esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Bacterial Binding Properties  
Adsorption kinetic data were tested as follows: 5 mg of the MIPs was added to 1 mL _L. monocytogenes_ PBS solution (\(1.0\times 10^{5}\) colony forming unit (CFU) mL\({}^{-1}\)). The supernatant was removed after gently shaking for different periods of time (0.5, 1, 1.5, 2, 2.5, 3, 4, 5, 6, and 7 h) and allowing the sample to settle for 3 min. The target bacteria absorbed on the polymer beads were eluted with 1 mL PBST (PBS containing 5 mL L\({}^{-1}\) Tween 20) solution, and the number of bacteria in elution was analyzed by viable cell counting. The binding amount was calculated as followed:  
\[Q=\frac{C_{\mathrm{s}}}{M}\times V\]  
Here, \(Q\) (CFU mg\({}^{-1}\)) is the adsorption capacity and \(C_{\mathrm{s}}\) is the bacteria concentration in the elution. \(V\) (mL) is the volume of bacterial suspension and \(M\) (mg) is the adsorption weight.  
For the static adsorption testing, 5 mg of MIPs/NIPs were weighed into 1 mL PBS buffer with different concentrations of bacterial suspension (\(7.5\times 10^{1}\), \(1.6\times 10^{2}\), \(7.6\times 10^{2}\), \(1.5\times 10^{3}\), \(7.5\times 10^{3}\), \(1.5\times 10^{4}\), \(3.8\times 10^{4}\), \(7.5\times 10^{4}\), \(1.6\times 10^{5}\), and \(7.5\times 10^{5}\) CFU mL\({}^{-1}\)). After shaking for 2 h, the supernatant was removed and the binding amount of _L. monocytogenes_ was recorded.  
The selectivity study was conducted using two strategies depending on purpose. For visual studies, 5 mg of MIPs were incubated in 1 mL _L. monocytogenes_ and _S. aureus_ for 2 h under the same conditions, respectively. After removing the supernatant and Gram staining the MIPs, _L. monocytogenes_ and _S. aureus_ assembled on the polymer beads were directly observed by optical microscope. For a detailed evaluation, 5 mg of MIPs or NIPs were suspended in a 2 mL mixture containing _L. monocytogenes_, _E. coli, Salmonella_, and _S. aureus_ (each \(4.0\times 10^{4}\) CFU mL\({}^{-1}\)). The amounts of _L. monocytogenes_, _E. coli, Salmonella_, and _S. aureus_ binding on the polymer beads were recorded via the plate-coating method.
# Astronomical Investigation to Verify the Calendar Theory of the Nasca Lines
## 3 Astronomical Investigations in the Nasca Project
### Statistical Studies on the Astronomical Alignment of the Geoglyphs
#### 3.3.3 Investigation of the Correlation of Straight Lines with Stars
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Year** & **Achernar** & **Alkaid** & **Antares** & **Gacrux** & **Murzim** & **Rigel** & **Sirius** \\ \hline \(-200\) & 194.43\({}^{\circ}\) & 334.48\({}^{\circ}\) & 250.40\({}^{\circ}\) & 223.00\({}^{\circ}\) & 249.83\({}^{\circ}\) & 256.13\({}^{\circ}\) & 253.17\({}^{\circ}\) \\ \hline
800 & 201.82\({}^{\circ}\) & 328.22\({}^{\circ}\) & 246.08\({}^{\circ}\) & 217.00\({}^{\circ}\) & 251.14\({}^{\circ}\) & 259.17\({}^{\circ}\) & 253.55\({}^{\circ}\) \\ \hline Change & 7\({}^{\circ}\)24\({}^{\prime}\) & 6\({}^{\circ}\)16\({}^{\prime}\) & 4\({}^{\circ}\)19\({}^{\prime}\) & 6\({}^{\circ}\)00\({}^{\prime}\) & 1\({}^{\circ}\)19\({}^{\prime}\) & 3\({}^{\circ}\)02\({}^{\prime}\) & 0\({}^{\circ}\)23\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Azimuth changes for selected bright stars near the horizon at Nasca. Figure 15: Total number of hits (at intervals of 100 years) for the correlation of straight lines with randomly selected bright stars during the Nasca period.
