esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Modes of Laser-Induced Mass Transfer in Micro-Gaps
## 3 Details of the Numerical Simulation
### System of Equations  
The governing equations of our model were the Navier-Stokes equations for Newtonian fluids \(\rho\partial\mathbf{u}/\partial t+\rho(\mathbf{u}\cdot\
abla)\mathbf{u}=\
abla\cdot\left[- pI+\mu\left[\
abla\mathbf{u}+\left[\
abla\mathbf{u}\right]^{\mathrm{T}}\right]\right]\) with the incompressibility condition \(\rho\
abla\cdot(\mathbf{u})=0\), where \(\mathbf{u}(r,z,t)\) is the velocity field (initially 0 everywhere), \(p(r,z,t)\) the pressure, \(\rho=1.636\mathrm{kg/m^{3}}\) the density, and \(\mu=18\cdot 10^{-6}\) Pa-s the viscosity of air, and the advection-diffusion equation \(\partial c/\partial t+\
abla\cdot(-D\
abla c)+\mathbf{u}\cdot\
abla c=0\) for the volume concentration \(c(r,z,t)\) (initially 0), with the diffusion coefficient  
Figure 3: Material flow over and into micro-structures. (**a**) Schematic sketch of the experiment; (**b**) Structure of the applied fluorescent compound [9]; (**c**) Actual fluorescent image of the deposition pattern, in green color scale. The pattern on top of the structured substrate is similar to the result on a flat surface, but the bottom of the micro-wells was reached by the material flow and stained only in the center region of the spots. The diameter of the cavities was 23 \(\upmu\)m, their smallest distance 9.5 \(\upmu\)m, and their depth 8.7 \(\upmu\)m. The scale bar represents 250 \(\upmu\)m.  
\(D=10^{-7}\) m\({}^{2}\)/s that was chosen to be as large as is reasonable for molecules in a gas. From these material parameters, the characteristic pattern size of 100 \(\upmu\)m and the inlet velocity taken as 1.5 m/s resulted in a Reynolds number \(Re\approx 14\), whereas the critical Reynolds number for 2D flows between parallel plates was larger than 2285 [10]. If the inlet diameter \(2R=2\cdot 37.5\)\(\upmu\)m was used as length scale for the calculation, 10 was obtained as the value of \(Re\); there was one steady-state study of radial entrance flow between plates [11] that showed laminarity for values much higher than that (the series ended at \(Re=200\)). These considerations justify why we have not introduced a turbulence model term. The surface concentration \(c_{\mathrm{s}}(r)\) (initially 0) was integrated by the equation \(\partial c_{\mathrm{s}}/\partial t=|kc|\) defined on the boundary, where \(k=20,000\) m/s was a very high mass transfer coefficient ensuring complete deposition of the compounds. Applying the absolute value favored numerical stability.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Experimental Results and Discussion
#### 4.4.3 Results on the DIOR Dataset
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Method** & **mAP** & **AP** & **SH** & **ST** & **BD** & **TC** & **BC** & **GTF** & **HA** & **BR** & **VE** \\ \hline YOLOv3 [12] & 87.27 & 99.55 & 81.82 & 80.30 & 98.26 & 80.56 & 81.82 & 99.47 & 74.31 & 89.61 & 86.98 \\ Faster R-CNN [10] & 88.30 & 99.70 & 85.58 & **99.27** & 95.93 & 88.22 & 92.08 & 99.73 & 92.11 & 43.37 & 86.60 \\ FMSSD [54] & 90.40 & 99.70 & 89.90 & 90.30 & 98.20 & 86.00 & 96.80 & 99.60 & 75.60 & 80.10 & 88.20 \\ CAD-Net [91] & 91.50 & 97.00 & 77.90 & 95.60 & 93.60 & 87.60 & 87.10 & 99.60 & **100** & 86.20 & 89.90 \\ EGAT [55] & 92.01 & 97.32 & 96.72 & 97.16 & 96.51 & 86.64 & 94.46 & 94.18 & 86.18 & 80.14 & 90.79 \\ Cascade R-CNN [49] & 92.19 & 99.54 & 88.53 & 95.98 & 94.46 & 94.02 & 88.21 & 97.16 & 91.45 & 82.30 & 90.25 \\ YOLOv7 [14] & 92.94 & 99.52 & 92.60 & 96.61 & 97.78 & 93.85 & 90.78 & 95.60 & 89.54 & 83.72 & 89.38 \\ YOLOv5 [33] & 93.30 & 99.50 & 94.80 & 84.75 & 97.94 & 97.36 & 94.79 & 99.00 & 89.22 & 86.76 & 88.84 \\ CANet [56] & 93.33 & 99.99 & 85.99 & **99.27** & 97.28 & 97.80 & 84.77 & 98.38 & 90.38 & 89.16 & 90.25 \\ ABNet [16] & 94.21 & **100** & 92.58 & 97.77 & 97.76 & **99.26** & 95.98 & 99.86 & 94.26 & 69.04 & **95.62** \\ TBNet [53] & 95.44 & **100** & 95.09 & 97.35 & 97.35 & 97.55 & **98.96** & **100** & 94.64 & 80.77 & 92.71 \\ ProSAF-Net (Ours) & **96.56** & 99.50 & **98.01** & 98.07 & **98.73** & 89.56 & 95.18 & 98.60 & 94.05 & **91.69** & 93.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with the current state-of-the-art methods on the NWPU VHR-10 dataset. The top-performing results are shown in bold, and the runner-up results are underlined. Figure 8: Representative examples of detection outcomes from ProSAF-Net on the DIOR dataset. The scenes in these images are more diverse and varied, with many small objects present. Moreover, it covers a wide range of categories in the dataset, which effectively demonstrates the generalization capability of our method. Overall, our method demonstrates high detection accuracy across all objects.
# A Systematic Mapping Study on Research in Anemia Assessment with Non-Invasive Devices
## 4 Results
### Stakeholders  
Table 3 reports identified stakeholders' definitions, while Figure 5 shows the number of papers that a certain kind of stakeholder would be interested in.  
Most of the papers aim to improve screening efficiency in health facilities (89 papers). A considerable number of the chosen papers propose new technologies that could interest health device manufacturers (59), and a similar number treats cost efficiency aspects (53). In smaller quantities papers focus on aspects that can be significant for caregivers (36) and citizens self-care (32).  
Figure 4: Top productive countries.  
Figure 5: Distribution of stakeholders indicated in the studies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stakeholder** & **Description** \\ \hline Health facilities & Include papers that could improve the quality of private and public healthcare services. \\ \hline Device manufacturers & Includes papers that could improve existing commercial devices or propose innovative solutions that could be implemented. (on sale/commercial) marketed \\ \hline National Health System and Health insurance & Papers talk about devices that could save money or time augmenting the system’s efficiency. \\ \hline Doctors and caregivers & Include papers that can make diagnoses faster and easier. \\ \hline Citizens & Covers tools and techniques usable by non-technical people \\ \hline \hline \end{tabular}
\end{table}
Table 3: Identified stakeholders’ definitions.  
Figure 6 gives the distribution of papers by device for the assessment (see Table 4); some papers belong to many classes since often authors compare more than one of them.  
Figure 6 shows that the majority refers to co-oximeters (124 papers), perhaps because they are devices easily accessible due to low cost and ease of use. Camera-based solutions are quite popular (25 papers), followed by papers discussing mini-invasive devices (18 papers). Only a few (12 papers) focus on smartphone-centric measurements.
