esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Proactive Caching for Multi-View Streaming Mobile Augmented Reality
## 2 Related Work
Similarly, they also try to optimize the overall users' satisfaction by formulating a Mixed Integer Linear Problem (MILP) with computational and bandwidth constraints. In addition, the paper proposes a Fairness-Based Representation Selection (FBRS) heuristic algorithm to achieve near-optimal results [24]. However, the focus in that work is mostly on the application layer quality of experience and received utility without emphasis on the wireless transmission process [24]. In [4], an interactive multi-view HTTP adaptive streaming is proposed with the emphasis in exploring the optimal subset of views under channel constraints. Due to the NP-hardness of the corresponding optimization problem, the paper offers the design of a sub-optimal low-complexity greedy algorithm with focus on network congestion episodes in hot spot areas. The work in [25] anchors its contribution in an optimization framework for the allocation of power and bandwidth in multi-user video streaming to improve the quality of user experience (QoE). Their work tracks requests in the unmanned aerial vehicle (UAV) relay networks and proposes a decentralized allocation scheme to derive the optimal solution [25]. The work in [26] shares a similar target with the current work. More specifically, in order to solve the pro-caching problem for vehicular multi-view 3D video streaming, the authors explicitly consider the mobility and construct a user mobility model that includes predicted moving direction and destinations. The difference is that they treat it as a Markov decision problem, and such a joint optimization problem concentrates on the overall reward and cost in 5G small-cell networks [26]. Due to the complexity of finding optimal decisions, a dynamic K-Nearest Neighbor algorithm is embedded into their proposed deep learning approach to provide the allocation for cache memory and the selection for the views set [26]. In [27], an object detection model of AR applications in an EC-supported network is considered, and the authors provide effective task placement and offloading strategies achieving the balance between latency and energy. Furthermore, the work in [27] directly identifies three latency classes and achieves the task execution time through historical data, while in this paper, a more detailed communication process is focused and calculated from the given formula.
## 2 Methods
### Satellite Data and Preprocessing  
Landsat TM/ETM\(+\) data were acquired from the United States Geological Survey (USGS) EarthExplorer ([[http://edcsns17.cr.usgs.gov/NewEarthExplorer/](http://edcsns17.cr.usgs.gov/NewEarthExplorer/)]([http://edcsns17.cr.usgs.gov/NewEarthExplorer/](http://edcsns17.cr.usgs.gov/NewEarthExplorer/))). The 8-day MODIS surface  
Figure 1: Land cover map of three study areas.  
reflectance products at 500-m resolution (MOD09A1) were acquired from the National Aeronautics and Space Administration (NASA) Reverb portal ([[http://reverb.echo.nasa.gov/reverb/](http://reverb.echo.nasa.gov/reverb/)]([http://reverb.echo.nasa.gov/reverb/](http://reverb.echo.nasa.gov/reverb/))). Only the Collection 5 MODIS data were used for the study. The data used in the study are shown in Table 1.  
Both fine- and coarse-resolution images were preprocessed before the calculation. In this study, the top atmosphere Landsat TM/ETM\(+\) data were atmospherically corrected and converted to surface reflectance using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) [27]. Cloud and snow masking was performed using the automated cloud-cover assessment (ACCA) algorithm [28,29] built into LEDAPS. The atmospheric correction method in LEDAPS for Landsat TM/ETM\(+\) data is based on the 6S model, which is also used for MODIS data [5,6]. The MODIS surface reflectance product data (MOD09A1) were re-projected, clipped and resampled (30 m, bilinear approach) to the same extent as Landsat TM/ETM\(+\) data using the MODIS reprojection tools.
