esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 SFSE Double-Differenced GPS/Galileo Mathematical Model  
This section mainly discusses the SFSE double-differenced mathematical models for integrated GPS/Galileo relative positioning, including the non-differenced and double-differenced code and phase observation equations for single system and DD equations for integrated systems with different combination strategies with/without DISBs. Finally, the baseline length constraint model and the corresponding stochastic model are deduced.
## 2 Materials and Methods
### Boundary and Initial Conditions  
At the beginning of every charging and discharging process, the storage system has a uniform temperature. The storage system is charged from the top and discharged from the bottom. After charging and discharging, the phase change process has already been finished in the reference simulation. The initial and boundary conditions of the reference simulation can be seen in Table 3.  
According to the publication of [PERSON] et al. [31], it is assumed in the reference case that the outer wall is adiabatic. Throughout the variations, \(t_{char}\) as well as \(t_{dis}\) are kept constant, and \(T_{start}\), \(T_{in}\) and \(u_{in}\) are varied by a fixed value. Additionally, \(T_{in}\) is integrated once as a time curve, for \(u_{in}\) a parabolic velocity profile is implemented once at the inlet and the heat losses are considered once. The time curves for \(T_{in}\) are fitted to the measured inlet temperatures leading to  
\[T_{in}(t)/^{\circ}\mathrm{C}\ =\ 326.3\times e^{\mathrm{\
u}\times 1.909\times 10^{ -7}\times 1/\mathrm{s}}-1.162\ \times e^{-\mathrm{\
u}\times 1.419\times 10^{-3} \times 1/\mathrm{s}} \tag{2}\]  
for charging and  
\[T_{in}(t)/^{\circ}\mathrm{C}\ =\ 291.05+2.314\times\left(1-e^{-t/443.7\times 1/ \mathrm{s}}\right)-1.805\ \times\left(1-e^{-t/1326\times 1/\mathrm{s}}\right) \tag{3}\]  
for discharging. This results in maximum inlet temperature variations of about 1.7 K and 1.1 K for charging and discharging, respectively. To estimate the influence of the heat losses, a heat loss coefficient is defined, which includes the thermal resistance of the Plexiglas wall and the natural convection at the outside.  
\[U_{loss}=\left(R_{Plexi}A+\frac{1}{\alpha_{ext}}\right)^{-1} \tag{4}\]  
The natural convection at the outer surface \(A\) is calculated by means of laminar natural convection for a vertical cylinder [33]. Assuming \(T_{W,i}=(T_{s}+T_{l})/2=35.25\)\({}^{\circ}\mathrm{C}\) on average and \(T_{amb}=18.5\)\({}^{\circ}\mathrm{C}\) one gets for the overall heat flux coefficient \(U_{loss}=3.87\ \frac{\mathrm{m}\times 1}{\mathrm{m}\times 1}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Physical Properties** & **Value** & **Unit** & **Source** \\ \hline \(\rho\) & 760 & \(\frac{\mathrm{kg}}{\mathrm{kg}}\) & [31] \\ \(c_{s}\) & 5.0 & \(\frac{\mathrm{kg}}{\mathrm{kg}\mathrm{K}}\) & [32] \\ \(c_{l}\) & 2.1 & \(\frac{\mathrm{kg}}{\mathrm{kg}\mathrm{K}}\) & [32] \\ \(\lambda\) & 0.2 & \(\frac{\mathrm{W}}{\mathrm{kg}}\) & [31] \\ \(L\) & 220 & \(\frac{\
# Efficient Estimation of Generative Models Using Tukey Depth
## 5 Synthetic Experiments
### Data Generating Model
#### 6.1.2 Statistical Queuing Model  
We assume that the arrival times are independent outcomes from a Beta distribution  
\[a_{d1},\ldots,a_{dN_{d}}\overset{\text{i.i.d.}}{\sim}\text{Beta}(\phi,\beta),\quad d=1,\ldots,D. \tag{13}\]  
Different choices of the shape parameters \(\alpha\) and \(\beta\) yield different circumstances. For instance, if both of the shape parameters are high, e.g., \(\phi=\beta=20\), almost all arrivals (CPU requests) will take place within a short time period in the middle of the day (e.g., office hours). On the other hand, if \(\phi=\beta=1\), the arrivals will be uniformly distributed throughout the day. Figure 5 shows the beta distribution under these two parametric choices. For more details about the Beta distribution, see e.g., [14].  