## 4 Results  
### Performance of the Models  
After deploying all models for every category, evaluation metrics were extracted to assess the models' performance. The results help us to choose the best models for categories. This section gives the error rates for each model for forecasting new cancer cases and incidence rates for 2022 to 2026 for different categories. The models were trained on data from 1992 to 2021.  
It is important to note that negative R\({}^{2}\) values, as observed for the Prophet model in our analysis, indicate that the model does not improve predictive accuracy compared with a baseline mean-only (intercept-only) model. Such results do not inherently suggest that the Prophet model is incorrectly specified or invalid. Rather, these negative values highlight that, within the specific context of our forecasting problem, the Prophet model's predictive strength was comparatively weaker than the other evaluated models (ARIMA, LSTM, and Hybrid ARIMA-LSTM). Therefore, R\({}^{2}\) values, particularly negative ones, should be interpreted cautiously as indicators of relative model performance rather than absolute measures of model validity.
## 3 Results and Analysis
### Assessment of Soil Fertility Status  
#### 3.4.1 Selection of Soil Indicators for the Minimum Data Set  
Pearson correlation and principal component analysis (PCA) can be used to avoid data redundancy as much as possible and retain its soil interpretation information. Through principal component analysis, the eigenvalue, variance and cumulative variance contribution rate of each principal component and the common factor variance of each index are obtained (Table 5). According to the principle of eigenvalue \(\geq 1\), the cumulative variance contribution rate of the five principal components was 78.07%.  
From Table 5, it can be seen that the index with the largest load value in the first principal component is total nitrogen content, and the index in the range of 10% is organic matter content and alkali-hydrolyzed nitrogen. Both of these two indexes have a high correlation with wheat biomass (R = 0.57, 0.57, \(p<0.01\)), so total nitrogen, organic matter and alkali-hydrolyzed nitrogen are selected into MDS. In the second principal component, only available potassium content was a high factor load index, so available potassium was selected for MDS. In addition, available phosphorus was also significantly positively correlated with wheat biomass (R = 0.56, \(p<0.05\)). Considering that soil-available phosphorus is closely related to crop growth and is an inherent property of soil, reflecting the nutrient supply capacity of soil to plants, soil-available phosphorus was selected into MDS. In summary, five indicators of total nitrogen, organic matter, alkali-hydrolyzable nitrogen, available phosphorus and available potassium were finally selected into MDS for SQI calculation. In addition, in order to better reflect the impact of various soil nutrient indicators on wheat biomass, we used Pearson correlation analysis to further explain it, as shown in Figure 7.  
Figure 6: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline
**Ingredient** & **pH** & **SOM** & **TN** & **AN** & **AP** & **SK** & **AK** \\ \hline PC1 & \(-0.679\) & 0.905 & 0.934 & 0.919 & 0.523 & \(-0.499\) & 0.188 \\ PC2 & 0.535 & 0.026 & \(-0.068\) & \(-0.214\) & 0.118 & 0.477 & 0.860 \\ \hline \end{tabular}
\end{table}
Table 5: Correlation coefficient matrix of chemical properties of soils.
## 3 Results and Discussion
### Surface Area and Porosity Analysis  
The surface area of the prepared samples is measured using the BET equation, and is an important parameter contributing the photocatalytic performance. When the surface area is higher the photocatalyst will provide more surface for reactions and thus better performance. The surface area and porosity of as-synthesized samples are summarized in Table 1. The small addition of urea resulted in a minor reduction of the surface area with minimal effect on the pore volume. However, upon increasing the amount of urea, a continual decrease in surface area and pore volume is observed. Thus, it can be seen that the surface area of the NT-3 samples is reduced to approximately 45% of pure TiO\({}_{2}\). Such a  
Figure 6: (**a**) UV Vis DRS spectra, and (**b**) Band gap estimation of, Pure TiO\({}_{2}\), NT-1, NT-2 and NT-3.  