# Numerical Simulation and Parameter Optimization of Air Slide Based on CFD-DEM
## 1 Introduction
This research gap exists primarily because wheat flour exhibits relatively high adhesion properties, which substantially compromise its fluidization effectiveness in such conveying systems. This study employs a CFD-DEM coupled approach using Fluent v2023 and Rocky v23R1 software to numerically simulate the conveying process in an air slide. The fluid dynamics data from Fluent are imported into Rocky to analyze its interaction with wheat flour particles, while the particle-fluid feedback is returned to Fluent to achieve bidirectional coupling. Rocky's statistical methods are utilized to evaluate flour-conveying performance. The research focuses on investigating the effects of inclination angle, input air velocity, and air-permeable layer porosity on conveying ratio and material layer stability. Through multifactorial orthogonal experiments, the influence weights of different parameters were analyzed, leading to optimized air slide design. The findings provide practical guidance for engineering applications of air slide systems.
# Susceptibility of _Staphylococcus epidermidis_ to Argon Cold Plasma Jet by Oxygen Admixture
## 1 Introduction
The noble gas plasma jet is relatively the easiest kind to generate, as its breakdown voltage is less than the air and N\({}_{2}\), but they are not as reactive as air plasma jets. Therefore, some researchers mix the noble gases with a small percentage of reactive gases. In this case, the noble gas is used to serve as the carrier gas to generate the plasma. O\({}_{2}\) or H\({}_{2}\)O\({}_{2}\) is often added for biomedical applications, while O\({}_{2}\) or CF\({}_{4}\) can be frequently used for applications of etching. In addition, researchers, who work on N\({}_{2}\) plasma jets, faced the constituent of reactive radicals too [20]. Since _S. epidermidis_ infects the skin and soft tissue causing pain, swelling, warmth, and redness in the infected area [21], therefore, this work aimed to study the effect of O\({}_{2}\) admixture to argon non-thermal APPJ to obtain the desired reactive oxygen species (ROS) inducing maximum antibacterial activity. This treatment will allow the possibility of treatment of _S. epidermidis_ infected skin. Several trials were established with different settings for the argon non-thermal APPJ to study the optimum processing conditions that accomplish the maximum bacterial inactivation.
###### Abstract  
Existing transmission lines and pipelines are frequently crossed and erected in parallel, meaning that if lightning strikes a wire and causes insulator flashovers, the resulting lightning current will spread through the grounding of the tower where the flashover insulator is located. This dispersion of current can lead to overvoltage effects on nearby pipelines. This study performs simulation calculations to analyze the overvoltage experienced by pipelines due to the dispersion of grounding current from the tower. Furthermore, this paper proposes a method for protecting the pipeline from such an overvoltage. Firstly, the lightning transient calculation model of a transmission line tower is constructed using the electromagnetic transient software ATP-EMTP 5.5. The model calculates the effects of lightning peak currents and soil resistivity on the distribution characteristics of lightning current in the tower, specifically in the area where the flashover insulator is located. Subsequently, a calculation model of the tower grounding grid-natural gas pipeline is developed, taking into account the distribution characteristics of lightning current in the tower. This model analyzes the impact of lightning peak currents, soil resistivity, and pipeline spacing on pipeline overvoltage. Finally, the effectiveness of the solid-state decoupler in mitigating lightning overvoltage in the pipeline is verified. The results demonstrate a positive correlation between the lightning current entering the tower grounding grid through the flashover insulator and the lightning current distribution characteristics. The solid-state decoupling device proves to be effective in reducing the voltage of the pipeline insulation layer, and the simulation results provide the optimal laying length of the bare copper wire.  
