esa-sceva/satcom-chunk-collection huggingface dataset:
# A DFTB-Based Molecular Dynamics Investigation of an Explicitly Solvated Anatase Nanoparticle
## 3 Results
### Formation of Water Layers  
Another property of interest in this system is the self-assembly of water layers at the TiO\({}_{2}\) surface. Water molecules are known to form layers at planar TiO\({}_{2}\) surfaces [18], and it would be expected that these layers also form around the nanoparticle. Figure 4 shows the density of water oxygen and hydrogen atoms as a function of distance to the nanoparticle surface. Because the nanoparticle occupies a non-negligible volume, the radial distance was taken as the shortest distance between a given water oxygen molecule and a constituent  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\Delta\)**E (eV)** & **Description** & **Figure** \\ \hline \(-\)36.31 & Fully dissociated, one H atom adsorbed on nanoparticle O atom. & Figure 3a \\
24.47 & Fully dissociated, not adsorbed. & Figure 3b \\ \(-\)13.38 & Dissociated, hydroxyl species adsorbed. & Figure 3c \\ \(-\)0.96 & Intact, physically adsorbed. & Figure 3d \\ \hline \hline \end{tabular}
\end{table}
Table 2: Indicative water–nanoparticle interaction energies.  
Figure 3: Selected water–nanoparticle interactions. (**a–d**) depict four distinct interactions as described in Table 2.  
Figure 2: Energies of interaction for all water molecules in the nanoparticle system. The x-axis corresponds to the molecular ID of the water oxygen atoms.  
atom in the nanoparticle. To account for the large size and inhomogeneous shape of the nanoparticle within the simulation cell, a temporary 3 \(\times\) 3 \(\times\) 3 supercell was constructed by including duplicate systems with their coordinates translated by integer permutations of the cell vectors; this ensured that the density distribution remained smooth at distances up to 10 A. The density was calculated by constructing shells of 0.05 A width around the nanoparticle and dividing the number of atoms within the shell by the shell volume.  
Figure 4 shows clear density peaks for both hydrogen and oxygen atoms. The hydrogen atom density peaks at approximately 3 A, and its local minimum is at 4.0 A. The oxygen atom density peaks at approximately 2.4 A, with a number of shoulder peaks between 2.4 A and the local minimum at 4.2 A. The Ti-O bond length in anatase is approximately 2.0 A, which corresponds to a small shoulder peak in the oxygen atom density in Figure 4. Similarly, the average O-H bond length is approximately 0.96 A, corresponding to a small peak in the hydrogen atom density. This clearly indicates chemical bonding occurring between the nanoparticle surface atoms and water molecule constituents. Other interesting insights include the narrow oxygen density peak, with a maximum at r = 4.7 A, possibly corresponding to a second layer at the surface, as observed in other studies. However, the peak density in this region is quite low for a full layer compared to other studies of anatase systems, and the layer may only be partially present [3, 19, 20].
# Spectral Line Reflectance and Fluorescence Imaging Device for Skin Diagnostics
## 2 Method of Skin Chromophore Mapping  
The general concept of snapshot skin chromophore mapping at fixed wavelengths, used in this work, is explained at Figure 1 for the case \(n=3\)[24]. The three-chromophore skin model suggests that the dominant absorbers in skin at any of the fixed wavelengths \(\lambda_{\text{j}}\) (j = 1, 2, 3) are oxy-hemoglobin (further abbreviated by a), deoxy-hemoglobin (b), and melanin (c)--see the crossings of their absorption curves with the vertical lines at Figure 1. The RGB image of the skin pathology spot is captured under illumination that comprises discrete spectral lines at wavelengths \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\). With respect to the spectral sensitivity of the image sensor and the cross-talk between its detection bands at the particular wavelengths, three spectral line images are extracted from the image dataset as described in [19; 20]. If the skin surface reflection is suppressed (e.g., by means of crossed polarizers), variations in chromophore composition will lead to changes of the diffusely reflected light intensities at each of the fixed wavelengths. Such variations in the pathology region relatively to the healthy skin can be estimated by measuring reflected light intensities from equally sized regions of interest in the pathology (\(I_{j}\)) and the adjacent healthy skin (\(I_{0j}\)). The ratios \(I_{j}\)/\(I_{0j}\) at each pixel or pixel's group of three spectral line images contain information on the concentration increase or decrease of all three regarded chromophores \(\Delta c_{i}\) (i = a, b, c), which can be further mapped over the whole image area.  