decrease in the surface area can be attributed to the agglomeration of TiO\({}_{2}\) nanoparticles and increased size as displayed by the SEM images. Thus, nitrogen doping creates defects and narrows the band gap, and with the anatase phase enrichment it makes NT samples more efficient despite their reduced surface area.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 2 Related Work
### Loss Function
#### 2.1.2 Center Loss  
Ref. [30] points out that for the closed-set classification problem, the network parameters learned from the CE loss function performs well but lacks a good generalization for the open-set classification problem. To overcome the shortcomings of CE loss, [PERSON] et al. proposed a center loss [30], which aims to construct a center embedding vector for each class, and this center embedding is continuously updated with training. The distance between each speaker embedding and the corresponding center embedding is calculated and accumulated to obtain the center loss. After that, the center loss is multiplied by the corresponding weights and summed with the CE loss to obtain the final loss, as follows:\[\mathcal{L}_{\mathrm{S}}=-\sum_{i=1}^{n}\log\frac{e^{W_{y_{i}}^{T}x^{i}+y_{y_{i}}}}{ \sum_{j=1}^{C}e^{W_{x_{i}}^{T}x_{i}+b_{j}}} \tag{2}\]  
\[\mathcal{L}_{\mathrm{C}}=\frac{1}{2}\sum_{i=1}^{n}\
orm{\mathbf{x}_{i}-\mathbf{c}_{y_{i }}}_{2}^{2} \tag{3}\]  
\[\mathcal{L}=\mathcal{L}_{\mathrm{S}}+\lambda\mathcal{L}_{\mathrm{C}} \tag{4}\]  
The \(\mathbf{c}_{y_{i}}\) denotes the center embedding of the \(y_{i}\) class, and \(\lambda\) is the weight of center loss.  
Inspired by center loss, [PERSON] et al. [8] then introduced the idea of center loss into the field of speaker recognition. Their experiments demonstrate that center loss can achieve good results in the field of speaker recognition.
# Two-Stage Deep Learning Method for Breast Cancer Detection Using High-Resolution Mammogram Images
## 2 Datasets
### INbreast Dataset  
The dataset contains a total of 410 mammograms belonging to 115 patients. Each image belongs to one of \(\mathrm{R_{MLO}}\), \(\mathrm{R_{CC}}\), \(\mathrm{L_{MLO}}\), and \(\mathrm{L_{CC}}\) views and contains a labeled BI-RADS score. Mammograms containing breast masses (i.e., excluding BI-RADS 1) have annotated RoIs. The size of the mammogram is 4084 \(\times\) 3328 or 3328 \(\times\) 2560 pixels, depending on the compression plate used in the acquisition [14].
