esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 ATON Framework
### Hathor
This is particularly useful during collaborative sessions that do not intend to alter the original 3D scene setup, but rather show other participants temporary modifications (e.g., showing/hiding a layer, adding temporary annotations or measurements, etc.) for various purposes. Hathor fully exploits the _collaborative_ components of the ATON framework (see Section 3.7). A single button allows the user to switch between \"single\" and \"collaborative\" session, joining a specific session associated with the current scene ID. A basic chat panel is provided between participants, and it is possible to talk in real-time with others through the built-in microphone of the device (mobile, desktop PC, HMD) similarly to _Mozilla Hubs_. This is possible through a WebRTC library that allows to stream audio, video or screen activity as well--and broadcast them to other users in the scene. During a collaborative session, scene modifications are broadcast to participants: environment settings, lighting changes, semantic annotations, measurements, etc. are all synchronized in real-time, thanks to scene patches (see Section 3.5). It is thus possible to collaboratively enrich the scene with semantic annotations, measurements, as well as switch layers, testing different lighting setups and much more. A particularly useful tool for collaborative CH sessions offered by Hathor is the _focus streaming_. It is possible for a participant to broadcast their focal point in the form of a pulsating visual 3D indicator on the location where he/she intends to raise attention (see Figure 15). As highlighted by different experiments, these features enable rich collaborative experiences, for instance discussing a specific 3D scene or reconstruction hypothesis, directly online, with every device and without any installation required for participants. Figure 14: Sample captures from Hathor front-end. (**A**) sample scene presentation and basic UI; (**B**) layer switching; (**C**) multiple measurements added by the user; (**D**) HTML5 built-in editor and vocal notes for semantic annotations; (**E**) user-created rich HTML5 content; (**F**) semantic shapes export; (**G**) sharing options; (**H**) environment and lighting settings; (**I**) viewpoint options. Figure 15: Sample collaborative sessions in Hathor with 3 users (red, yellow and green). (**A**) User 0 (red, left view) is streaming its focus to other participants, right is yellow user view. (**B**) User 1 (yellow) is streaming its focus, and changed lighting settings at runtime (red view left, yellow view right). (**C**,**D**) All three users perform annotations and measurement tasks at different scales.
# Balloon Design for Mars, Venus, and Titan Atmospheres
## Nomenclature  
**Latin Letters**  
\(A\)surface area of balloon/spheroid  
\(a\) equatorial radius of a spheroid  
\(A_{\rm e}\)effective area of balloon (\(0.5(A_{xy}+A_{xz})\))  
\(a_{\rm s}\) albedo  
\(a_{x}\) and \(a_{y}\)acceleration of balloon in \(x\) and \(y\) directions  
\(A_{xy}\)top area of balloon (projected onto \(x-y\) plane)  
\(A_{xz}\)area of balloon as projected onto \(x-z\) plane  
\(a_{\rm z}\)vertical acceleration of balloon  
\(b\)one of the three radii of an ellipsoid, see Equation (3)  
\(c\)distance from centre to pole along the axis of symmetry of a spheroid  
\(C_{\rm D}\)drag coefficient  
\(c_{\rm e}\)specific heat capacity of balloon envelope  
\(C_{\rm mass}\)virtual mass coefficient  
\(c_{\rm pg}\)specific heat capacity of the balloon lifting gas  
\(D_{\rm b}\)balloon diameter  
\(D_{\rm x}\)and \(D_{y}\)aerodynamic drag in \(x\) and \(y\) directions caused by horizontal winds  
\(D_{\rm z}\)drag experienced by balloon  
\(F\)free lift factor  
\(f\)fineness ratio (ratio of \(a\) to \(c\))  
\(F_{\rm bs}\)shape factor from the balloon to the planet  
\(g\)planetary acceleration of gravity  
\(G\)foss inflation (net buoyant force)  
\(h_{\rm ae}\)convective heat transfer coefficients between the envelope and atmosphere  
\(h_{\rm ge}\)convective heat transfer coefficients between the lifting gas and envelope  
\(l_{0}\)solar constant (solar irradiance per unit area)  
\(k_{\rm a}\)conductivity of atmospheric air  
\(k_{\rm g}\)lifting gas conductivity  