lightning stroke conductor; natural gas pipeline; grounding current divergence; pipeline overvoltage protection
# Anti-Adhesion Superhydrophobic High-Frequency Electrotome
## 1 Introduction
[24] utilized femtosecond pulsed laser processing to process micro-/nanostructures on the surface of the HFE, which reduced thermal damage and improved anti-adhesive properties. [PERSON] et al. [25], inspired by pangolin scales, prepared a bionic scale structure on an HFE by means of a long-pulsed laser, and the water contact angle was increased from 68\({}^{\circ}\) to 94\({}^{\circ}\). The coefficient of friction and the mass of tissue adhesion of the bionic HFE were reduced by 14.9% and 16.5%, respectively, compared to the initial surface. However, HFE's anti-adhesion ability relying on microstructures is still limited. [PERSON] et al. [26] were inspired by corn blades to fabricate a groove structure arranged equidistantly along the direction of the blade and coated it with a TiO\({}_{2}\) film to develop a novel HFE with a coupled biomimetic anti-adhesive surface, which increased the water contact angle from 71\({}^{\circ}\) to 148\({}^{\circ}\) and reduced the tissue adhesion quality by 71.43%. However, it is worth noting that the TiO\({}_{2}\) film coating may have some impact on the electrical conductivity of the HFE, which is a factor to be considered in practical applications. [PERSON] et al. [27] constructed uniformly arranged micro-pit structures on the surface of the HFE and injected silicone oils after silanization to form a liquid-infused micro-structured surface to reduce tissue adhesion. However, silicone oil is easily lost at high temperatures, which may result in the inability of the HFE to perform surgical operations consistently and efficiently, thus affecting the continuity and effectiveness of the procedure [28; 29]. Therefore, a safe, stable, conductive, and low-cost method is needed to improve the anti-adhesion performance of HFEs. In this paper, we propose a simple and environmentally friendly new superhydrophobic high-frequency electrotome (SH-HFE) based on surface patterning, which was fabricated by nanosecond laser and stearic acid modification with blood anti-adhesion and tissue anti-adhesion. We first studied the influence of laser processing parameters, such as laser power, scanning speed, number of processing, and line spacing, on the surface wettability and surface roughness of the HFE. We then analyzed the surface chemical compositions of the ordinary HFE and SH-HFE. Then, inspired by the excellent hydrodynamic properties and anti-adhesion properties of shark skin due to its structure being naturally selected over a long period of time, we fabricated three different patterned microstructures on the HFE to mimic the micro- and nanostructures of shark skin, and compared the performance of the ordinary HFE to that of SH-HFEs with three different patterned microstructures in meat-cutting and blood-dipping experiments. The SH-HFE with arrowhead patterns had the best performance in blood anti-adhesion and pork tissue anti-adhesion. This provides a new technology for the anti-adhesion performance of HFEs and is expected to improve the efficiency and safety of surgeries.
## 2 Developments of the Methods
\right)\]  
whose solutions are given by  