## 3 Experimental Results and Analysis
### Droplet Size Distribution Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**S/cm**} & \multicolumn{1}{c}{**D/mm**} & \multicolumn{1}{c}{**P/MPa**} & \multicolumn{1}{c}{**D\({}_{50}\)**} & \multicolumn{1}{c}{**D\({}_{[3,2]}\)**} & \multicolumn{1}{c}{**D\({}_{[4,3]}\)**} & \multicolumn{1}{c}{**S/cm**} & \multicolumn{1}{c}{**D/mm**} & \multicolumn{1}{c}{**P/MPa**} & \multicolumn{1}{c}{**D\({}_{50}\)**} & \multicolumn{1}{c}{**D\({}_{[3,2]}\)**} & \multicolumn{1}{c}{**D\({}_{[4,3]}\)**} \\ \hline \multirow{8}{*}{30} & 0.5 & 1 & 681.3 & 516.1 & 677.2 & & & & & & & \\  & 0.5 & 2 & 681.5 & 567.9 & 681.0 & & & & & & & \\ \cline{2-10}  & 0.5 & 3 & 674.6 & 208.1 & 580.3 & & & & & & & \\  & 0.7 & 1 & 668.4 & 212.4 & 534.2 & & & & & & & \\  & 0.7 & 2 & 668.8 & 201.3 & 536.2 & & & & & & & \\  & 0.7 & 3 & 658.2 & 160.5 & 482.8 & & & 0.5 & 2 & 105.9 & 86.2 & 238.1 \\  & 1.1 & 1 & 672.5 & 274.6 & 571.0 & & & 0.5 & 3 & 93.7 & 70.8 & 331.7 \\  & 1.1 & 2 & 668.1 & 238.6 & 533.3 & & & 0.7 & 1 & 62.0 & 47.0 & 160.6 \\  & 1.1 & 3 & 656.1 & 199.8 & 474.5 & 70 & 0.7 & 2 & 61.5 & 49.1 & 144.2 \\ \hline \multirow{8}{*}{50} & 0.5 & 1 & 669.5 & 321.5 & 553.5 & & & 0.7 & 3 & 71.8 & 60.2 & 230.4 \\  & 0.5 & 2 & 544.8 & 191.8 & 468.0 & & 1.1 & 1 & 68.2 & 51.9 & 77.3 \\  & 0.5 & 3 & 296.0 & 111.3 & 349.8 & & 1.1 & 2 & 64.7 & 50.2 & 72.9 \\ \cline{1-1}  & 0.7 & 1 & 139.1 & 99.0 & 333.8 & & 1.1 & 3 & 71.9 & 53.5 & 101.4 \\ \cline{1-1}  & 0.7 & 2 & 80.3 & 58.3 & 152.2 & & & & & & \\  & 0.7 & 3 & 71.4 & 57.5 & 192.1 & & & & & & \\  & 1.1 & 1 & 95.0 & 64.4 & 153.0 & & & & & & \\  & 1.1 & 2 & 81.2 & 54.5 & 90.4 & & & & & & \\  & 1.1 & 3 & 84.9 & 64.7 & 165.4 & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Droplet size parameters.
## 3 Results  
### Accommodative Response and Pupil Size  
The accommodative responses and pupil sizes of the subjects when gazing at a real object at a viewing distance of 0.4 m are shown in Table 1. The mean value of the accommodative response of the male subjects was 1.55 \(\pm\) 0.25 D, while that of the female subjects was 1.82 \(\pm\) 0.21 D.  
The accommodation stimulus changed with the different viewing distances and resulted in an accommodative response. The viewing distance was converted into the accommodation stimulus, and the accommodation stimulus formula is expressed as follows:  
\[\text{Ac (D)}=1/\text{d (m)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Average (Standard Deviation)**} \\ \cline{2-4}  & **Male (N = 16)** & **Female (N = 16)** & \(p\) \\ \hline Age (y/o) & 20.82 \(\pm\) 0.88 & 20.81 \(\pm\) 1.08 & 0.99 \\ Equivalent sphere (D) & 1.90 \(\pm\) 1.43 & 2.17 \(\pm\) 1.46 & 0.97 \\ Accommodative response (D) & 1.55 \(\pm\) 0.25 & 1.82 \(\pm\) 0.21 & 0.08 \\ Pupil size (mm) & 3.82 \(\pm\) 0.50 & 3.67 \(\pm\) 0.55 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The baseline of the subjects.  
where d is the viewing distance, which is measured in meters, and the unit of the accommodation stimulus is a diopter (D). The accommodative stimulus is 2.5 D (1/0.4 = 2.5 D) when the viewing distance is 0.4 m, and the measured data for all of the subjects were less than 2.5 D, dropping an average of 0.81 D. When the accommodative response is smaller than the accommodative stimulus, it is called the 'lag of accommodation', the expected value of which also varies with age. For 20-year-old adults, the expected value of the lag of accommodation is 0.75 \(\pm\) 0.64 D, and the lag of accommodation observed in this study was within the expected values [22].  