# Quantum Field Theory Representation in Quantum Computation
## Appendix A  
In QFT, the n-points correlation function for general state \(|\Omega\rangle\) can always be represented using the general state two points correlation functions \(\langle\ \Omega|T\left\{\vec{\psi}_{(\vec{r}_{1},t_{1})}\vec{\Psi}_{(\vec{r}_{2},t_{2}) }\right\}|\Omega\rangle\) with \(\vec{\overline{\psi}}_{(\vec{r}_{2},t_{2})}=\psi^{\dagger}_{(\vec{r}_{2},t_{2} )}\gamma^{0}\) and where \(\dagger\) stands for conjugate transpose, and \(\gamma^{0}\) is the \(4\times 4\) time-like Dirac matrix. A general state two-points correlation function can be expressed by perturbation series of the vacuum state \(|0\rangle\) two-points correlation function combined with the interaction Lagrangian density \(\mathcal{L}_{int}\left(\stackrel{{\rightarrow}}{{r}},t\right)\). If \(T\) is the time-ordering of the quantum fields \(\psi\left(\stackrel{{\rightarrow}}{{r}},t\right)\), then there is an equivalence [4] between the path integral (left side of Equation (A1)) and the canonical quantization (right side of Equation (A1)) approaches for calculating \(\langle\Omega|T\left\{\vec{\psi}_{(\vec{r}_{1},t_{1})}\vec{\Psi}_{(\vec{r}_{2 },t_{2})}\right\}|\Omega\rangle\) where \(\varepsilon\) is an infinitesimal number:  
\[\lim_{\tau\rightarrow\infty(1-i\varepsilon)}\frac{\int D\psi\ \psi_{(\vec{r}_{1},t_{1})}\vec{\overline{\psi}}_{(\vec{r}_{2},t_{2})}^{ \varepsilon}e^{i\int_{-\tau}^{\tau}dt\ \int_{-\tau\varepsilon}^{\tau }d\vec{r}^{\tau}\ \frac{\mathcal{L}_{int}(\vec{r}_{2},t_{2})}{n}}}{\int D\psi\ e^{i\int_{-\tau}^{ \tau}dt\int_{-\tau\varepsilon}^{\tau\ }d\vec{r}^{\tau}\ \frac{\mathcal{L}_{int}(\vec{r}_{2},t_{2})}{n}}}=\lim_{ \tau\rightarrow\infty(1-i\varepsilon)}\frac{\langle\ 0|T\left\{\vec{\psi}_{(\vec{r}_{1},t_{1})}\vec{ \overline{\psi}}_{(\vec{r}_{2},t_{2})}^{\varepsilon}e^{i\int_{-\tau}^{\tau}dt \int_{-\tau\varepsilon}^{\tau\ }d\vec{r}^{\tau}\ \frac{\mathcal{L}_{int}(\vec{r}_{2},t_{2})}{n}} \right\}|0\rangle}{\langle\ 0|T\left\{e^{i\int_{-\tau}^{\tau}dt\int_{-\tau \varepsilon}^{\tau\ }d\vec{r}^{\tau}\ \frac{\mathcal{L}_{int}(\vec{r}_{2},t_{2})}{n}} \right\}|0\rangle}\] (A1)  
The numerator of Equation (A1) is a product of the contributions by Feynman diagrams connecting the two spacetime locations \(\left(\stackrel{{\rightarrow}}{{r}}_{1},t_{1}\right)\) and \(\left(\stackrel{{\rightarrow}}{{r}}_{2},t_{2}\right)\) with the non-connecting diagrams, whereas the denominator is a contribution of only the non-connected diagrams; the division thus eliminates the contribution of the non-connected diagrams (which are proportional to the vacuum energy density) to Equation (A1) [4].