\[a_{1} =\frac{1}{2}\,\frac{-3\,{A_{1}}^{2}A_{3}B_{2}+3\,{A_{1}}^{2}A_{2} B_{3}+4\,{A_{1}}{A_{3}}^{2}B_{2}-4\,{A_{1}}{A_{2}}^{2}B_{3}-12\,{A_{2}}{A_{3}}^{2}B_{1} +12\,{A_{2}}^{2}A_{3}B_{1}}{-4\,{A_{1}}{A_{2}}^{2}+3\,{A_{1}}^{2}A_{2}+4\,{A_{ 1}}{A_{3}}^{2}-3\,{A_{1}}^{2}A_{3}-6\,{A_{2}}{A_{3}}^{2}+6\,{A_{2}}^{2}A_{3}},\] \[a_{2} =\frac{{A_{1}}^{2}B_{2}-{A_{1}}^{2}B_{3}-2\,{A_{3}}^{2}B_{2}-4\,{ B_{1}}{A_{2}}^{2}+2\,{A_{2}}^{2}B_{3}+4\,{B_{1}}{A_{3}}^{2}}{-4\,{A_{1}}{A_{2}}^{ 2}+3\,{A_{1}}^{2}A_{2}+4\,{A_{1}}{A_{3}}^{2}-3\,{A_{1}}^{2}A_{3}-6\,{A_{2}}{A_ {3}}^{2}+6\,{A_{2}}^{2}A_{3}},\] \[a_{3} =-\frac{1}{2}\,\frac{2\,{A_{1}}B_{2}-2\,{A_{1}}B_{3}+6\,{B_{1}}{ A_{3}}-3\,{A_{3}}B_{2}-6\,{B_{1}}{A_{2}}+3\,{A_{2}}B_{3}}{-4\,{A_{1}}{A_{2}}^{2}+3 \,{A_{1}}^{2}A_{2}+4\,{A_{1}}{A_{3}}^{2}-3\,{A_{1}}^{2}A_{3}-6\,{A_{2}}{A_{3}} ^{2}+6\,{A_{2}}^{2}A_{3}}.\]  
So, \(q_{1}^{\prime}(\psi_{5^{th}3 pVS}(x))=f^{\prime}(x)+2a_{1}(\psi_{5^{th}3 pVS} (x)-x)+3a_{2}(\psi_{5^{th}3 pVS}(x)-x)^{2}+4a_{3}(\psi_{5^{th}3 pVS}(x)-x)^{3}\). Using [PERSON]'s theorem with \(p=q=5\), we can obtain a four-point 10 th-order I.F.s (\(10^{th}\)4 pVS):  
\[\psi_{10^{th}4 pVS}(x)=\psi_{5^{th}3 pVS}(x)-G_{1}\times f(\psi_{5^{th}3 pVS}(x)),\quad G_{1}=\frac{1}{q_{1}^{\prime}(\psi_{5^{th}3 pVS}(x))} \tag{15}\]  
The efficiency index has now increased since \(IE_{10^{th}4 pVS}=10^{1/5}=1.585\) with \(2\) function and \(3\) first derivative evaluations. We point out that the optimal order of a four-point I.F. with \(5\) functional evaluations is \(16\) but this can be achieved with either \(5\) function evaluations or \(4\) function and \(1\) first derivative evaluations (see [5, 14] and the references therein). Using a similar approach to estimate \(f^{\prime}(\psi_{10^{th}4 pVS}(x))\) by the following polynomial:  
\[q_{2}(t)=f(x)+f^{\prime}(x)(t-x)+b_{1}(t-x)^{2}+b_{2}(t-x)^{3}+b_{3}(t-x)^{4}+b _{4}(t-x)^{5} \tag{16}\]  
which satisfies the following conditions  
\[ \begin{split}& q_{2}(\psi_{5^{th}3 pVS}(x))=f(\psi_{5^{th}3 pVS}(x)),\,q_{2}(\psi_{10^{th}4 pVS}(x))=f(\psi_{10^{th}4 pVS}(x)),\\ & q_{2}^{\prime}(\psi_{2^{nd}NR}(x))=f^{\prime}(\psi_{2^{nd}NR}(x) ),\,q_{2}^{\prime}\left(\frac{x+\psi_{2^{nd}NR}(x)}{2}\right)=f^{\prime}\left( \frac{x+\psi_{2^{nd}NR}(x)}{2}\right).\end{split}  \tag{17}\]  
Furthermore, let \(A_{4}=\psi_{10^{th}3 pVS}(x)-x\), \(B_{4}=f[\psi_{10^{th}4 pVS}(x),x,x]\). Equation (17) reduces, after using the divided differences, to a system of 4 linear equations in matrix form:  
\[\left( \begin{array}{cccc}1&A_{1}&{A_{1}}^{2}&{A_{1}}^{3}\\ 2&3A_{2}&4{A_{2}}^{2}&5{A_{2}}^{3}\\ 2&3A_{3}&4{A_{3}}^{2}&5{A_{3}}^{3}\\ 1&A_{4}&{A_{4}}^{2}&{A_{4}}^{3}\end{array} \right)\,\left( \begin{array}{c}b_{1 }\\ b_{2}\\ b_{3}\\ b_{4}\end{array} \right)=\left( \begin{array}{c}B_{1}\\ B_{2}\\ B_{3}\\ B_{4}\end{array}
# Fragile Watermarking of 3D Models in a Transformed Domain
## 5 The Proposed Algorithm
### Watermark Embedding  
The watermark string \(w\) to be embedded into an EU is derived by the key generator module from the fixed part of the EU itself, i.e., the diagonal striped bytes in Figure 2. This string can be computed as the MD5 hash of the aforementioned data: from the resulting 128 bits, a predefined subset is extracted for embedding. Note that this step is not strictly necessary because the watermark is embedded into a secret space, as will be discussed in the following; thus, also a constant \(w\) bit string would suffice; nonetheless, a variable \(w\) further improves security.  