\(M_{\rm a}\)  
molar mass of air  
\(m_{\rm b}\)ballast mass  
\(m_{\rm e}\)mass of balloon envelope  
\(M_{\rm g}\)  
molar mass of the lifting gas  
\(m_{\rm g}\)mass of lifting gas  
\(M_{\rm gross}\)cross mass (\(M_{\rm t}-m_{\rm g}\))  
\(m_{\rm p}\)payload mass  
\(M_{\rm t}\)total mass of balloon  
\(m_{\rm v}\)virtual mass (accounting for mass of air dragged along the balloon)  
\({\rm Nu_{a}}\)Nusselt number for the atmosphere  
\({\rm Nu_{g}}\)Nusselt number for the lifting gas  
\(P_{\rm a}\)atmospheric pressure  
\(Pr_{\rm g}\)lifting gas Prandtl number  
\(Q_{\rm conv_{\rm rest}}\)convective heat exchange with the external air  
\(Q_{\rm conv_{\rm rest}}\)convective heat exchange with the internal gas  
\(Q_{\rm e}\)total heat flux exchanged with the balloon envelope  
\(Q_{\rm g}\)heat exchanged with the balloon lifting gas  
\(Q_{\rm IR_{\rm peak}}\)net infrared heat flux between the gas inside the balloon and the envelope  
\(Q_{\rm IR_{\rm planet}}\)net infrared heat flux from the planet to the balloon  
\(Q_{\rm sum+albedo}\)direct solar heat and solar albedo  
\(R_{\rm a}\)specific gas constant of the atmospheric air  
\({\rm Re}\)Reynolds number  
\(r_{\rm e}\)solar reflectivity of the balloon envelope  
\(r_{\rm ei}\)infrared reflectivity of the balloon envelope
# A Primer on CFD-DEM for Polymer-Filled Suspensions
###### Abstract  
This work reports on an evaluation of the computational fluid dynamics-discrete element method (CFD-DEM) numerical approach to study the behavior of polymer-filled suspensions in a parallel-plate rheometer. For this purpose, an open-source CFD-DEM solver is used to model the behavior of such suspensions considering different particle volume fractions and different types of fluid rheology. We first validate the numerical approach for the single-phase flow of the continuum phase (fluid phase) by comparing the fluid's azimuthal velocity and shear stress components obtained from the open-source solver against the analytical expressions given in cylindrical coordinates. In addition, we compare the numerical torque given by the numerical procedure with analytical expressions obtained for Newtonian and power law fluids. For both cases, there is a remarkable agreement between the numerical and analytical results. Subsequently, we investigated the effects of the particle volume fraction on the rheology of the suspension. The numerical results agree well with the experimentally measured ones and show a yield stress phenomenon with the increase of the particle volume fraction.  
OpenFOAM; LIGGGHTS; CFD-DEM; multiphase flows; suspensions Article
## 2 Main Technical Indexes  
The FY-3 satellite operates in a sun-synchronous orbit of 830 km, which is located at the edge of the inner radiation belt. When the space environment is in a quiet period, the particles in this orbit are distributed in the negative magnetic anomaly region of the South Atlantic Ocean. When the space environment is disturbed, there will also be particle distribution in the high latitudes, and the particles including mainly protons and electrons [20; 21; 22; 23; 24].  
The main detection objects of the HEPD are the high-energy particles in the radiation belt. The detection content is the energy spectrum and flux of high-energy protons and electrons in three orthogonal directions of the satellite, direction of satellite retreating \(-X\), direction towards the sky +Y, and direction of perpendicular to the orbital plane \(-Z\). By detecting the energy spectrum and flux of charged particles, their time-varying spatial distribution can be obtained, providing information on the motion-low of high-energy particles. This can provide detection data for the space environment security monitoring and system of China, and also provide analysis data for satellite flight control management and abnormal situation analysis.  
The main technical indexes of the HEPD on the FY-3E satellite are shown in Table 1.  
In this table, the energy range represents the energy of incident particles that can be measured by the instrument. The detection field represents the field of view of incident particles that the instrument can accept. In addition, sensitivity refers to the minimum space environment change that can cause a one-count change in it.
