esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Model Development
### Properties and Boundary Conditions of the COS Hydrolysis Reactor  
Properties of the individual gaseous constituents in the reactant gas mixture are systematically listed in Table 2 for accurate and comprehensive numerical modeling of the COS hydrolysis reactor, mainly citing NIST(National Institute of Standard and Technology) data.  
Inter-diffusion coefficients of major gaseous constituents were calculated using Equation (12) from Perry's Chemical Engineer's Handbook [18].  
Binary-Diffusivity  
\[D_{12}=\frac{0.01013T^{1.75}\Bigl{(}\frac{1}{M_{1}}+\frac{1}{M_{2}}\Bigr{)}^{0.5}}{P\Bigl{[}\bigl{(}\sum\
u_{1}\bigr{)}^{1/3}+\bigl{(}\sum\
u_{2}\bigr{)}^{1/ 3}\Bigr{]}^{2}} \tag{12}\]  
The binary diffusivity equation predicts inter-diffusion coefficients using molecular weight and structure at a specific temperature and pressure, and the calculated inter-diffusion coefficients are accurate within \(\pm 5\%\).  
The present numerical modeling necessitates operational parameters to predict performance of the COS hydrolysis reactor. The present modeling was based on ordinary boundary conditions with upper and lower limits suitable for proper evaluation of the COS hydrolysis reactor with variation of operational parameters.  
Table 3 summarizes pertinent boundary conditions for the COS hydrolysis reactor. At first, objective hourly capacity of 200 Nm\({}^{3}\)/h was converted to velocity, which was varied to estimate its effect on reactor performance. Inlet gas temperature and chemical composition were specified considering the overall Integrated Gasification Fuel Cell (IGFC) process gas temperature and composition; the gas temperature was also carefully cross-checked with variations of the internal heater temperature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Description** & \begin{tabular}{c} **Density** \\ **[kg/m\({}^{3}\)]** \\ \end{tabular} & \begin{tabular}{c} **Viscosity** \\ **[Pa-s]** \\ \end{tabular} & \
\begin{tabular}{c} **Specific Heat** \\ **[J/kg-K]** \\ \end{tabular} &
\begin{tabular}{c} **Thermal Conductivity** \\ **[W/m-K]** \\ \end{tabular} \\ \hline \(\mathrm{H_{2}}\) & 0.91 & \(1.22\times 10^{-5}\) & 14,522 & 0.269 \\ \(\mathrm{CO}\) & 13.79 & \(2.49\times 10^{-5}\) & 1071 & 0.038 \\ \(\mathrm{H_{2}S}\) & 17.48 & \(2.06\times 10^{-5}\) & 1137 & 0.031 \\ COS & 31.40 & \(2.30\times 10^{-5}\) & 850 & 0.032 \\ \(\mathrm{N_{2}}\) & 13.78 & \(2.53\times 10^{-5}\) & 1063 & 0.037 \\ \(\mathrm{H_{2}O}\) & 9.80 & \(1.61\times 10^{-5}\) & 3169 & 0.042 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of working fluid at 473.15 K and 1950 kPa.
## 2 Numerical Model
### Model Setup  
Different physical processes dominate in the two functional units, the reactive bed and the heat transfer channel. Thus, we split the reactor into two separate domains, (i) the free flow channel (HTF channel) with the air flux and (ii) the porous-medium domain with the reactive bulk and internal water-vapor fluxes. Both domains are coupled by conductive heat flow. Due to symmetry, the reaction domain is reduced to the reaction bed above the heat transfer channel according to Reference [20]. The simulation setup is displayed in Figure 1 on the top right. The boundary effects of the third dimension are assumed to be negligibly small, so that the numerical model represents the experiment in two dimensions. The numerical model is based on the following simplifying assumptions:  
* The air density in the HTF channel is a function of temperature only (the influence of pressure variation on the density is negligible).
* Gas flow in the HTF channel is laminar with a fully developed viscous boundary layer.
* Heat conduction in the metal casing is much larger than in the HTF channel and the porous bulk. The casing is thus not considered in the model.
* There is local thermal equilibrium in the reactive bulk.
* Gas flow in the reactive bulk is creeping.  
The following sections contain the description of the two subdomain models, their coupling and the description of the heat losses.
## 5 Evaluation
### Impacts of Experimental Parameters
#### 5.6.2 Impact of Testing Area Size  
The testing area size \(A\) in previous experiments was always 51.84 m\({}^{2}\). Instead, we set it to \(A=51.84/92.16/144.00\) and investigated its impact on the system's performance. We kept \(C=144\) and \(\sigma=0\).  