Figure 5: Beta distributions with different shape parameters.  
Further, we assume that the sizes (CPU processing time) for each request are independent outcomes from an exponential distribution with rate \(\lambda\)  
\[s_{d1},\ldots,s_{dN_{d}}\overset{\text{i.i.d.}}{\sim}\text{Exp}(\lambda),\quad d =1,\ldots,D. \tag{14}\]  
The expected CPU processing time is therefore \(1/\lambda\).  
Consequently, the current CPU consumption at some time \(\tau\in[0,1]\) on day \(d\) is given by the requested tasks that are not yet finished.  
\[y_{d}(\tau)=\sum_{n=1}^{N_{d}}I(a_{dn}<\tau<h_{dn}). \tag{15}\]  
It follows that the average CPU consumption for a given time interval is  
\[\overline{y}_{dt}=\frac{1}{\overline{\tau_{t}-\tau_{t-1}}}\int_{\tau_{t-1}}^{ \tau_{t}}y_{d}(\tau)\,d\tau. \tag{16}\]  
Figure 6 shows an example of a generated \(\overline{y}_{dt}\) for one day using \(\phi=6\), \(\beta=4\), \(\lambda=10\), \(T=144\) and \(N_{d}=20\).  
The resulting distributions for arrival and processing times are shown in Figure 7. We observe that the CPU usage is zero during the first part of the day since the probability of receiving requests is very small, as shown in the left panel of Figure 7. As soon as the requests are received, the CPU load increases. When the rate of request decreases, the CPU usage also gradually decreases.
# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
## 3 Numerical Experiments
### Grinding Tool-Like Forging  
To test the reliability of the proposed approach, a literature case relevant to grinding tool-like forging [26] was considered, as shown in Figure 14. In the considered geometry, two symmetry planes were identified, as shown in Figure 14.  
Figure 14: The grinding tool-like forging shape [26].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Height [mm]** & **CNN#1** & **CNN#2** & **CNN#3** & **CNN#4** & **CNN#5** & **CNN#6** \\ \hline H\({}_{0^{\circ},\text{ max}}\) & 39.44 & 43.05 & 42.41 & 41.64 & 42.83 & 38.11 \\ H\({}_{0^{\circ},\text{ min}}\) & 30.43 & 30.11 & 31.36 & 28.39 & 32.55 & 31.79 \\ H\({}_{90^{\circ},\text{ max}}\) & 54.64 & 51.19 & 43.54 & 52.67 & 43.54 The one-quarter models of the derived preform shapes are shown in Figure 15 and Table 4. For the estimation of the forging load reduction, 3D plane-symmetric FEM simulations were implemented in DEFORM 3D, as summarized in Appendix B, considering the plasticine material reported in [27].  
As shown in Figure 16, all six preform shapes allowed a maximum forging load reduction in comparison with the cylindrical preform, and the maximum reduction was identified for the preform shape derived by the CNN#5 sub-model. In Figure 16c, the comparison between the experimental results [26] and the authors' FEM model results for the cylindrical billet show a particularly good agreement, proving the reliability of the implemented FEM model. However, the preform design derived by the proposed methodology reduced the forging load and eliminated forging defects.