## 5 Result and Discussion  
### Instance Validation Conditions  
In this section, the HO-NSGAII algorithm developed in Section 4 will be applied to address the shipboard material location-routing problem within a standard deck area. Due to the limitations of similar heuristic algorithms in handling obstacle blocks, the experimental outcomes lack reference significance. Therefore, this study exclusively contrasts the HO-NSGAII algorithm's performance with the traditional NSGAII algorithm, which does not consider obstacle blocks, to evaluate the efficacy of the former.  
The study was carried out within the construction setting of the fifth and sixth primary vertical zones on the sixth deck of a sizable cruise ship primarily designated for passenger cabins. The designated area spans 100 m in length and 35 m in width. In the process of cabin assembly, materials are first transported to a temporary storage location on the ship before being conveyed to the construction site of each cabin. Within this area, there are a total of four common obstacles, comprising twenty-five cabin waiting points and five alternative distribution centers throughout the construction phase.  
Two algorithms were employed to determine the optimal delivery route and the number of vehicles needed within the standard deck area. The comparison of algorithms involved the following parameter settings: the population size for the HO-NSGAII and NSGAII algorithms was set to popsize = 400, with a maximum iteration number of genmax = 500, a crossover probability of crossp = 0.9, and a mutation probability of mutp = 0.05.  
All experiments were run on a computer with an AMD Ryzen5 4600H 3.00 GHz processor, 16 GB RAM.
## 2 Materials and Methods  
### Experimental Design  
Ten single-rooted teeth extracted for periodontal reasons were selected. The teeth were subjected to two-dimensional radiography (RVG CS 6200, Carestream Health, Inc., Rochester, NY, USA) to verify the presence of a single root canal. Two teeth were excluded due to the presence of two canals.  
The selected teeth were decrowned at 17 mm from the anatomical apex, and canal patency was confirmed with a size 10 K-file. Following a protocol described in the literature to simulate a closed-end canal system, the middle and apical thirds of the roots were coated with adhesive (Universal Tray Adhesive, Zhernack SpA, Badia Polesine, Italy) [17]. Subsequently, each sample was placed inside a transparent Plexiglas tube filled with polyvinylsiloxane (Affinis Putty Soft, Coltene/Whaledent, Altstatten, Switzerland) [19].  
Each root was shaped at the working length (17 mm) using the Protaper Next system ([PERSON], [PERSON], Ballaigues, Switzerland) up to the X3 file (30/07). After each instrument (X1, X2, and X3), the canal was irrigated with 1 mL of 5% NaOCl using a 30 G needle positioned 1 mm from the working length. NaOCL was delivered at the rate of 1 mL/30\". After the shaping phase, irrigation with 5 mL of 17% EDTA was performed using a 30 G needle positioned 1 mm from the working length. EDTA was delivered at the rate of 5 mL/min. Each canal was subsequently irrigated with 5 mL of deionized water and dried with paper points, at the rate of 5 mL/min. Then, the canals were irrigated with a solution made of contrast medium (8 M cesium chloride solution) mixed in equal proportions with 5% sodium hypochlorite using a 30 G needle positioned 1 mm from the working length [31].  
Each sample was analyzed with a CBCT scanner (Planmeca ProMax 3D Mid, Planmeca, Helsinki, Finland) to detect the presence of vapor lock. Two researchers from Messina University independently conducted the same analysis, and in cases of discrepancies in the results, a third senior researcher was consulted.  
The presence and volume of bubbles in the coronal, middle, and apical thirds of each canal were quantified by evaluating the CBCT scans (Figures 1 and 2).  
The volume of each individual spherical bubble was calculated using the formula for calculating the volume of a sphere:  
\[V=\frac{4}{3}\pi r^{3}\]  
The volume of the canal and the volume of the gas columns were calculated using the formula for calculating the volume of a truncated cone:  
\[V=\frac{\text{A}b+Ab^{\prime}+\sqrt{\text{A}\text{b}\times\text{A}\text{b}^{ \prime}}}{3}\]
## 4 Experiments
### Experiments on the Performance of Descriptors  
#### 4.2.1 Dataset  
We chose four source point clouds from the Stanford 3D Scanning Repository for these experiments to evaluate the performance of our proposed feature descriptor in these experiments. Our target point clouds are created by superimposing Gaussian noise on our source point clouds (at three distinct levels of Gaussian noise, with standard deviations of 0.1 mr, 0.3 mr, and 0.5 mr).