Figure 18 depicts the performances of the oracle/baseline cases and our solution. The performances of the oracle and baseline cases almost remained the same as \(A\) increased, whereas the location-estimation error of our solution grew faster than the reference cases. However, this phenomenon is reasonable because our solution needs to perform adversarial learning on more testing points. This means that it becomes harder for the feature extractor to obtain codeword-independent representations as the number of fingerprints grows. Both the oracle and baseline cases perform no adversarial learning; hence, their performance remained nearly unchanged. Nevertheless, our solution still achieved significantly better results than the baseline case and is closer to the oracle case. Therefore, we conclude that our solution is robust against changes in the testing area size. All corresponding numerical results are presented in Table 4.
# Electromagnetic and Thermal Phenomena Modeling of Electrical Discharges in Liquids
## 2 Discharge Models in Liquids
### Direct Impact Ionization
#### 2.1.4 Electrostriction Mechanism  
The electrostriction mechanism is used to describe discharges characterized by short duration. In the vicinity of electrodes, an inhomogeneous distribution of the high intensity electric field is created. Under conditions of significant gradients of the electric field intensity, in poorly conducting liquids, there are stresses causing deformations and discontinuities of the liquid [37; 38]. In these areas, micropores filled with gas are formed, featured by a characteristic extension toward the electric fields force lines. In these structures, the free electrons can obtain energy values that enable the initiation of the collision ionization process [16; 38; 39]. The forces affecting the liquid as a result of the electrostriction phenomenon are described by the formula (1) for non-polar and (2) for polar liquids [16; 38].  
\[F\approx\frac{\varepsilon_{0}}{2}\frac{(\varepsilon-1)(\varepsilon+2)}{3} \
abla E^{2}, \tag{1}\]  
\[F\approx\frac{\alpha\varepsilon\varepsilon_{0}}{2}\
abla E^{2}, \tag{2}\]  
where: \(\varepsilon_{0}\)--vacuum dielectric permittivity; \(\varepsilon\)--dielectric permittivity of the medium in which the discharge occurs; \(\alpha\)--a coefficient determined empirically for polar liquids  
The analysis of the given equations shows that the electrostriction mechanism is intensified in the conditions of fast-changing electromagnetic fields with significant gradients. Therefore, this process can be used to describe discharges of nanosecond duration, in the vicinity of pierce electrodes [39; 40]. For longer discharges, the hydrostatic pressure reduces the formation of micropores [40].
# Study on Relay Contact Bounce Based on the Adaptive Weight Rotation Template Matching Algorithm
## 4 Contact Bounce Analysis
### Analysis of Contact Bounce Process
Specifically, at \(t_{1}=1.667\) s, the moving contact and the stationary contact separate, and at \(t_{2}=1.676\) s, they restore contact and continue to maintain the contact state. Therefore, the calculation of the contact bounce time for this group is obtained as follows:  
\[t_{ht}=t_{2}-t_{1}=1.676-1.667=0.009\text{ s}=9\text{ ms} \tag{27}\]  
During the contact bounce process, the maximum bounce distance, \(x_{m}\), between the moving contact and the stationary contact represents the bounce height, \(h\), of the contacts. Therefore, the bounce height of the contacts is as follows:  
\[h=x_{m}=d_{max}=0.0889\text{ mm} \tag{28}\]  
The time, \(t_{m}\), required for the contacts to bounce to the maximum distance can be obtained by analyzing the data:  
\[t_{m}=t_{h}-t_{1}=1.672-1.667=0.005\text{ s}=5\text{ ms} \tag{29}\] \begin{table}
\begin{tabular}{c c c c} \hline
**Time/s** & **r/mm** & **D/mm** & **d/mm** \\ \hline
1.6745 & 4.56 & 4.61795 & 0.05795 \\
1.675 & 4.56 & 4.60524 & 0.04524 \\
1.6755 & 4.56 & 4.59257 & 0.03257 \\
1.676 & 4.56 & 4.58526 & 0.02526 \\
1.6765 & 4.56 & 4.56 & 0 \\... &... &... &... \\
1.6995 & 4.56 & 4.56 & 0 \\
1.7 & 4.56 & 4.56 & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 13: Curve of change in distance between moving contact and stationary contact during contact bounce. where \(t_{h}\) represents the moment when the moving contact and the stationary contact bounce to the maximum distance. The above analysis demonstrates that examining contact bounce through high-speed shooting and image processing enables the calculation of not only the relay's contact bounce time but also the contact bounce height and the time required for the contact to bounce to the maximum distance. This allows for a more comprehensive analysis of the entire contact bounce process.