The intensity changes at three exploited wavelengths due to absorption can be represented in terms of the Beer-Lambert law:  
\[\left\{\begin{array}{l}\ln\left(\frac{I_{1}}{I_{01}}\right)=-l_{1}(\Delta c_ {a}\cdot\varepsilon_{a}(\lambda_{1})+\Delta c_{b}\cdot\varepsilon_{b}(\lambda _{1})+\Delta c_{c}\cdot\varepsilon_{c}(\lambda_{1}))\\ \ln\left(\frac{I_{2}}{I_{02}}\right)=-l_{2}(\Delta c_{a}\cdot\varepsilon_{a}( \lambda_{2})+\Delta c_{b}\cdot\varepsilon_{b}(\lambda_{2})+\Delta c_{c}\cdot \varepsilon_{c}(\lambda_{2}))\\ \ln\left(\frac{I_{3}}{I_{03}}\right)=-l_{3}(\Delta c_{a}\cdot\varepsilon_{a}( \lambda_{3})+\Delta c_{b}\cdot\varepsilon_{b}(\lambda_{3})+\Delta c_{c}\cdot \varepsilon_{c}(\lambda_{3}))\end{array}\right. \tag{1}\]  
where \(\varepsilon_{i}(\lambda_{i})\)--extinction coefficients of the three regarded chromophores at three exploited wavelengths, and \(l_{j}\)--absorption path length in skin at a particular wavelength. Chromophore concentration increase or decrease at each image pixel or selected group of pixels is found by solving the linear equation system (1). For an increased number of chromophores to be mapped (e.g., for \(n=4\)), the number of working wavelengths and equations in (1) has to be correspondingly increased.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 4 System Modelling
### Blockchain-Empowered Credit System  
In contrast to a Bitcoin system, where later, the user is discouraged from joining due to the significantly increased difficulty to obtain a new coin, our system offers the same initial credit, \(\varpi\), to any new user to the system. Users need to pay credits from their own balance to get help from peers and will earn credits after helping peers on computational tasks. At the beginning of each task period \(\Psi\in\{1,2,3,...\}\), the credit balance \(B_{k}^{\Psi}\) of each user \(u_{k}\) is obtained by the supernode by referring to the blockchain. For each user \(u_{k}\) in period \(\Psi\), we denote the amount of help received by peers as \(R_{k}^{\Psi}\), the amount of work contributed to peers as \(H_{k}^{\Psi}\) and the block reward as \(\eta_{k}^{\Psi}\). For simplicity, we limit a user to be either a requester, a helper or a miner within a given task period \(\Psi\). In our blockchain-empowered credit system, \(u_{k}\) needs to pay \(\alpha R_{k}^{\Psi}\) from and is rewarded \(\beta H_{k}^{\Psi}\) to the balance \(B_{k}^{\Psi}\); therefore,  
\[B_{k}^{\Psi+1}=\left\{\begin{array}{ll}B_{k}^{\Psi}-\alpha R_{k}^{\Psi}+\beta H _{k}^{\Psi}+\eta_{k}^{\Psi},&\Psi\geq 1,\\ \varpi,&\Psi=0.\end{array}\right. \tag{7}\]
# Multi-Objective Optimization of LCC-S-Compensated IPT System for Improving Misalignment Tolerance
## 3 Multi-Objective Optimization of Compensation Parameters
### Overview of MOPSO Algorithm  
With faster computing speed and better global search ability, the PSO algorithm can converge to the optimal solution with high probability, which is suitable for dynamic optimization. In order to solve the multi-objective model, the multi-objective particle swarm optimization (MOPSO) algorithm was used in this paper.  