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 3 Results
### Results Analysis  
In this section, the verification of the vector loop is divided into two primary aspects. One is the testing with the simulated signal generated by a self-designed signal generator, which is used to inspect the dynamic performance of the loop. The other is illustrating the completeness of the entire structure in highly dynamic environments with the signal of the navigation signal simulator, and the convergence performance of the vector loop is verified by these signals. Finally, the positioning results of actual data and simulator data are added as supplementary verification results.
## 5 Plant Growth-Promoting Metabolites for Soil Remediation and Crop Improvement
### Phosphate Solubilization  
Phosphorus is one of the most important elements for plant nutrition. In agriculture, it is generally compensated through the addition of chemical fertilizers to soil. However, phosphorus coming from such fertilizers is rapidly immobilized, becoming useless for plants [146]. In addition, the high release of contaminants into the main product, gas steam and by-products, but also toxic metal accumulation in both the soil and crop due to the repetitive use of phosphoric fertilizers, has obliged producers to look for better tools to reduce the use of such chemical fertilizers [147; 148]. Among these alternatives, the use of phosphate-solubilizing bacteria (PSB) is one of the most ecofriendly options to avoid or to minimize the exaggerated use of chemicals [149].  
[PERSON] [150] reviewed the role of metal phytoremediation in association with PSB and reported that such associations considerably overcome the practical drawbacks imposed by metal stress on plants. Furthermore, [PERSON] and [PERSON] [151] isolated and selected a group of PSB with a high ability to tolerate toxic metal stress. They suggested that using toxic metal PSB in metal-contaminated areas might be exploited for bioremediation studies. Otherwise, [PERSON] and [PERSON] [152] found that inoculation with the two phosphate-solubilizing bacteria _Pantoea cypripedii_ (PSB-3) and _Pseudomonas plecogloscida_ (PSB-5), together with rock phosphate fertilization, increased shoot height, shoot and root dry biomass, grain yield and total phosphorus uptake in both maize and wheat as compared to the control. The application of phosphate-solubilizing _Azobacter_ strains, alone or together with chemical fertilizers, improved the yield and root biomass of three wheat varieties under greenhouse conditions [153].  
Several works have shown the efficiency of PSB such as _Pantoea_, _Azobacter_, _Rhizobium_, _Pseudomonas_ and _Serratia_ in nitrogen uptake and wheat growth enhancement under different stress conditions [154; 155; 156; 157]. The in vitro experiment, conducted by [PERSON] et al. [158], to study the influence of two phosphate-solubilizing _Bacillus_ spp. (PSB9 and PSB16), together with triple supper phosphate on aerobic rice growth, showed that the coupled \"bacteria-triple supper phosphate\" increased phosphate uptake, available soil phosphate and rice growth. The two phosphate-solubilizing bacteria PSB 12 identified as _Gluconacetobacter_ sp. (MTCC 8368) and PSB 73 identified as _Burkholderia_ sp. (MTCC 8369) were examined for their potential ability to enhance rice growth. They revealed high growth promotion ability under pot culture assays and were presumed to be of potential to develop as biofertilizers [159]. [PERSON] et al. [160] and [PERSON] et al. [158] have also discussed the role of some PSB in improving phosphate uptake, soluble soil phosphate and rice growth.
## 3 Results and Discussion  
### Heavy Metal Elements of Phosphogypsum Samples  
Table 3 shows the contents of some heavy metal elements in phosphogypsum samples before and after microbial modification. The contents of Be\({}^{2+}\) and Zn\({}^{2+}\) in the original phosphogypsum sample exceed the standard critical value, but they meet the emission standards after microbial treatment. In the process of microbial modification, the ion states of the heavy metals in phosphogypsum are extracted and fixed by combining them with carbonate through microbial complexation, which changes the chemical form of the heavy metal elements. The movement of harmful elements is reduced so as not to damage the ecological environment [53].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Untreated Phosphogypsum** & **Microbially Treated Phosphogypsum** & **Critical Value** \\ \hline Se & \textless{}0.01 & \textless{}0.01 & 0.01 \\ Cd & \textless{}0.003 & \textless{}0.003 & 0.003 \\ Pb & \textless{}0.05 & \textless{}0.05 & 0.05 \\ Ni & \textless{}0.01 & \textless{}0.01 & 0.01 \\ Hg & \textless{}0.01 & \textless{}0.01 & 0.01 \\ As & \textless{}0.1 & \textless{}0.01 & 0.01 \\ Cr & \textless{}0.01 & \textless{}0.01 & 0.01 \\ Be & 0.045 & \textless{}0.005 & 0.005 \\ Ba & \textless{}0.004 & \textless{}0.004 & 0.004 \\ Ag & \textless{}0.1 & \textless{}0.1 & 0.1 \\ Cu & \textless{}0.01 & \textless{}0.01 & 0.01 \\ Zn & 0.181 & \textless{}0.006 & 0.006 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Content of heavy metal elements (mg/L) in different phosphogypsum samples.
