esa-sceva/satcom-chunk-collection huggingface dataset:
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 4 Use of Doped ZnO NPs in the Biomedical Field
### Nanotools for Photobiomaging  
#### 4.3.1 Photoluminescence Properties  
Pure ZnO has a wide band-gap of about 3.3 eV at room temperature and thus can only absorb UV light which is just 5% of the solar spectrum [239]. To improve the light absorption efficiency toward a more biologically-relevant visible-near infrared (vis-NIR) region, the modification of ZnO nanoparticles with impurities incorporated in the host lattice has been introduced. Actually, the stimulation of the photoluminescence response of doped ZnO NPs by using vis-NIR light, which does not damage tissues and organs, is expected to be particularly useful in bioimaging applications. Herein, to extend the emission range from the intrinsic band-gap of ZnO to the infrared region, doping with optically-active impurities like transition metals (TM) or rare earth (RE) elements has been reported even though TM and RE elements reveal different spectral properties [239]. For TMs, as the influence of the surroundings is stronger due to the weaker shielding of the 3d-shell by the 3s orbit, the spin-orbit coupling is weaker and the 3d-levels are broad. As a result, the energetic spacing varies with the host material and the electron-phonon interaction appears stronger than RE elements [240]. For RE elements, because of the strong shielding of the 4f-shell by the 5s2 and 5p6 orbits, the interaction with the surroundings is weak and almost all RE elements show intra-shell luminescence, i.e., the one least occupied, and unoccupied intra-shell levels are located in the band-gap of ZnO [240,241]. Thus, a very similar chemical behavior for all lanthanides in ZnO has been identified, whereas filling the d-shell in transition metals has a stronger influence on the outer shells and its bonding behavior in the ZnO lattice. To this end, there are various reports incorporating optically-active dopants in the ZnO lattice to change the band-gap value and increase the UV luminescence properties of pure ZnO [242, 243, 244]. Additionally, the optical parameters (band-gap) of the doped ZnO nanoparticles are summarized in Table 3. The band-gap values of the doped ZnO depend on several factors such as grain size, carrier concentration, lattice strain, size effect, oxygen vacancy and its related disorder-created localized defect states within the band-gap of ZnO, orbital hybridization between the dopant and the host band, etc. [245,246]. Depending upon these factors, red shift behavior (or a decrease in the band-gap) and blue shift behavior (or an increase in the band-gap) have been reported by several research groups (Table 3).
# Rangeland Productivity Partitioned to Sub-Pixel Plant Functional Types
## 2 Materials and Methods
### NDVI Disaggregation
Due to the spatial and temporal scale of analysis, we implemented these methods in Google Earth Engine [28] and developed a refined sampling scheme of pixels to account for the variability of NDVI within PFTs. We used the EPA Level IV ecoregions to define relatively homogenous areas based on ecological and climatic characteristics [29] in which the phenological characteristics within each PFT were assumed to be similar. Within each ecoregion the pixel level NDVI, percent cover of each PFT, and pixel coordinates were extracted for 600 points, selected through a stratified random sample. Each ecoregion was stratified by areas of high relative PFT fractional cover, defined as pixels with a fractional cover greater than the 90 th percentile of fractional cover for the respective PFT across the ecoregion. For each PFT 100 random points were sampled within the areas containing the purest available pixels, enabling a more robust estimation of the mean phenology of each PFT. Then, for every pixel within the ecoregion, the distance to each sample point was calculated allowing us to weight the sample points by distance. As valid NDVI values can only range between \(-1\) and \(1\) we used ridge regression [30] to solve the equations while effectively constraining the coefficients within reasonable ranges. Ridge regression adds a parameter imposing a penalty on the size of the coefficients. The resulting coefficients are estimates of the mean NDVI for each PFT across the sampled region for each pixel. While weighting by distance accounts for some phenological variability across an ecoregion, finer scale heterogeneity is still missed. Slight shifts in elevation, aspect, or soil hydrology can result in departures from the mean phenology. For example, the mean NDVI of a PFT across a dryland ecoregion does not represent specific dynamics of that PFT occurring in small mesic patches within the ecoregion. To account for this variation within PFTs, we adjusted the estimated NDVI of each PFT for each pixel so that the estimated pixel NDVI matched the actual pixel NDVI. To do so, we assumed the NDVI contribution of each PFT to the estimated NDVI was proportional to the contribution to the adjustment (the difference between the pixel NDVI and estimated NDVI).