The pupil size was measured for 30 s, and then the mean value was calculated. The average pupil size of the male subjects was 3.82 \(\pm\) 0.50 mm, while that of the female subjects was 3.67 \(\pm\) 0.55 mm. The results showed that there is no significant gender difference (\(p\) = 0.51).
# Accurate Landmark Localization for Medical Images Using Perturbations
## 2 Related Works
### Landmark Localization  
Landmark localization was first developed to analyze face images, and was then expanded to medical images, such as hand X-ray and cephalometric images. Ref. [1] provides an excellent review of face landmark localization. Our review is focused on medical images.  
As for classical approaches, for the medical landmark detection the random forests wise methods [15, 26] have been proposed. They employed a regression-voting strategy and showed outstanding performance. The region proposal approaches [12, 15, 27, 28, 29] have applied a cascade process that consists of region proposal detection and landmark point localization. Although these approaches have shown success for the medical landmark localization problem, the computational complexity is increased, and an architecture modification is sometimes required. Moreover, its hyper-parameters of complex architecture make the problem heuristic. In recent years, deep learning-based end-to-end learning strategy [11], which employs the whole image as input, has been outperforming region proposal approaches. This approach incorporates the region proposal and landmark point detection without feature engineering by human knowledge, and this has led to outstanding performance. When considering output designs, the approaches can be divided into coordinates regression [29, 30, 31] and heatmap regression [10, 11, 32, 33, 34, 35]. For coordinate regression, the method returns x and y coordinates, which means one pixel of each landmark using CNN with fully connected layers. In contrast, heatmap regression approaches estimate the heatmap of each landmark, which is located on the centroid of the landmark. In such a way, Gaussian distribution is typically used to draw a heatmap shape, and truth landmark points are considered as the centroid of the Gaussian. Recently, heatmap regression-wise approaches have outperformed the coordinate regression methods, and our method is also based on heatmap regression.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 2 Theory
### Robustness Optimization Method
Then the vector of control signals is derived as  
\[\mathbf{y}_{\infty}=-\left(\mathbf{S}_{\mathrm{v}}^{\mathrm{H}}\mathbf{S} \right)^{-1}\mathbf{\hat{S}}_{\mathrm{v}}^{\mathrm{H}}\hat{\mathbf{G}}_{ \mathrm{opt}}\mathbf{d}_{\mathrm{p}}(n). \tag{24}\]  
By subtracting Equation (24) from both sides of Equation (23), the update equation can be written as  
\[\mathbf{y}(n+1)-\mathbf{y}_{\infty}=\left(\mathbf{I}-\alpha\mathbf{S}_{ \mathrm{v}}^{\mathrm{H}}\mathbf{S}\right)(\mathbf{y}(n)-\mathbf{y}_{\infty}). \tag{25}\]  
Then, the condition that ensure the stability of the adaptive algorithm can be obtained as [9]  
\[\mathrm{Re}\Big{(}\mathrm{eig}\Big{(}\mathbf{\hat{S}}_{\mathrm{v}}^{\mathrm{H} }\mathbf{S}\Big{)}\Big{)}>0. \tag{26}\]  
It is worth mentioning that the real parts of the eigenvalues of \(\mathbf{\hat{S}}_{\mathrm{v}}^{\mathrm{H}}\mathbf{S}\) are always positive when \(\mathbf{S}_{\mathrm{p}}=\mathbf{\hat{S}}_{\mathrm{p}}\), at which point the stability constraint is automatically satisfied. As the head is generally in the nominal position for a relatively long time, a lower limit threshold for noise reduction can be set. Therefore, the optimization problem with constraints to be solved can be expressed as  
\[\min_{\mathbf{\hat{S}}_{\mathrm{v}}\sigma\mathbf{G}}J=\left\|\beta_{\mathrm{r }}(\theta)\right\|_{\infty}, \tag{27}\]  
\[\mathbf{S}(\theta)=\mathbf{\hat{G}}_{\text{opt}}\big{(}\mathbf{S}_{\text{p}}( \theta)-\mathbf{\hat{S}}_{\text{p}}\big{)}+\mathbf{\hat{S}}_{\text{v}}, \tag{19}\]0  