## 2 Materials and Methods
### Performance Loss Rate Evaluation Using Mixed-Effects Linear Models
\]  
Equation (2) can be written as a linear mixed model:  
\[y_{ij}=\gamma_{10}+\gamma_{11}x_{i1}+\ldots+\gamma_{1k}x_{ik}+\gamma_{10}t_{ ij}+\gamma_{11}x_{i1}t_{ij}+\ldots+\gamma_{1k}x_{ik}t_{ij}+\left(b_{0,i}^{*}+b_{1,i}^{*}t_{ij}+\varepsilon_{ij}\right) \tag{3}\]  
The above Equation (3) can be written into matrix form:  
\[\mathbf{Y}_{i}=\mathbf{X}_{i}\boldsymbol{\beta}+\mathbf{Z}_{i}\mathbf{b}_{i}^ {*}+\boldsymbol{\varepsilon}_{i} \tag{4}\]  
where  
\[\begin{array}{l}\mathbf{Y}_{i}=\left(y_{i\;1},\ldots,y_{i\;m_{i}}\right)^{t} ;\boldsymbol{\varepsilon}_{i}=\left(\varepsilon_{i\;1},\ldots,\varepsilon_{i\; m_{i}}\right)^{t};\mathbf{b}_{i}^{*}=\left(b_{0,i^{\prime}}^{*},b_{1,i}^{*} \right)^{t};\\ \left(b_{0,i^{\prime}}^{*},b_{1,i}^{*}\right)^{t}\;\sim\;\text{BVN}(\mathbf{0},\mathbf{V})\\ \varepsilon_{i}\sim\text{MVN}(\mathbf{0},\sigma^{2}\mathbf{I}_{i});\\  
challenging conditions in which the studied PV production units operate, in terms of exposure to high levels of irradiation and under quite harsh climatic environments, the obtained \(\Delta\)_PR_% per year is slightly lower than 0.5%/year, which is the median value reported by [PERSON] and [PERSON] after analyzing 2000 degradation rates, available in the literature, which were measured on individual modules or entire systems [13]. When considering the PR of the PV production units, this time by taking into account the module manufacturer (_Manufacturer_, in this case two manufacturers: \"SW\" and \"KC\"), the technology (_Technology_, that is, whether the modules were laminated with monocrystalline \"m\" or polycrystalline \"p\" cells), and the nominal power (_NominalP_, ranging from 155 to 175 W) as covariates, that is, the typology model, the results indicate that although the effect of the manufacturer and technology were not significant, when the nominal power of a type of PV module used is increased by one unit, the variation of the PR of the corresponding PV production unit per elapsed month decreases by 0.000897% (see Table 6). Although this may look like a rather small number, according to this result and by considering it on an annual basis, its effect adds up so that, throughout the considered period, a PV production unit that has 175 W modules was, on average, 0.2153%/year below one who was made with 155 W ones (see Figure 6). In economic terms and by considering the tariff granted by the regulation in force at the time of completion of the installation [35], this means that by the year 2019, a PV production unit that has 175 W modules earned, on average, EUR 11.02/kW less than one that has 155 W.  
Now, taking into account how large the SOLTEN I facility is, where it was placed, and the way that the PV production units were distributed in it, the location model was
###### Abstract  
Location awareness is crucial for numerous emerging wireless indoor applications. Deep learning algorithms have demonstrated the potential for achieving the required level of positioning accuracy in indoor environments. However, obtaining abundant labels for data-driven machine learning is costly in practical situations. As an effective solution to alleviating the insufficiency of labeled data for deep learning-based indoor positioning, deep semi-supervised learning (DSSL) can be employed to lessen the dependency on labeled data by exploiting potential patterns in unlabeled samples. In this paper, we propose an Adapted Mean Teacher (AMT) model within the DSSL paradigm for indoor fingerprint positioning by using a channel impulse response. To enhance the generalization of the trained model, we design an efficient implicit augmentation scheme for the training process in the AMT model. Furthermore, we develop a tailored residual network to efficiently extract location characteristics in the AMT framework. We conduct extensive simulation experiments for indoor scenarios with heavy non-line-of-sight conditions based on open datasets to demonstrate the effectiveness of our proposed AMT model. Numerical results indicate that the AMT model outperforms several consistency regularization methods and the pseudo-label method in terms of positioning accuracy and lower positioning latency, achieving a mean error of 90 cm when using a small number of labels.  