_Appl. Sci._**2015**, \(5\), 532-543; doi:10.3390/app5030532  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Microwave-Assisted Conversion of Levulinic Acid to \(\gamma\)-Valerolactone Using Low-Loaded Supported Iron Oxide Nanoparticles on Porous Silicates  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Departamento de Quimica Organica, Universidad de Cordoba, Campus de Rabanales, Edificio Marie Curie (C-3), Ctra Nnal IV-A, Km 396, E14014, Cordoba, Spain; E-Mails: z22 [EMAIL_ADDRESS] (A.Y.); qo2 [EMAIL_ADDRESS] (S.D.); qo1 [EMAIL_ADDRESS] (M.S.C.); qo1 [EMAIL_ADDRESS] (A.A.R.)  
* Author to whom correspondence should be addressed; E-Mail: q62 [EMAIL_ADDRESS]; Tel.: +34-95721050; Fax: +34-957212066.  
Academic Editor: [PERSON]  
_Received: 6 July 2015 / Accepted: 31 August 2015 / Published: 9 September 2015_  
**Abstract:** The microwave-assisted conversion of levulinic acid (LA) has been studied using low-loaded supported Fe-based catalysts on porous silicates. A very simple, productive, and highly reproducible continuous flow method has been used for the homogeneous deposition of metal oxide nanoparticles on the silicate supports. Formic acid was used as a hydrogen donating agent for the hydrogenation of LA to effectively replace high pressure H\({}_{2}\) mostly reported for LA conversion. Moderate LA conversion was achieved in the case of non-noble metal-based iron oxide catalysts, with a significant potential for further improvements to compete with noble metal-based catalysts.  
**Keywords:** flow nanocatalysis; conversion of levulinic acid; hydrogenation; GVL; supported nanoparticles
# Lightweight Violence Detection Model Based on 2D CNN with Bi-Directional Motion Attention
## 4 Experiments
### Comparison with the State-of-the-Art Methods  
We compare our model with the state-of-the-art models in terms of accuracy, computational cost, and parameters on the Movie Fight dataset, the Hockey Fight dataset, the Surveillance Camera dataset, and the RWF-2000 dataset. For a comprehensive comparison, models based on 2D CNNs [1, 2, 11, 15, 16, 36], (2 + 1)D CNN [33], and 3D CNNs [4, 6, 7, 9, 17, 34] are taken into consideration. Experiment results of all models are given in Table 8.  
As observed from Table 8, each of the models achieves a comparatively lower accuracy on the RWF-2000 dataset than on the Hockey Fight dataset, the Movie Fight dataset, and the Surveillance Camera dataset. The reason lies in the fact that the RWF-2000 dataset covers more complicated scenarios than the other three datasets and, thus, is more challenging for violence detection.  
As can be seen from Table 8, our model achieves very competitive accuracies on the four datasets. On the Movie Fight dataset, our model achieved the best accuracy of 100%. On the Hockey Fight dataset, our model achieves an accuracy of 98.5%, which is 0.5% lower than the best and ranked third among all models considered. On the Surveillance Camera dataset, our model achieves an accuracy of 91.67%, which is 0.83% lower than the best and ranked third among all models as well. However, on the RWF-2000 dataset, one can find that our model achieves the best accuracy of 90.25% among all models considered. Compared with the second-placed model, our model shows an advantage of 1% in accuracy. It reveals that our model is more robust than its counterparts in scenarios with complicated backgrounds.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**CNN Type**} & \multicolumn{5}{c}{**Datasets**} & \multirow{2}{*}{**GFLOPs**} & \multirow{2}{*}{**Parameters**} \\ \cline{3-3} \cline{5-8}  & & **Movie Fight** & & & & & \\ \hline ConvLSTM [1] & 2D & 100\% & 97.1\% & – & 77.00\% & 15.69 & 9.76 M \\ ConvBiLSTM [2] & 2D & 99.66\% & 98.78\% & – & 79.1\% & 24.52 & 11.20 M \\ Efficient Spatiotemporal [15] & 2D & 100\% & 99.0\% & 92.0\% & 89.25\% & 4.26 & 5.29 M \\ FightCNN-based Attention [16] & 2D & 99.5\% & 96.1\% & 7  
Table 8 also shows that our model is lightweight in both computational cost and parameters. Among all the models considered, our model requires the lowest computational power of 1.21 GFLOPs, which is only 28% of that of the second-placed model and far less than those of others. Meanwhile, our model has only 4.48 M parameters, and ranked third among all models considered. These merits can be attractive, especially for real-time violence detection tasks on resource-limited platforms.