## 3 Bibliometric Analysis
### Preliminary Analysis of Publications
It is therefore curious that the number of articles is not higher, although the number of publications related to AI and aerospace complexity will certainly increase in the future. In addition to the publications by the WoS category, the number of publications by journals or conferences is also presented in this preliminary analysis. This analysis (Figure 4) complements the previous analysis by category by allowing us to localise the interest in this topic. There are many conferences and journals interested in this topic, reaching up to  
Figure 3: Number of publications related to air traffic complexity organised by WoS category. 113 different fields. But, in Figure 4, the 15 journals or conferences with the most contributions have been represented. In this case, most of the publications related to air traffic complexity belong to the \"IEEE AIAA Digital Avionics System Conference\" (1). In total, 19 publications belong to this conference. Another conference that also shows great interest in this topic is the \"IEEE Conference on Decision and Control\" (2), with six contributions. Among the journals with the most publications in this topic, the \"Journal of Air Transport Management\" (3), with eight publications; \"Aerospace\" (4), with five publications; and \"IEEE Transactions on Intelligent Transportation Systems\" (5), with five publications, stand out. This preliminary analysis has attempted to demonstrate the growing interest in the engineering industry, and especially in the aerospace industry, in solving the problem of complexity in aerospace. Therefore, through this analysis, it has been possible to answer the following question:  
Is the study of air traffic complexity really relevant? The answer is as follows: more and more papers related to complexity are published in different journals and conferences, and citations to publications on this topic have grown in recent years, along with the number of publications. This makes the topic interesting, and interesting to study and perform a bibliometric analysis on it. In addition, air navigation system providers are interested in complexity indicators to measure the workload of air traffic controllers [15]. Therefore, it can be said that both academia and industry are interested in air traffic complexity. Once this analysis is performed, the performance analysis within the bibliometric analysis begins. This will focus on the analysis of co-citations and co-authorship to help to identify the main contributions to the sector. Figure 4: Number of publications related to air traffic complexity organised by journal or conference.
## 4 Mechanical Test Results and Discussion
### Tensile Behavior
#### 4.2.4 Energy Absorption Capacity  
Figure 14 shows the energy absorption capacity of the 7 d and 28 d cured specimens, obtained by measuring the area under the stress-strain curves up to the peak point, as shown in Figure 10a,b. It is seen that specimens D7, F7, and H7 with smaller dosages of GGBS, SF, and CKD absorbed considerable energies with values of 458.1 kJ/m\({}^{3}\), 281.1 kJ/m\({}^{3}\), and 667.1 kJ/m\({}^{3}\), respectively. However, as the contents of the mineral additives increased, the values decreased to 182.8 kJ/m\({}^{3}\), 153.7 kJ/m\({}^{3}\), and 434.5 kJ/m\({}^{3}\) for specimens E7, G7, and I7, respectively. On the other hand, specimens A7, B7, and C7 absorbed relatively low energies of 144.6 kJ/m\({}^{3}\), 93.3 kJ/m\({}^{3}\), and 206.9 kJ/m\({}^{3}\) and exhibited higher energy absorption capacities on increasing the proportion of silica flour.  
As the curing time increased to 28 d, the energy absorption capacity increased enormously as compared to that of 7 d cured specimens except for specimen H. Specimens D28, F28, and H28 with smaller dosages of GGBS, SF, and CKD exhibited energy absorption capacities of 560.7 kJ/m\({}^{3}\), 442.0 kJ/m\({}^{3}\), and 475.3 kJ/m\({}^{3}\), respectively. Moreover, energy absorption capacities of specimens E28, G28, and I28, with higher dosages of mineral addi  
Figure 14: The energy absorption capacity of 7 d and 28 d cured specimens.  
Figure 13: SEM images on PE fibers pulled out of 28 d cured specimens.  
tives, were measured at 532.2 kJ/m\({}^{3}\), 419.3 kJ/m\({}^{3}\), and 493.2 kJ/m\({}^{3}\), respectively. Unlike in the case of 7 d cured specimens, the increase in the contents of the mineral additives did not cause significant decreases in the energy absorption capacity of the 28 d cured specimens. In the case of GGBS and SF, this is attributed to the slower rate of reaction than that of OPC. On the other hand, in the case of CKD, the higher energy absorption capacity of specimen I28, as compared to that of H28, can be attributed to the fast reaction of the materials and the lower amount of the total hydrates, as discussed earlier. The higher amount of the non-reactive silica flour also resulted in the higher energy absorption capacity of specimen C than that of specimen B at both 7 d and 28 d. Specimens A28, B28, and C28 exhibited significantly increased energy absorption capacities as compared to those of the 7 d cured specimens with values of 537.2 kJ/m\({}^{3}\), 446.1 kJ/m\({}^{3}\), and 558.7 kJ/m\({}^{3}\), respectively.
