracts.  
\begin{table}
\begin{tabular}{c c c c} \hline  & _S. horneri_ & _U. australis_ & **Assorbic Acid** \\ \hline DPPH & 0.64 \(\pm\) 0.29 & 1.58 \(\pm\) 0.17 & 0.03 \(\pm\) 0.00 \\ (IC\({}_{50}\) mg/mL) & & & \\ \end{tabular}
\end{table}
Table 2: DPPH radical scavenging activity of _Sargassum horneri_ and _Ulva australis_ ethanol extracts.
# Short-Term Power Load Forecasting Using an Improved Model Integrating GCN and Transformer
## 4 Analysis of Experimental Results
### Ablation Experiment  
The purpose of the ablation experiment is to verify the validity of the components in the model. In the experiments of this paper, in order to test the effectiveness of MLLA and the Transformer components improved based on MLLA, we designed two variants: (1) removing MLLA (-w/o MLLA) and (2) removing the MLLA-Transformer component (-w/o MLLA-Transformer).  
Figure 18 shows the index results of the ablation experiments conducted on three datasets. It can be found from the figure that when the MLLA or the MLLA-Transformer component is removed, the prediction accuracy of the model for the data decreases, which also verifies the effectiveness of the proposed model.
## 5 Result of Experiment
### Data Model Development
#### 5.2.1 Conceptual Design  
The aim of the conceptual design step is to translate the identified functional requirements into a conceptual schema. The entity-relationship model is used as a conceptual schema because it is a widely exploited model and allows for a detailed definition of the entities and their relationships on the database. The simplified conceptual database schema of the SSI application is shown in Figure 13. The root element of the system is a customer, which can have one netting agreement and many confirmations and agreements. Netting is an option to merge many payments into one. An agreement is a special contract with a customer, usually for a specific product and currency that have a specific settlement instruction. Each short name can have one account and many receive and deliver instructions. A receive instruction is an instruction for incoming payment and a delivery instruction is an instruction for outgoing payment.
# Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning
###### Abstract
Neural networks (NNs) are a type of artificial intelligence algorithm which, through a process of deep learning with extensive amounts of data, can enable a computer to present with the capacity to learn to think on its own and make decisions and solve problems in a similar way to humans [23; 24]. Convolutional neural networks have been used to detect periapical lesions on different radiographic modalities [11; 25] including panoramic radiographs. This could make the interpretation more objective, and help the dentists save time and focus more on the treatment and identifying the problem at an early stage and avoid further complications. However, even though the performance of these algorithms is promising, there is need for improvement in order to meet the requirements for clinical application [5; 26; 27]. Thus, the aim of this study was to develop an AI able to detect PL on panoramic radiographs. This was done using CNN in a two-step approach that involved the use of a detector followed by a classifier. We hypothesized that an AI tool trained on healthy and non-healthy periapical areas in panoramic radiographs could detect the periapical regions of the teeth and classify them into healthy apices and apices with periapical lesions.