## Abstract  
This study investigated the efficiency of chitosan/polyethylene oxide (PEO)-based nanofibers with incorporated bioactive glass particles as a coating for titanium alloy, in order to improve the bacteriostatic behavior and, concurrently, promote the production of mineralized tissue. Nanofibers with and without bioglass powder were fabricated by electrospinning technique and characterized using several microscopic and spectroscopic techniques in order to study their morphological and physiochemical properties. Subsequently, the substrates were tested in vitro against _Staphylococcus epidermidis_ and SaOS-2 human osteosarcoma cell line. After in vitro testing, viability and CFU counting assays combined with fluorescence microscopy showed a clear decrease in bacterial growth on all substrates with increasing time. However, this trend was stronger for substrates coated with nanofibers. Formation of mineralized matrix upon exposure to osteoblasts was confirmed by means of SEM/EDX and the content/distribution of osteoclastin and osteepontin estimated by fluorescence microscopy. Incorporation of bioglass promoted biomineralization and stimulated osteoblasts to produce a higher amount of bone extracellular matrix. The present results suggest that a chitosan/PEO/bioactive glass nanofiber composite applied as coating on titanium alloys could concurrently improve antibacterial and osteeconductive properties and could be a potential candidate for dental and orthopedic applications.  
Nanofiber; composite; antibacterial; osteeconductive; PEO; chitosan; bioactive glass
## 3 Results
### Clinical and Radiographic Outcomes Following NSRME and SARME
#### 3.3.4 Probing Depths (PDs)  
Two studies using SARME [7; 36] and one study using NSRME [27] reported PDs post-operatively. [PERSON] and [PERSON] reported no PDs greater than 3 mm after NSRME in a young population [27], whereas studies using SARME measured the incidence of PDs greater than 3 mm before and after treatment and an increase ranging between 4.3% and 12.7% [7; 36]. The heterogeneity in the methodology and reported results did not allow a direct comparison of the effects of SARME and NSRME upon PDs.
# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) and Multiple-Input Multiple-Output (MIMO) Radar Antennas Technique
#### 2.2.5 Reconstruction Image  
In the millimeter wave radar imaging process, the radar transmits an FMCW signal and irradiates the target through a synthetic aperture. The received signal at different space points is interferometric demodulated and recorded, and then the IF signal of target to host after scanning is uploaded. Since the purpose of this paper is to generate SAR images, we chose the analytical Fourier transform, which is an existing image reconstruction algorithm [18], according to the dispersion relation of the plane wave in free space, the wave number \(k\) is divided into three components in a Cartesian coordinate system:  
\[k_{x}^{2}+k_{y}^{2}+k_{z}^{2}=\left(2k\right)^{2} \tag{4}\]  
The values of the Fourier transform variables \(k_{x}\) and \(k_{y}\) are \(-2k\) to \(2k\), which satisfy the visible region:  
\[k_{x}^{2}+k_{y}^{2}\leq\left(2k\right)^{2} \tag{5}\]  
Figure 5: Radar three-dimensional data block and the schematic diagram of extracting two-dimensional data block under a fixed \(Y\)-direction displacement.  
Two-dimensional plane reflectance of target at a distance of \(z_{0}\) from the radar can be expressed as:  
\[p(x,y,z_{0})=FT_{2D}^{-1}[\sum_{n=0}^{n_{sample}-1}\Bigl{[}FT_{2D}[u(x,y,n)]e^{- \beta_{z}z_{0}}\Bigr{]}] \tag{6}\]  
where \(u(x,y,n)\) is a three-dimensional data block. \(FT_{2D}\) and \(FT_{2D}^{-1}\) in Formula (6) denote 2D Fourier and inverse Fourier transform operations, respectively.  