A high level description of the algorithm is the following.  
The fragile watermark is stored in the coefficients of every EU KLT: the 28 bytes of the EU are considered as 28 non-negative integer pixel values, which are KLT transformed, producing 28 coefficients. If \(m\) is the payload in bpv (bits-per-vertex), i.e., \(m\) is the length of the watermark \(w\) for every EU, then a subset of \(m\) KLT coefficients is chosen a priori to store the watermark bits. In the present implementation, a coefficient \(c\) is considered as carrying a watermark bit \(b\) in position \(p\) (the \(p\) value is a parameter of the algorithm) iff:  
\[b=\text{round}(2^{-p}c)\text{ mod }2 \tag{7}\]  
Figure 2: The fields and structure of an Embedding Unit (EU).  
(see [24] for a deeper discussion on various methods for embedding the watermark bits into a set of coefficients).  
The GA alters the values of the vertical striped bytes in Figure 2 until the watermark is embedded into the EU KLT coefficients, i.e., if \(\mathbf{g}=(g_{1},g_{2},\ldots,g_{28})\) is the EU, the GA alters \(g_{4},g_{8},g_{12}\) (the vertical striped bytes) so that the predefined coefficients in \((c_{1},c_{2},\ldots,c_{28})=\text{KLT}(g_{1},g_{2},\ldots,g_{28})\) contain the respective \(m\) bits watermark. The reason for using a GA in this task is its ability to find the solution of a non-linear problem (bytes representing integer values) looking for the (local) minima of the error w.r.t. the original data.  
Referring to Equation (7), the present implementation of the algorithm considers the first \(m\) coefficients among the 28 transform coefficients and embeds into the bit in position \(p=-2\).  
When all the EUs have been processed by the GA, the 3D model with the new modified coordinates contains the fragile watermark allowing for the integrity check by the modules presented in the following section.
# Optimal Transport in Multilayer Networks for Traffic Flow Optimization
## 2 Materials and Methods
### The Model
Interpreting the conductivities as quantities proportional to the size of an edge, this dynamics enforces a feedback mechanism such that the edge size increases if the flux through that edge increases, it decreases otherwise. This feedback mechanism was observed in biological networks, such as the one made by slime mold _Physarum polycephalum_[24; 44], which adapts its body shape to optimally navigate the space, searching for food. The important property of this dynamics is that its stationary solutions minimize a multilayer transport cost function:  
\[J_{\beta}=\sum_{\alpha=1}^{L+1}\sum_{e\in\mathcal{E}_{\alpha}}\ell_{e}||F_{e} ||_{2}^{\Gamma(\beta_{e})}\quad, \tag{4}\]  
where \(\Gamma(\beta_{e})=2(2-\beta_{\alpha})/(3-\beta_{\alpha})\) for all \(\alpha\) and the 2-norm is calculated over the \(M\) entries of each \(F_{e}\). This means that solving the systems of Equations (1)-(3) is equivalent to finding the optimal trajectories of passengers in a multilayer network, where optimality is given with respect to the cost in Equation (4). An extended discussion and a formal derivation of this property can be found in [32; 40]. The parameter \(\beta_{q_{e}}\) (taking value \(\beta_{\alpha}\) on layer \(\alpha\)) regulates how the fluxes should distribute in each of the layers. In fact, according to Equation (4), when \(\beta_{\alpha}>1\), the fluxes are encouraged to consolidate into few edges of a layer \(\alpha\), being \(\Gamma(\beta_{\alpha})<1\), and thus the cost in Equation (4) is sub-linear. In the opposite scenario, when \(0<\beta_{\alpha}<1\), we have that the fluxes are encouraged to distribute over more edges and with lower values in order to keep traffic congestion low. Finally, when \(\beta_{\alpha}=1\), we obtain the shortest path-like minimization. The consequence of having different \(\beta_{\alpha}\) in different layers is that the optimal trajectories have different topologies in each of the layers. At the same time, layers are coupled together, thus the final trajectories are a complex combination of the weights \(w_{\alpha}\) and the \(\beta_{\alpha}\). We give an example of optimal flows for various combinations of these parameters in Figure 2.