## 4 Results and Discussion  
### Effect of Friction  
The objective is to investigate the influence of friction during the collision process between the droplet and the blade's flat surface for varying coefficients. Figure 6 illustrates the von Mises and S33 stress graphs, representing the normal stress applied on the LEP top surface when the friction coefficient \(\mu\) is implemented in the model, ranging from frictionless to 0.5. This graph is complemented by Table 3 showing the computational time of each simulation. According to [PERSON] et al. [33], the static friction coefficient is larger than the dynamic friction coefficient in the sliding regime of a droplet impacting a solid surface. The current paper considers only the static coefficient, as it has a more significant impact compared to the kinetic, or in-motion, friction. The static coefficient \(\mu_{s}\) typically ranges from 0.114 to 0.428 for droplet collision [33].  
The graph (b) in Figure 6 shows minimal changes due to the variation of the friction coefficient. Indeed, all curves exhibit the same overall trend, with higher friction coefficients leading to more convergent curves. However, the graph (a), presented in Figure 6, shows a variation of von Mises peak stress depending on the value of the friction coefficient. Indeed,  
Figure 5: von Mises and S33 stress analysis for tie, bonded, and merged contact interactions between Filler and LEP assigned with same materials.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Friction Coefficient** & **WALL Time (s)** \\ \hline
0 & 509 \\ \hline
0.1 & 452 \\ \hline
0.2 & 437 \\ \hline
0.3 & 411 \\ \hline
0.4 & 412 \\ \hline
0.5 & 416 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Computational time for different friction coefficients (from frictionless up to \(\mu=0.5\)).  
the greater the friction coefficient, the greater the von Mises stress. The value seems to converge to 65 MPa as the friction coefficient increases. The computational time decreases until stabilising after a friction coefficient of 0.2. Frictionless conditions seem to be a less favourable solution as they tend to diverge more than the other curves, particularly in relation to stress propagation through the layers. The low impact of the friction coefficient in this contact scenario is predictable given the low viscosity of the water droplet, and consequently, the low resistance in contact with the solid surface. A friction coefficient of 0.2 will be retained for further simulations as it represents a decent compromise between accuracy and computational performance.
# Stress Analysis and Spalling Failure Simulation on Surrounding Rock of Deep Arch Tunnel
## 2 Stress Analysis of Surrounding Rock
### Determination of the Mapping Function  
According to the principle of angle-preserving transformation, the boundary outer domain of the arched hole on the z-plane can be converted to the unit circle boundary outer domain on the \(\zeta\)-plane by the mapping function Z, as shown in Figure 2. The mapping function is as follows:  
\[Z=\omega(\zeta)=R(\zeta+\sum_{k=0}^{\infty}C_{k}\zeta^{-k}) \tag{11}\]  
where \(R\) is a real number and is related to the hole size on the z-plane, and \(C_{k}\) is generally a series of complex constants. However, it represents the real constants when the hole is symmetric on the \(x\)-axis [62].  
A given point on the boundary of the z-plane surface hole is marked by \(A_{j}\), and polar coordinates \((r_{j},\ \alpha_{j})\). Assuming that its polar coordinates correspond to a point mapped on the \(\zeta\)-plane as \((1,\ \theta_{j})\), the relationship between the two points can be determined according to Equation (11):  
\[r_{j}e^{i\alpha_{j}}=R(e^{i\theta_{j}}+\sum_{k=0}^{\infty}C_{k}e^{-i\delta \theta_{j}}) \tag{12}\]  
Equation (12) can be modified as follows:  
\[r_{j}=R(e^{i(\theta_{j}-\alpha_{j})}+\sum_{k=0}^{\infty}C_{k}e^{-i(k\theta_{j }+\alpha_{j})}) \tag{13}\]  
According to [PERSON]'s formula, the real and imaginary parts of Equation (13) give the following:  
\[\sin(\alpha_{j}-\theta_{j})+\sum_{k=0}^{\infty}C_{k}\sin(\alpha_{j}+k\theta_{j })=0 \tag{14}\]  
\[r_{j}=R[\cos(\alpha_{j}-\theta_{j})+\sum_{k=0}^{\infty}C_{k}(\cos\alpha_{j}+k \theta_{j})] \tag{15}\]Assuming that the polar position \((1,\ 0)\) on the boundary of the unit circle hole on the \(\zeta\)-plane corresponds to the mapping point \((r_{0},\ 0)\) on the z-plane, we have the following:  
\[R=\frac{r_{0}}{1+\sum\limits_{k=1}^{\infty}C_{k}} \tag{16}\]  
Substituting the \(m\) sampling points of the boundary attachment of the z-plane surface hole into Equation (15), the objective function \(h\) can be defined according to the least squares principle as follows:  
\[h=\sum\limits_{j=1}^{m}\left\{r_{j}-R[\cos(\alpha_{j}-\theta_{j})+\sum \limits_{k=0}^{n+1}C_{k}(\cos\alpha_{j}+k\theta_{j})]\right\}^{2} \tag{17}\]  
In Equation (17), \(C_{k}\) and \(\theta_{j}\) should satisfy the following conditions:  
\[\sin(\alpha_{j}-\theta_{j})+\sum\limits_{k=0}^{n+1}C_{k}\sin(\alpha_{j}+k \theta_{j})=0\quad\ j=1\,\ 2,\ 3,\ \ldots,\ m \tag{18}\]  
In the above equations, \(C_{k}\) can generally be solved by optimization methods [63].