The following is the image reconstruction of the actual object, as shown in Figure 6. In this paper, the millimeter wave radar is used to detect hidden objects, so the target is placed in a cardboard box with a distance of 90 mm from the radar. The simultaneous activation of the MIMO-SAR radar and the mechanical slide ensured the resolution of the image.  
The target used in the test is the scissors, which are opened and placed in the paper box. After the image reconstruction algorithm, the details of the scissors can be clearly seen with high object identification. The result of image reconstruction is shown in Figure 7.  
Figure 6: Millimeter wave radar detects hidden targets.  
Figure 7: **(a)** The actual picture of scissors covered by paper box; **(b)** SAR image of scissors covered by paper box 90 mm from radar.  
The scissors placed in the paper box can be detected by the millimeter wave radar, and the SAR image is clearly visible, which verifies the effectiveness and reliability of the analytic Fourier imaging algorithm.
# Assessment of a Patient Dose Monitoring System for Average
## 2 Materials and Methods
Both the \(g\) and \(c\) factors depend on HVL and CBT. [PERSON]'s method estimates the AGD using the following equation:  
\[AGD=K\cdot D_{gN} \tag{2}\]  
where \(D_{gN}\) (mGy/mGy) is the normalized glandular dose coefficient. It depends on the quality of the radiation beam (kVp, HVL, anode-filter combination) and on breast characteristics (composition and thickness). The \(D_{gN}\) values used in this work were determined by means of Monte Carlo simulations for breast glandular density of 0%, 12.5%, 25%, 37.5%, 50%, and 100% and compressed breast thickness of 2-9 cm as a function of kVp and HVL [27]. For all the images taken on the two mammography units considered in this study, the air kerma was calculated using the exposure values provided by the mammography systems and sampled by DoseWatch and the tube output values measured during periodic quality controls based on tube voltage and anode-filter combination, with the detector located on the surface of the breast support plate at 6 cm from the chest side [28]. Kerma calculated values were corrected according to the inverse-square law for each individual breast thickness using CBT values from DoseWatch. The AGD was calculated according to [PERSON]'s Equation (1) using the following: (i) the tube output in \(\mu\)Gy/mAs (from kV, filter material and output measured during quality controls), (ii) the air kerma in mGy (from tube output, mAs and thickness), (iii) the g factor value (from a third-order polynomial regression of HVL and thickness according to [PERSON]'s Table 2 of [2]), (iv) the c factor value (from a third-order polynomial regression of HVL, thickness and glandularity according to [PERSON]'s Tables 5 and 6 of [2]), and (v) s factor (single value for the W/Rh and W/Ag spectra used [2,3]). Equations (3) and (4) represent the regression models applied to the g and c factors, respectively:  
\[ \begin{array}{l}g=\alpha_{0}+\alpha_{1}\cdot X_{1}+\alpha_{2}\cdot X_{2}+ \alpha_{3}\cdot X_{1}^{2}+\alpha_{4}\cdot X_{1}\cdot X_{2}+\alpha_{5}\cdot X_{2 }^{2}+\alpha_{6}\cdot X_{1}^{3}+\alpha_{7}\cdot X_{1}^{2}\cdot X_{2}\\ +\alpha_{8}\cdot X_{1}\cdot X_{2}^{3}+\alpha_{9}\cdot X_{2}^{3}\end{array}  \tag{3}\]  
where \(X_{1}\) = HVL (mm Al) and \(X_{2}\) = thickness (cm)  