The algorithm first generates the initial solution; that is, the population \(X=\{X_{1},\,X_{2},\,X_{3},\,\cdots,\,X_{m}\}\) composed of \(m\) particles is randomly initialized in the feasible solution space, where the position \(X_{i}=\{x_{i1},\,x_{i2},\,x_{i3},\,\ldots,\,x_{iD}\}\) of each particle represents a solution. The velocity of the \(i\)th particle is also a D-dimensional vector, denoted as \(V_{i}=\{v_{i1},\,v_{i2},\,v_{i3},\,\ldots,\,v_{iD}\}\). The optimal position searched for the moment by the \(i\)th particle is called the individual extreme and is denoted as \(p_{i\text{-best}}\), while the optimal position searched by the whole particle swarm is the global extreme, which is denoted as \(g_{\text{best}}\). Each possible solution is expressed as a particle in the population, and each particle has its own velocity vector, position vector and a fitness determined by the objective function. In each iteration, the particle will update itself by tracking with two extreme values. The individual optimal vector \(p_{i}\) and the global optimal vector \(p_{\text{g}}\) at the current moment are determined by calculating the objective function. During each iteration, each particle updates its speed and position according to:  
\[v_{i}^{k+1}=wv_{i}^{k}+c_{1}r_{1}(p_{i}^{k}-x_{i}^{k})+c_{2}r_{2}(p_{g}^{k}-x_ {i}^{k}) \tag{14}\]  
\[x_{i}^{k+1}=x_{i}^{k}+v_{i}^{k+1} \tag{15}\]  
where \(w\) is the weight of inertance, \(c_{1}\) and \(c_{2}\) are learning factors and \(r_{1}\) and \(r_{2}\) are independent random numbers, which are evenly distributed on \([0,1]\). The value of the inertia weight \(w\) indicates the ability to inherit the current velocity of the particle. In order to enhance the local optimization ability, this paper adopts a small, fixed weight. The acceleration constants \(c_{1}\) and \(c_{2}\) regulate the maximum stride length in the direction of \(p_{\text{i-best}}\) and \(g_{\text{best}}\), respectively. They determine the influence of individual and group experience of particles on the trajectory of particles and reflect the information exchange within the particle swarm. \(c_{1}=c_{2}\) is usually set so that individual experience and group experience have the same important influence and the final optimal solution is more accurate. In general, \(c_{1}\) and \(c_{2}\) can take values around 2. From the update formula of particles, the moving speed of a particle is determined by three parts: its original speed, the distance from its best experience and the distance from the group's best experience. Their relative importance is determined by the weight coefficient \(w\), \(c_{1}\) and \(c_{2}\), respectively.
# Pressure-Reducing Design of 3D-Printed Diabetic Shoe Midsole Utilizing Auxetic Lattice Structure
## 3 Results
### Plantar Pressure Distribution  
Plantar pressure distribution is a crucial indicator of shoe fit, where a comfortable shoe should distribute body weight as evenly as possible across the entire plantar area [42]. The viscoelastic properties of the midsoles made from the TPU material revealed stress distribution on the plantar foot surface in contact with the shoe insole during downward displacement. This facilitated the assessment of plantar pressure for different types of midsoles during different activities. The FEA results demonstrated the distribution of peak plantar pressure while walking and running with the A60, A75, and N90 midsoles (Figure 7a). Figure 7 shows that the plantar pressure was predominantly concentrated in the MH and HF regions, which are the most common areas for ulceration in diabetic foot patients [43].  
In both the walking and running scenarios, the peak pressure values across various regions of the foot were generally lower when wearing the midsoles with auxetic structures than when wearing the N90 midsole (Figure 7b,c). Under walking conditions, compared to the non-auxetic 90\({}^{\circ}\) midsole, the auxetic sample A75 reduced the peak pressures in the MH, MF, and HF regions of the foot by approximately 5.13%, 27.13%, and 36.48%, respectively; the auxetic sample A60, with the smallest internal angle, achieved reductions of about 19.68%, 48.93%, and 55.25%. Considering the impact of the internal angle, the peak pressures in the MH, MF, and HF regions of the foot were reduced by 15.34%, 29.92%, and 29.55%, respectively, for A60 compared to A75. During running, compared to the N90 midsole, the auxetic sample A75 showed reductions of approximately 16.2%, 18.62%, and  
Figure 7: Distribution of peak plantar pressure (**a**) and comparison of peak pressures between different regions of the plantar foot surface (**b**,**c**).  
9% in the MH, MF, and HF regions, respectively; the sample with the smaller internal angle (A60) exhibited reductions of approximately 45.49%, 54.39%, and 16.19%, respectively.
# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 3 Method
### Model Evaluation Methods  
In deep learning and machine learning, the commonly used evaluation metrics include the Accuracy (Acc), Precision (P), Recall (R), F1, and MIoU [42]. These metrics are calculated based on the following four fundamental values. High accuracy ensures that when the system issues an alert, it has a high degree of confidence, reducing false positives. High recall guarantees that of all the potential landslide events, as many as possible are identified and catastrophic missed reports are avoided. The F1 score provides a way to balance precision and recall, which helps to optimize the reliability and response efficiency of early warning systems at the same time. As an important indicator for image segmentation tasks, the MIoU can effectively evaluate the accuracy of the model for landslide boundary identification, which is crucial for making evacuation plans. These evaluation indexes need to be calculated by TP, TN, FP, and FN.  