# Sequential Movie Genre Prediction Using Average Transition Probability with Clustering
## 3 Genre Prediction Algorithm
### Average Transition Probability of Genres  
#### 3.3.1 Markov Chain for Set of Genres  
In our genre prediction system, we use transition probabilities from genre to genre. It is known that many approaches for the sequential recommendation, the MC is used to reflect the short-term sequential behavior of a user. The MC assumes the next choice of item depends only on the current choice. Formally, it is described as follows. The transition probability matrix is generated for each cluster. To do this, we first consider the sequence of selected movies for each user in a cluster. However, as described before, a movie may contain multiple genres such as romance, action, and comedy simultaneously. We consider all genres included in the current and next movies and count them in the \(n\) by \(n\) matrix. We consider the transition matrix for each cluster separately. Then, for all \(C_{l}\in\mathcal{C}\), we summarize them for all the user's chosen movies and normalize them to obtain the transition probability from genre to genre as shown in Figure 5.  
Figure 3: Example of data preprocessing for movie genre prediction (\(n=19\)).  
To describe this, we let \(M_{t}^{l}\) and \(M_{t-1}^{l}\) be the selected movie sets for all user \(u\in C_{l}\) at time \(t\) and \(t-1\), respectively. Then, the transition probability of the first-order Markov chain for the movie selection for the cluster \(l\) is given by:  
\[p(M_{t}^{l}|M_{t-1}^{l}). \tag{1}\]  
However, in the genre prediction, we focus on the transition from genre (included in a movie) to genre. For this, we let \(\mathcal{G}_{t}\subset\mathcal{G}\) be the set of genres that are contained in the movie \(M_{t}^{l}\) for all user \(u\in C_{l}\) at time \(t\). Consider two genres \(i,j\in\mathcal{G}\); we model the genre transition probability in the cluster \(C_{l}\) as:  
\[p_{ij}:=p(j\in\mathcal{G}_{t}^{l}|i\in\mathcal{G}_{t-1}^{l}). \tag{2}\]
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 4 Parallel Edge Deblocking Filter Architecture (PEDBF)
### Filter Module
#### 4.3.3 Filter Decision Unit  
Based on the values of the parameters \(\beta\) and \(t_{c}\), the strength of the filtering procedure to be used for a 4 \(\times\) 4 pixel block is determined. The pixel threshold values for the two neighboring blocks are decided by the filter decision unit.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 3 Experiments and Results
### Elitism Size  
Table 3 shows the influence of elitism on the performance of the GA. The results were also obtained with OX crossover and a population of 25 individuals. Mutation rate was varied between 0 and 1%. The best results are highlighted in bold type. As the table shows, the best result was obtained for an elitism of 6, with a mutation rate of 1%. Those parameters required the shortest processing time and the lowest number of generations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Population**} & \multicolumn{2}{c}{**OX**} & \multicolumn{2}{c}{**ER**} \\ \cline{2-5}  & **Time** & **Generation** & **Time** & **Generation** \\ \hline
10 & 8233 & 46.10 & 21,733 & 85.60 \\
15 & 6900 & 39.70 & 19,366 & 74.84 \\
20 & 7833 & 40.28 & 19,733 & 76.70 \\
**25** & 6366 & **37.12** & 18,600 & 75.00 \\
**30** & **5933** & 38.23 & 18,266 & 73.20 \\
35 & 7800 & 41.77 & 19,200 & 74.32 \\
40 & 9900 & 45.49 & 19,033 & 77.80 \\
45 & 11,866 & 47.92 & 18,300 & 72.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of different crossover methods: Ordered Crossover (OX) and Edge Recombination (ER). Elitism was 15 and Mutation Rate 1%.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Population**} & \multicolumn{2}{c}{**M = 0\%**} & \multicolumn{2}{c}{**M = 0.1\%**} & \multicolumn{2}{c}{**M = 1\%**} & \multicolumn{2}{c}{**M = 2\%**} & \multicolumn{2}{c}{**M = 5\%**} \\ \cline{2-11}  & **Time** & **Gen.** & **Time** & **Gen.** & **Time
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 3 Systematic Errors