## 3 Research Methodology
### Performance Metrics
#### 3.1.1 Throughput  
The rate of successful data transmission at the destination over a given time interval divided by the duration of the time interval is known as throughput [16]. Throughput has been measured by either packets per second (pps) or bits per second (bps). In this paper, bps is considered to measure the throughput. It indicates the volume of traffic an application generates when traversing the network. Mathematically, throughput can be expressed as follows:  
\[T=\frac{N_{b}}{T_{lst}} \tag{1}\]  
where \(T\) indicates the throughput, \(N_{b}\) is the total number of bits sent, and \(T_{lst}\) is the total data sending time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation area & 1000 m \(\times\) 10,000 m \\ Number of UAVs & 30 and 50 \\ Directional Gain & 10 dBi \\ Node type & Mobile \\ Mobility model & Random waypoint \\ Altitude & 200 m and 50 m \\ UAV max speed & 40 m /s and 30 m /s \\ Routing protocols & E-OLSR, OLSR, DSR, GRP and AODV \\ Physical characteristics & Extended rate PHY (802.11 g) \\ Data rate & 1 Mbps to 24 Mbps \\ Transmit power & 0.005 W \\ Simulation duration & 10 min \\ Simulation seed & 128 \\ IP addressing & Auto-assign IPv4 addressing \\ Packet interval & Exponential (1) s \\ Reception Power Threshold & \(-95\) dBm \\ Packet size & 1024 byte \\ Large packet processing & Drop (if bigger then 2304 bytes) \\ Buffer size & 256,000 bits \\ AP beacon interval & 0.02 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation design parameters in OPNET modeler.
## 6 Conclusions  
Light-1 is a 3U CubeSat designed to detect and monitor terrestrial gamma ray flashes. The CubeSat will detect TGF events at the ISS orbit at microsecond time granularity, allowing for an unprecedented definition of a TGF typical time structure. If successful, Light-1 will be a pioneering CubeSat for future constellations of small satellites targeting TGFs with extreme spatial and time resolution. The improved knowledge of TGF science will also be beneficial for commercial and military airlines, preventing dangerous and high intensity exposures. Orbital analysis conducted on the CubeSat shows that with the preferred horizontal orientation and an effective drag area of 0.01 m\({}^{2}\) as well as a lifetime of 2.2 years can be achieved, which is considerably greater than the required 6 months for the mission. With the three ground stations in Abu Dhabi, Vilnius, and Aalborg, a collective average access time of 37.14 min/day can be achieved, during which 50 MB of payload data and a generated housekeeping data of 1.04 MB/day can be duly transmitted to the ground stations. The telemetry analysis shows that the margin is closing, which enables good communication with the ground station. A significant mass margin is available for any design changes. Furthermore, the power budget shows a sufficient power profile that can accommodate additional power requirements for CubeSat functioning if required. The attitude determination and control analysis show satisfactory performance of the ADCS system that meets the mission requirements. Finite element analysis conducted shows that the CubeSat can withstand launch conditions and meets the minimum criteria for safety. Finally, the results of the thermal analysis concluded that Light-1 can withstand extreme temperature conditions, i.e., complete sunlight and complete eclipse. Light-1 is currently in its assembly stage and is expected to launch in late 2021 from the International Space Station.  
Conceptualization, F.J., H.A. and P.M.; investigation, B.A. ([PERSON]), H.I, P.D. and V.T.; project administration, F.J., H.A. and P.M.; supervision, A.K. ([PERSON]) and F.J.; writing--original draft, A.A. ([PERSON]), A.K. ([PERSON]), A.A. ([PERSON]), A.A. ([PERSON]), A.B., A.A.M., A.A. ([PERSON]), A.A. ([PERSON]), A.A. ([PERSON]), A.K. ([PERSON]), A.A. ([PERSON]), B.A. ([PERSON]), E.A., M.T.A., N.A., N.B., R.A. ([PERSON]), R.A. ([PERSON]), R.A.-h., S.A., S.B. and Y.A.; writing--review and editing, [PERSON] ([PERSON]) and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by the UAE Space Agency, grant number K11-2016-002.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Thermal Test Environments** & **Temperature Ranges (\({}^{\circ}\)C)** \\ \hline HTV & +5\(\rightarrow\)32 \\ \hline Dragon & +18.3\(\rightarrow\)29.4 \\ \hline Cygnus & +10\(\rightarrow\)46.1 \\ \hline Inside the ISS & +16.7\(\rightarrow\)29.4 \\ \hline Outside the ISS (when the satellite is inside the J-SSOD) & \(-\)15\(\rightarrow\)60 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Light-1 thermal vacuum test conditions [3].