\[\mathbf{S}(\theta)=\mathbf{\hat{G}}_{\text{opt}}\big{(}\mathbf{S}_{\text{p}}( \theta)-\mathbf{\hat{S}}_{\text{p}}\big{)}+\mathbf{\hat{S}}_{\text{v}}, \tag{19}\]1  
where \(\theta=0\) denotes the head in the nominal position. Equation (27) indicates that the robustness optimization method achieves the optimization objective of minimizing the maximum value of the residual noise factor for different rotation angles by optimizing the vector of virtual plant models \(\mathbf{\hat{S}}_{\mathrm{v}}\) or \(\mathbf{\hat{G}}\), while Equations (28) and (29) are constraints on the system stability and on the noise reduction of the head at the nominal position, respectively. Although there are many ways to solve this multi-objective optimization problem with constrains, such as the sequential quadratic programming (SQP) method [21], the genetic algorithm (GA) [22] and the particle swarm optimization (PSO) [23], the genetic algorithm [22] was used for this paper due to its generality, robustness, and because it is not biased towards the locally optimal solution.
# Failure Propagation Controlling for Frangible Composite Canister Design
## 4 Results  
The five simulation models consist of three shape configurations (planar, pyramidal, dome) respecting the overall dimensions and the weight limitations. The best solution for the static requirement is the pavilion dome, as reported in the Figure 16, because in addition to supporting the static \"wave load\", it allows for the management of the breakage by pressure load, but this aspect must be studied further.  
Table 4 summarizes all of the results collected:  
Figure 16: Pyramidal and Pavilion Dome Tailgate configuration: Stress evaluation.  
Figure 15: Pyramidal and Pavilion Dome Tailgate Configuration: Stress Distribution.  
Planar configuration is lighter, but it has a faster and a more sudden breakage, and this can lead to the uncorking of the tailgate or damaging the missile tip. The petals fail due to the in-plane bending stresses and shear stresses. The stresses are very high in both symmetric and asymmetric impact, but introducing carvings can mitigate the peak of stress by having a progressive failure.  
The introduction of an impact angle due to multifaced pyramidal and dome structures helps to increase the duration of impact and to give rotational speed around the edges to the petals, as shown in Figure 17. The structures exhibit a smoother behaviour, but for the pyramidal one the contact force is more concentrated, causing indentations, while the pavilion dome exhibits a softer and a more gradual contact.  
Thanks to the curvature design optimization, the pavilion dome structure shows low stress levels and longer times for breakage, as shown in Figure 18. The tailgate subjected to impact has a more gradual breakage and, as seen in the following figure, the Von Mises stress in an edge element after an initial growth goes down until the whole structure is involved and the element fails for tensile load.  
The pavilion dome structure was more suitable to reach the goal because it resists better under pressure load and allows the designer to manage the carving curvature to optimize the structure fragmentation, as shown in Figure 19.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Time [s]** & **Breakage Time [s]** & **Von Mises Stress [MPa]** & **Weight [kg]** \\ \hline Planar 4 Petals Centred & 0.035 & \(7\times 10^{-4}\) & 131 & 2.02 \\ \hline Planar 4 Petals Asymmetric & 0.035 & \(6\times 10^{-4}\) & 139 & 2.10 \\ \hline Planar 5 Petals Asymmetric & 0.035 & \(6\times 10^{-4}\) & 70 & 2.08 \\ \hline Planar 4 Petals Asymmetric & 0.035 & \(6\times 10^{-4}\) & 139 & 2.10 \\ \hline Pyramidal Multifaced & 0.032 & \(1.4\times 10^{-3}\) & 79 & 2.80 \\ \hline Pavilion Dome & 0.033 & \(1\times 10^{-3}\) & 39 & 3.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical results.  
Figure 17: Planar VS Pyramidal Multifaced Tailgate: Stress evaluation.