TP is the actual positive example that is correctly predicted as positive. FP is the actual negative instance that is incorrectly predicted as positive. FN is the actual positive instance that was incorrectly predicted as negative. TN is the actual negative instance that is correctly predicted as negative. These metrics are all calculated based on the pixel level.  
Accuracy represents the proportion of all the correctly predicted samples; specifically, the sum of _TP_ and _TN_ in the total number of samples. This is shown in Equation (6):  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN}, \tag{6}\]  
Precision represents the ratio of _TP_ to the total number of predicted positive samples (_TP_ + _FP_). This is shown in Equation (7):  
\[Precision=\frac{TP}{TP+FP}, \tag{7}\]  
Recall, also known as the true positive rate, represents the proportion of actual positive samples that are correctly identified as positive. It is the ratio of _TP_ to the total number of actual positive samples (_TP_ + _FN_). This is shown in Equation (8):  
\[Recall=\frac{TP}{TP+FN}, \tag{8}\]  
The \(F1\) score, also known as the \(F1\) value, is the harmonic mean of the precision and recall. It provides a balanced measure of both metrics. This is shown in Equation (9):  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall}=\frac{2 TP}{2 TP+FP+ FN}, \tag{9}\]  
The MIoU represents the average ratio of the intersection to the union for each class in the dataset. Here, _p(i,j)_ denotes the number of pixels of class \(i\) predicted as class \(j\) (_FN_), _p(i,i)_ indicates the number of pixels of class \(i\) correctly predicted as class i (_TP_), and _p(j,i)_ represents the number of pixels of class \(j\) incorrectly predicted as class _i_(_FP_). This is shown in Equation (10):  
\[MIoU=\frac{1}{k+1}{\sum_{i=0}^{k}\frac{p_{ii}}{\sum_{j=0}^{k}p_{ij}+\sum_{j=0} ^{k}p_{ji}-p_{ii}}}=\frac{1}{k+1}{\sum_{i=0}^{k}\frac{TP}{FN+FP+TP}}. \tag{10}\]
## 4 Results
### Hydrological Model Calibration  
For rain gauge-based precipitation data, The NS values range from 0.40 to 0.97 with an average of 0.77. The calibrated model parameters are shown in Table 5. The mean relative bias on peak discharge and volume for all events are -7.07% and -0.7%, respectively (Table 6). For the events simulated with the GPM IMERG-E product, the average of NS is equal to 0.74 with a relative bias on peak discharge and volume of -20.57% and 3.99%, respectively. Thus, it can be concluded that the 17 events are well simulated (Figure 2). The model was able to reproduce the peak discharge and the shape of different hydrographs for the events occurred in the studied basin between 2011 and 2018 for both rain gauge-based precipitation data and satellite precipitation data (Figure 2 and Figure 4). These results are similar to the study of [63], who found that IMERG-E gave a good performance in term of model calibration using the HEC-HMS model. The overestimation of flood volume with GPM IMERG-E is consistent with the positive bias in IMERG-E found in the previous section. Table 5 shows the calibrated model parameters with observed or IMERG-E precipitation data. The CN values are very similar between the simulations with observed or IMERG-E precipitation. For some events, the Tc and Sc parameters show a greater variation between the two different sources of precipitation data, mostly due to compensation effects during calibration due to different rainfall inputs.  
Despite the fact that IMERG-E better reproduces observed rainfall at the daily time step rather than at the hourly time step, the hydrological model driven by IMERG-E is able to reproduce floods with a good efficiency. This could be partly explained by the fact that the hydrological model parameters are being calibrated, so the adjustment of its parameters makes it possible to compensate for the bias on the satellite precipitation. This can be seen in the Sc and Tc parameter values (Table 5) that are quite different between observed precipitation and IMERG. We also note slightly higher average CN values with the observed than with the IMERG precipitation, due to the overestimation of IMERG compared to the observed rainfall.
# Title-to-Length Coupling Analysis of an Off-Axis Optical Bench Design for NGGM
## 3 Local TTL Coupling
### Transmissive Optical Components  
Refractive indices of optical components, the beamsplitters in particular, will generate beam walk on the surface of the photodiode detectors. To investigate the TTL coupling due to the imperfect imaging of the lens systems and the impact of the mismatch between the interfering beams, for our simulation, the beamsplitters are assumed to be made of fused silica with a refractive index of 1.458; the refractive index of mirrors and lenses is chosen to be 1.517, as in N-BK7.  