# A Platform of Unmanned Surface Vehicle Swarms for Real Time Monitoring in Aquaculture Environments
## 6 Data Acquisition and Analysis
### Automatic Indirect Data Acquisition  
To visualize the data, the user can use a dashboard where a map will be presented with various markers on it (Figure 12). Each marker will represent a location where the USVs have gathered data. By clicking on each USV, the most recent values of each sensor reading will appear below the map. Alternatively, it also shows graphs depicting the change of a particular sensor over time.  
The user will be able to define new missions for the USVs using the dashboard. In the route definition page (Figure 12(a)), the user is presented with a map where he can click to define a way-point where the team should make readings. In this menu, the user also has to define a number and a name for the mission, which will later be used when he wants to start or stop it. To start a mission, the user should head to the _mission select_ page (Figure 12(b)), where he can select it from a list of all routes available and specify a time and date for the mission to occur.To cancel the mission, the user should head to the _mission information_ page (Figure 12(c)) and eliminate the mission to cancel.  
Figure 12: Dashboard presenting the information.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 3 Methods
### Data Sources and Data Fusion Assessment  
Landsat and MODIS are the major data sources for the most data fusion models and applications. Three MODIS NBAR products (Aqua NBAR, Terra NBAR, and Terra/Aqua combined NBAR as listed in Table 2) and two Landsat surface reflectance products (Landsat 7 ETM+ and Landsat 8 OLI) were used in data fusion. In total there are six different combinations between MODIS and Landsat data sources, including (1) Aqua NBAR and Landsat 7 (L7), (2) Aqua NBAR and Landsat 8 (L8), (3) Terra NBAR and L7, (4) Terra NBAR and L8, (5) Terra/Aqua combined NBAR and L7, and (6) Terra/Aqua combined NBAR and L8. Since Landsat and MODIS data products have different characteristics, the consistency of the image pairs between MODIS and Landsat needs to be examined. In this study, the root mean square errors (RMSE) and correlation coefficients (R) between MODIS and Landsat data products for six combinations were computed and the temporally-averaged RMSE (<RMSE>) and R (<R>) were used to analyze the consistency of the different Landsat-MODIS combinations (Section 4.1).  
These six combinations of data sources were then used to generate six sets of data fusion results for an entire year at 30m spatial resolution. The fusion results were evaluated using the actual Landsat observations. The temporally averaged MAD (<MAD>) for NDVI through the year for different Landsat-MODIS combinations was computed to evaluate the effects of different data sources (Section 4.2).  
To evaluate the effects of pair date and land cover type on fusion predictability, the simulated MODIS data aggregated from Landsat data (see Section 2.3) were used in place of actual MODIS images. In this way, the Landsat and simulated MODIS data are completely consistent and we can ignore issues related to the view angle and other sensor inconsistencies and focus on the effects of the pair date. Daily surface reflectance images were generated for an entire year based on one pair. The MAD between the observed Landsat NDVI and the fused results at each Landsat overpass date was computed and evaluated for five land cover types (corn, soybean, deciduous forest, evergreen forest, and mixed forest) (Section 4.3). Annually averaged MAD (<MAD>) and R (<R>) values for five land covers were calculated separately and used to assess data fusion accuracy as a function of landcover type for the two study sites. Results are presented and analyzed in Sections 4.3 and 4.4.  
Finally, we evaluate strategies for selecting optimal input image pair dates, assuming the one-pair image strategy in STARFM. To evaluate the effect of pair selection strategy and the predictability of different combinations of pair dates, several pair combinations with different dates were tested and each pair selection strategy (MC and ND) was applied separately to determine the pivot dates for each pair. MAD and <MAD> were used to evaluate the pair selection strategy (Section 4.5).