indoor positioning; channel impulse response; heavy non-line-of-sight; deep learning; semi-supervised learning +
Footnote †: journal: Electronics Electronics
# Effect of Shot Peening on the Evolution of Scale on T91 Steel Exposed to Steam
## 4 Discussion  
### Evolution of Oxidation Scale  
Figure 6 shows a plot of the oxidation scale thickness in Table 3 as a function of oxidation duration. The abrupt decrease in the scale thickness of the untreated specimens at 800 and 1000 h are believed to be due to the exfoliation of the outer layer, as shown in Figure 5(a3,a4). In agreement with previous observations [14; 15; 16; 17], parabolic oxidations kinetics were observed for both untreated and shot peened specimens. As can be seen in Figure 6, the experimental data points can be fit by a parabolic curve, represented by the following equation:  
\[\mathrm{d}^{2}=\mathrm{k}_{\mathrm{p}}\times\mathrm{t}, \tag{1}\]  
where d is the total scale thickness, \(\mathrm{kp}\) is the parabolic rate constant, and \(\mathrm{t}\) is the oxidation time. The values of \(\mathrm{kp}\) of untreated and shot peened specimens derived from best fits to the curve are 3 \(\times\) 10\({}^{-7}\) and 1.5 \(\times\) 10\({}^{-7}\) mm\({}^{2}\)/min, respectively. It can be said that the growth rate of oxidation scale of shot peened specimens is much slower than that of untreated specimens at 650 \({}^{\circ}\)C steam exposure.  
Figure 5: Cross-sectional SEM images of oxide scale after steam exposure of for various durations: (**a1–a4**) for untreated and (**b1–b4**) for shot peened specimens.
## 6 Discussion
### System Performance and User Experience  
The system's performance in processing both spatial and temporal data in real-time represents an advancement in recommendation technology. The integration of multiple development languages and databases has created a robust platform capable of handling complex geographical calculations while maintaining system responsiveness. Users now have a better knowledge of product placements and engagement patterns due to the web application's ability to display temporal data with geographic mapping. The efficiency of the scoring system in integrating customer evaluations and geographic distance (\(\delta\) = 0.7 and \(\sigma\) = 0.3) shows that the framework can provide accurate, location-aware suggestions while preserving computational efficiency. The importance of computational efficiency in GIS-based systems has previously been discussed by the authors in the Related Work section, who stress that location-aware applications require real-time processing. This is further supported by our results, which demonstrate the scalability of our methodology in large-scale retail environments.
# A Review on the Thermal Hazards of the Lithium-Ion Battery and the Corresponding Countermeasures
## 4 Thermal Hazards of Lithium-Ion Battery
### Thermal Hazards of Typical Commercial Lithium-Ion Batteries
#### 4.4.4 Lithium nickel cobalt aluminate oxide  
Similar to NMC material, NCA ternary material was also developed on the basis of LCO positive electrode materials by adjusting the content of Ni, Co and Al elements. Within, Ni and Co elements are applied to improve the capacity and the electronic conductance, respectively. The main purpose of Al addition is to stabilize the lattice structure and avoid structural collapse during charging/discharging. So far, NCA materials are generally regarded as one of the most appealing materials due to their comparable operating voltage, energy density and ideal properties for practical applications [185]. Especially for the LiNi\({}_{0.80}\)Co\({}_{0.15}\)Al\({}_{0.05}\)O\({}_{2}\) cathode, which has achieved ubiquitous commercial success. The remaining problem for NCA material is the rapid capacity fading during cycling, which is due to the mixing of Li/Ni cations [186]. Because the radii of Ni\({}^{2+}\) and Li\({}^{+}\) are similar, it is easy for Ni\({}^{2+}\) to enter the lithium layer, therefore results in capacity loss and thermal hazard. At present, several studies have been reported on the strategies to improve the stability of NCA materials [187; 188; 189]. These studies are mainly based on various cationic doping in Ni-rich cathode materials to cease the migration of Ni\({}^{2+}\) towards the Li\({}^{+}\) site. Moreover, doping in the Li\({}^{+}\) intercalation site is also a very important strategy to reduce structural disorientation. Among, magnesium (Mg) and sodium (Na) are the most well-studied dopants that can be embedded into the Li\({}^{+}\) site.