The RX beam is rotated around the RX RP point for \(\pm 2\) mrad in yaw and pitch degrees of freedom, respectively. With the activation of the DWS feedback loop, the angle between the interfering beams received by the detector is minimised. LPS signals derived from the QPD1 and QPD2 are then calculated and the local TTL coupling noise is obtained. The results are shown in Figure 5. The figures depict the local TTL coupling in yaw and pitch degrees of freedom.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 2 Materials and Methods
### Data Pre-Processing
#### 2.4.4 Feature Extraction  
In order to recognize the different activities based on the processed activity segments, we have extracted a total number of 565 features divided into 16 sets: (1) Time series features [46]. (2) The signal-to-noise ratio (SNR). (3) Frequency series features. (4) Linear predictor coefficients (LPC) [51]. (5) Line spectral frequencies (LSF) [52]. (6) Distribution tests [53]. (7) Location tests [54]. (8) Dispersion tests [55]. (9) Stationary tests set [56]. (10) Correlation [46]. (11) Causation [57]. (12) Heteroscedasticity [58]. (13) Cointegration set [59]. (14) Instantaneous frequency set [60]. (15) Spectral entropy set. (16) Audio features set [61]. A summary of the extracted features can be found in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Set of Features** & **Feature** & **Description** \\ \hline \multirow{8}{*}{Time series features} & \multirow{4}{*}{Mean} & Several mean values were collected for: signal elements, linear weighted signal, power values in the signal, linear weighted power values, difference in adjacent elements, absolute difference in adjacent elements, power of difference in adjacent elements, and the absolute difference of differences. \\  & Skewness & Signal skewness \\  & Kurtesis & Signal kurtosis \\  & MAD & Signal mean absolute deviation \\  & Crossing rate & for 2, 1, 0, -1, -2 values \\  & RSSQ & Root sum of squares \\  & MAX & Maximum element in the signal \\  & MIN & Minimum element in the signal \\  & MAX-MIN & Difference between the max and min \\  & Quartile values & The first, second (also known as median), and the third quartile \\  & IQR & Inter quartile range (Q3-Q1) \\  & Normalized count of samples & For both the samples higher and lower than the mean of the signal. \\  & Element index & index of the first occurrence of the max and min signal elements \\  & cross-cumulants & second, third, and fourth order \\  & Number of peaks & based on a prominence value of 0.1 and 0.2 \\  & ARMA model parameters & Values for the AR, MA, and the model’s constant \\ \hline SNR & SNR & Signal to noise ratio \\ \hline \multirow{8}{*}{Frequency series features} & \multirow{4}{*}{Mean frequency features} & The signal’s median angular frequency of: the entire signal, the first 10\% of the signal, \\  & Mean frequency & the next 10\% of the signal,..., the last 10\% of the signal \\ \cline{1-1}  & Bandwidth & The occupied bandwidth of the signal \\ \cline{1-1}  & Power bandwidth & Compute the bandwidth of the part that is 3-dB from the peak value \\ \hline Linear predictor coefficients & LPC & The second, third, and fourth coefficients \\ \hline \multirow{2}{*}{Line spectral frequencies} & \multirow{2}{*}{LSF} & Obtained from the prediction polynomial with coefficients obtained from the LPC \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted features.
## 3 Solution Approach  
Figure 2 presents the various steps of the proposed solution approach. In the first step, data are collected for the network, EVs, technical parameters, and cost. The AHP (analytic hierarchy process) method is then used to determine the weight of the costs and the revenue criteria. Following this, the backward/forward sweep power flow method is used to calculate power losses in the distribution network. The particle swarm optimization (PSO) algorithm is then employed to determine the number of electric vehicles in each bus and reduce the losses. Finally, the revenue is calculated, which is net income minus the required cost, and at the end, the total benefit is computed. The model was built using Python.
# The Effect of Laser Pulse Widths on Laser--Ag Nanoparticle Interaction: Femto- to Nanosecond Lasers
###### Abstract  
The effect of the laser pulse width on the production of nanoparticles by laser fragmentation was investigated. Laser pulse widths of 164 fs, 5 ps, 4 ns, 36 ns, 64 ns, and 100 ns were used. To assess the effect of the laser pulse width on the energy distribution in the nanoparticles, the energy distribution was simulated using wave optics. Silver (Ag) nanoparticles were produced by laser irradiation of an Ag target in distilled water. The wavelength of the femtosecond, picosecond, and nanosecond lasers used was 1070 nm, and their fluences were 0.10-0.13 mJ/cm\({}^{2}\). Nanoparticle microstructure was visualized by transmission electron microscopy and scanning electron microscopy, and the nanoparticle size distribution was evaluated using a particle size analyzer.  
laser fragmentation; laser pulse width; nanoparticles +
Footnote †: journal: Applied sciences