# Handover Management for D2D Communication in 5G Networks
## 4 Proposed Approach  
Our objective is to reduce the unnecessary handovers caused by the possible ping-pong effect while keeping both users of the same D2D pair connected to the same eNB. Our proposed method consists of two phases--(1) choosing the appropriate target eNB as the potential handover target for each pair of D2D users and (2) deciding whether or not to hand over to the target eNB.
# Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming +
## 1 Introduction
In this work, we try to provide an answer to the following research questions:  
**RQ1**: _Can we easily obtain balanced solutions?_
**RQ2**: _Can we leverage a GP-based evolution to discover solutions with higher non-linearity than random search?_
**RQ3**: _What are the implications of changing the tree syntactical structure as regards our fitness function?_  
We test and compare different combinations of function sets and terminal sets for the structure of the Walsh-transform-based tree. Additionally, we analyze the trend of the percentage of uncertain positions when computing the nearest Boolean function. We provide a review of all these comparisons to highlight the best type of setting for a Walsh-transform-based search-space exploration. Results show that by leveraging GP to evolve Walsh transforms, it is possible to outperform random search, meaning that the evolutionary process and the type of chosen representation are both effective at providing a high-quality exploration strategy, which, consequently, deserves further research effort. Furthermore, the discovered solutions appear also to be balanced thanks to the balancedness-preserving strategy adopted during the nearest Boolean function computation. We detail the main contributions of this manuscript:  
1. We show how we can evolve Walsh Transforms with GP to maximize non-linearity by using spectral inversion. 2. We show how we can discover balanced solutions with an effective balancedness-preserving strategy when computing the nearest Boolean functions from the corresponding pseudo-Boolean ones. 3. We demonstrate that, even with a small population size and with a low number of generations, GP is able to outperform RS if we choose the correct function set and terminal set. 4. We provide an analysis for explaining why certain types of function and terminal set combinations perform poorly by inspecting the percentages of uncertain positions. In Section 2, we analyze the current literature regarding optimization of Boolean functions for building secure stream ciphers. In Section 3, we describe the application domain by analyzing stream ciphers and Pseudo-Random Generators (PRG) in Section 3.1 and Boolean function representations in Section 3.2. In Section 4, we describe our proposed evolutionary-based approach. In Section 5, we describe our experimental phase and present the results. In Section 6, we inspect the obtained results and provide an answer to the research questions.
## 1 Introduction
### Topology Optimization  
In this work, only the case of mono-material topology optimization is considered. The material of which the structure is to be build is a constant of the problem, and the geometry remains unknown.  
The function to be minimized in stiffness optimization is usually the scalar measure of structural compliance. In addition, the material quantity condition must be fulfilled. This material quantity corresponds to the fraction of the maximum possible amount of material (degree of filling) and is often also referred to as the volume fraction. The minimization of compliance results in maximizing the stiffness [2; 3]. Typically considered restrictions are the available design domain, the static and kinematic boundary conditions for the regarded load cases as well as strength thresholds.  
There are numerous possible approaches to TO [1]. According to the \"Solid Isotropic Material with Penalization\" (SIMP) approach of [PERSON] [2], a subdivision of the design domain into elements takes place. A factor, yet to be determined, scales the contribution of each element to the overall stiffness of the structure.  
The SIMP approach is able to provide optimized geometries for many practical cases by means of an iterative process. Each iteration involves computationally intensive operations: the most critical ones are assembling the stiffness matrix and solving the system's equation. The involvement of restrictions, such as stress restrictions, increases the complexity of the optimization problem [4; 5].