### Viewing Geometry (Space)
#### 3.1.1 Topographic Phase
However, we note that values lower than 10 m are possible. Sentinel-1 baselines are often less than 150 m, with values as low as 50 m or less being frequent [58], making the sensor unsuitable for topographic mapping but ideal for deformation mapping. A thorough literature review on the topic targeted at Sentinel-1 was discussed in [PERSON] [57]. ERS and ENVISAT both have extremely high perpendicular baselines, which provides enough height ambiguity for DEM generation (successful example, e.g., [59]). The topographic phase is often eliminated for deformation mapping applications using an arbitrary reference surface (usually Shuttle Radar Topography Mission, SRTM). Nevertheless, if the reference surface is not correct (not up to date, low spatial resolution, or low vertical accuracy) or if the InSAR geometric parameters (i.e., system altitude, baseline, and orbit position) are inaccurate, there will be topographic phase residuals that will substantially impact the output deformation product. As a result, an accurate DEM and geometric parameters are deemed critical for properly detecting minor deformation. Some common ways to reduce topographic residuals include stacking interferograms [61], small baseline subset (SBAS) processing [62; 63; 64], or Multiple Aperture SAR Interferometry (MAI) [65; 63].
## 3 Methodology
### Implementation Details
#### 3.2.2 Depthwise Separable Convolution  
Depthwise separable convolution (DWConv) [61] is a special convolution operation in CNNs, and the structure of DWConv is shown in Figure 4. It includes depthwise convolution and point convolution operations. Among them, \(H\) and \(W\) indicate the height and width, respectively, and \(M\) and \(N\) indicate the numbers of input and output channels, respectively:  
* Depthwise Convolution  
In the depthwise convolution stage, one \(k\times k\times 1\) convolutional kernel is applied independently to each input channel, producing a feature map of \(M\) size (\(H\times W\)). This stage does not perform linear combinations across channels but processes each channel separately, thereby reducing the amount of computation and the number of parameters;  
* Pointwise Convolution  
This convolution stage employs one \(1\times 1\times M\) convolution kernel to linearly combine the feature map output from the depthwise convolution and generate new feature maps. This is a cross-channel operation, which can adjust the number of channels in the feature map to facilitate the extraction of higher-level features.  
Figure 3: C2f_Van module structure diagram.  
To improve the recognition of pedestrian crossings and guiding arrows, DWConv is employed to replace the traditional convolution operations. Specifically, specific layers starting from the second layer are converted to DWConv, such as the 3 \(\times\) 3 standard convolution. The advantage of this transformation is the reduction in the number of parameters and the calculation so that the model can greatly improve the detection efficiency in resource-constrained environments.  
For an input vector with \(M\) channels operated on by a standard convolution kernel of \(k\times k\) size to produce an output vector with \(N\) channels, the number of \(P_{Conv}\) parameters required is calculated as shown in Equation (3). In contrast, when DWConv is used, the equation for calculating the number of \(P_{DWConv}\) parameters is shown in Equation (4):  
\[P_{Conv}=k\times k\times M\times N \tag{3}\]  
\[P_{DWConv}=k\times k\times M+M\times N \tag{4}\]  
To describe the difference between DWConv and standard convolution in the number of parameters, the ratio (\(R\)) is introduced, which is calculated using Equation (5):  
\[R=\frac{P_{DWConv}}{P_{Conv}}=\frac{k\times k\times M+M\times N}{k\times k \times M\times N}=\frac{1}{N}+\frac{1}{K^{2}} \tag{5}\]  
When the 3 \(\times\) 3 DWConv is used in the proposed model, the number of parameters is reduced to about one-ninth of the standard convolution. This reduction in the parameter count is crucial for edge deployment, as the simplified model structure makes deploying the model in resource-limited environments more feasible. On the Jetson Orin Nano, this lightweight model structure not only reduces the storage requirements but also improves the computational efficiency, making the model more suitable for the real-time detection of crosswalks and guide arrows.