# PPDC: A Privacy-Preserving Distinct Counting Scheme for Mobile Sensing
## 4 Performance Evaluation  
In this section, we conduct experiments to evaluate the accuracy of PPDC as well as its efficiency compared with the situation lacking of privacy protection. In our experiments, the schemes are implemented with Python. The sensing data of users are randomly formed by the Python programs in the uniform random distribution, where we use difference values of integers to represent users' data.
## 3 Simulation Results and Discussion
### Mutual-Coupling Reduction  
Mutual coupling is the amount of energy realized by a nearby antenna when another antenna broadcasts. Mutual coupling alters the reflection coefficient, radiation pattern and input return loss of RDRA MIMO antennas. [25; 26] are the basic mutual coupling, _MC ii_ empirical models.  
\[MC\;ij\;=\;\exp{(-\frac{2d_{ij}}{\lambda}(\alpha+j\pi)).i}\;\
eq j \tag{5}\]  
\[MC\;ij\;=\;1\;-\;\frac{1}{N}\sum_{I}\sum_{i\
eq j}d\;ij \tag{6}\]  
Somewhere _dij_ is the space among the ith and jth antenna elements, is the mutual coupling intensity regulating parameter, and N is the quantity of array components. Mutual coupling in practice is determined by the array configuration and the energizing of other components. It is often expressed in dB-values, S-parameters among the ith and jth antenna elements, and 20 log10(Sij) separation. However, the precise mechanism of reciprocal coupling is mostly determined by the transmitting and receiving modes [27; 28].  
Despite the fact that Design-2 generates Circular polarization and broadens the impedance matching bandwidth, the major parameter, Mutual coupling (S12/S21) between the radiating parts, is reduced by a little, i.e., \(-19\) dB, whereas Design-1 was \(-15\) dB. In Figure 10, the modeled S-parameters of Design-1 and Design-2 are indicated for comparison. The minimum isolation level of Design-1 is -16 dB at 3.69 GHz in the S11 band (3.62 GHz-3.84 GHz), while isolation improves to \(-19.9\) dB at the same frequency in Design-2. Mutual coupling remains significant at lower frequencies (3.5 GHz-4. GHz), but steadily reduces as frequency increases, with the lowest coupling obtained at 4.3 GHz for both Design-1 and Design-2.  
Figure 9: Distribution of magnetic field (**a**) TE\({}_{x}\)[813] at 3.72 GHz and (**b**) TE\({}_{y}\)[183] at 4.35 GHz.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 4 Validation of the Experimental Results With ECMWF and Sensitivity Analysis
### Sensitivity Analysis of Significant Wave Height Estimation
#### 4.2.1 Sensitivity Analysis of Significant Wave Height to Azimuth Cutoff  
According to Equation (19), the significant wave height is proportional to the azimuth cutoff \(\lambda_{c}\). When the azimuth cutoff error increases, the estimation error of significant wave height will also increase. In order to analyze the sensitivity of the significant wave height to the azimuth cutoff \(\lambda_{c}\), it is necessary to assume that the wavelength \(\lambda_{p}\) of ocean waves and propagation direction \(\phi\) of ocean waves remain unchangeable. Due to \(\lambda_{p}=240\) m, the sensitivity is analyzed within \(\lambda_{c}\in[10\,\mathrm{m},200\,\mathrm{m}]\), and the sensitivity curve of significant wave height to azimuth cutoff is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parametric Name** & **Parametric Symbol** & **Parametric Value** \\ \hline Azimuth cutoff (m) & \(\lambda_{c}\) & 90 \\ Wavelength of ocean waves (m) & \(\lambda_{p}\) & 240 \\ Propagation direction of ocean waves (\({}^{\circ}\)) & \(\phi\) & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters using in sensitivity analysis.  
Figure 11: Scatter plots of the significant wave height derived by the proposed method and the ECMWF data.  
As shown in 12, no matter how the azimuth cutoff changes, the sensitivity does not change. In other words, the sensitivity of the significant wave height to the azimuth cutoff is independent of the azimuth cutoff itself.