\[ \begin{array}{l}c=\beta_{0}+\beta_{1}\cdot X_{1}^{3}+\beta_{2}\cdot X_{2}^{3 }+\beta_{3}\cdot X_{3}^{3}+\beta_{4}\cdot X_{1}^{2}\cdot X_{2}+\beta_{5}\cdot X _{1}^{2}\cdot X_{3}+\beta_{6}\cdot X_{2}^{2}\cdot X_{1}+\beta_{7}\\ X_{2}^{2}\cdot X_{3}+\beta_{8}\cdot X_{3}^{2}\cdot X_{1}+\beta_{9}\cdot X_{3}^{ 2}\cdot X_{2}+\beta_{10}\cdot X_{1}\cdot X_{2}+\beta_{11}\cdot X_{1}\cdot X_{3 }+\beta_{12}\cdot X_{2}\cdot X_{3}\end{array}  \tag{4}\]  
where \(X_{1}\) = HVL (mm Al), \(X_{2}\) = thickness (mm), and \(X_{3}\) = glandularity (%). Similarly, the AGD was calculated according to Boone's Equation (2) using the air kerma calculated value and the polynomial regression model (5) for the DgN coefficients for the W/Rh and W/Ag spectra used:  
\[
## 3 Materials and Methods
### Framework of This Work  
The framework of this study can be summarized as follows:  
1. Data Preprocessing: - Normalization is applied using Min-Max scaling to enhance model stability and convergence. - Outlier detection is performed using Z-scores. - Anomaly detection uses the k-nearest neighbours (k-NN) method to exclude anomalous data points.
2. Correlation Analysis and Trend Exploration: - Correlation between PM concentrations and other studied variables is analyzed. - Trends in PM levels are visualized to gain insights into temporal patterns.
3. Data Splitting: - The dataset is divided into training (70%) and testing sets to evaluate model performance effectively.
4. Model Selection: - The performance of 23 ML and DL models is assessed to identify the best-performing model for hourly PM prediction.
5. Feature Selection: - The effectiveness of 50 feature selection techniques is evaluated to improve the prediction accuracy. - The most relevant predictor variables and optimal models are selected based on this assessment.
6. Multi-Step-Ahead Forecasting with Deep-NARMAX: - A deep-NARMAX model is used to forecast future hourly PM concentrations. - The best mathematical formulation is derived and fine-tuned to forecast hourly PM values based on historical PM data and other relevant variables.  
Overall, this methodology integrates data pre-processing, correlation analysis, model selection, feature selection, model training, and final deployment for accurate PM concentration forecasting.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 2 Materials and Methods
### Self-Organizing Control Using Artificial Potential Functions
#### 2.2.3 Self-Organizing Control Rules for Coplanar Satellites  
For three adjacent coplanar satellites, \(i-1\), \(i\) and \(i+1\), the self-organizing control rules are defined as follows.  
\[\phi_{i}=\left\{\begin{array}{cc}k_{i\Omega}(\sin i_{i}\sin\Omega_{i}-\sin i _{t}\sin\Omega_{t})^{2}+k_{i\Omega}(\sin i_{i}\cos\Omega_{i}-\sin i_{t}\cos \Omega_{t})^{2},\\ \mathrm{if}\quad\delta\Omega>\delta\Omega_{\mathrm{max}}\cup\delta\Omega<\delta \Omega_{\mathrm{min}}\\ k_{bound}\left(\widetilde{d}_{i,i-1}-d_{\mathrm{mint}}\right)^{2},&\mathrm{if} \quad\widetilde{d}_{i,i-1}<d_{\mathrm{mint}}\cap\widetilde{d}_{i,i+1}  
To put it simply, the RAAN control will be performed once the RAAN difference between satellite \(i\) and the designed value does not fit the \(\delta\Omega\) range. The relative motion-bound control will work only if one of two adjacent coplanar satellites of satellite \(i\) (denoted as \(i-1\) and \(i+1\)) does not meet the boundary constraint while the other does. For other cases, the semi-major axis control will be implemented, even if \(\widehat{d}_{i,i-1}\), \(\widetilde{d}_{i,i-1}\), \(\widehat{d}_{i,i+1}\), and \(\widetilde{d}_{i,i+1}\) do not all meet the boundary constraint.