## 2 Related Works
### Designing the Solution Dedicated to Blind Users  
The literature and research conducted so far have distinguished three important approaches relating to the design of solutions for the blind:  
* **Audio interfaces.** From the perspective of individuals with visual impairments, audio interaction with electronic devices is the most convenient [3; 23]. The accuracy of the descriptions is especially important in this context. In addition to accuracy, the authors of the relevant book [24] have described several distinguished features based on numerous expert interviews. Recommendations include personalizing the synthesizer voice and adjusting the length of messages.
* **Touch interfaces.** The great advantage of tactile interfaces is their naturalness. Individuals with visual impairments who have used Braille since childhood can quickly adapt to such interfaces. Tactile interfaces are commonly used in navigation systems for the blind [25; 26]. However, when dealing with complex objects or increasing details, it is desirable to combine tactile interfaces with another sense, such as hearing [27]. Many scientists have demonstrated that combining the sense of touch with signals such as force, vibration, and additional voice messages allows blind users to acquire a larger amount of information [28; 29].
* **Multimodal interfaces.** Combining the senses is assumed to be the most useful strategy when improving perception [30; 31]. Research conducted by various sources [10; 32] has proven this method to be the most effective for designing interfaces for users who are blind. In the study presented in [33], in which the sense of sight was replaced by a combination of two senses, improved results were demonstrated for both the sighted and blind participants.  
This article aims to present an alternative audio-tactile method of presenting structural information contained in mathematical drawings adapted to the needs of the blind. As a contribution and new element of the developed method, in relation to the well-known art, one can consider (i) the adaptation of the idea of feedback independently in touch and sound channels, (ii) the integration of different navigation modes into the adaptive way of drawing presentation, and (iii) the possibility of audio-tactile exploration of drawings consisting of several layers of objects.  
The structure of this article is organized as follows. Section 3 provides a detailed description of each step of the proposed solution. Section 4 includes the results of the evaluation of the tools developed for blind students. Finally, Section 5 contains the conclusions, summary, and directions for further development.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Sensor-Based Modality
#### 3.4.4 IoT-Based  
Since the term IoT was first used in the late 1990s by [PERSON] [176], exceptional research work has been published. Through IoT-based interactions, it is easier to provide humans with situational and context awareness, enhance decision-making in everyday tasks, and control any type of system and offer novel interactions to disabled individuals. [PERSON] et al. [177] used open-source web technologies, such as X3 DoM, to integrate 3D graphics on the web. Their paper described a methodology that connects IoT data with the virtual world and the benefits of using web-based human-machine interfaces. They used the Autodesk 3 ds Max Design software to develop a 3D scene, including a four-apartment building. An autonomous, self-sufficient IoT mixed reality system was installed to exploit the _infrastructure control_ and _data monitoring_ methods for the water management infrastructure. In another work, [PERSON] and [PERSON] [178] installed markers and an IoT system in an apartment for monitoring indoor comfort-related parameters, such as air temperature, humidity, light level, and thermal comfort. As the mobile device scans a marker, it can acquire real-time data transmitted by the sensors and visualize them as augmented reality content. [PERSON] et al. [179] introduced AR-IoT, a system that superimposes IoT data directly onto real-world objects and enhances object interaction. Their purpose was to achieve novel approaches to monitoring crop-related data, such as coordinates, plant color, soil moisture content, and nutrient information. Other applications of IoT-based data monitoring methods include fuel cell monitoring [180], campus maintenance [181], and environmental data monitoring utilizing serious gaming [182]. Using the IoT-based context, we can also interact with virtual agents to \"humanize\" interactions with objects or living organisms. [PERSON] et al. [183] developed an avatar to express a plant's \"emotion states\" for plant-related data monitoring. The IoT system calculates arousal and valence based on soil, light, and moisture levels. By turn, the virtual avatar is designed to express these states on behalf of the plant. For example, the avatar will grow large and angry if the plant is left unwatered or be happy when the plant's soil moisture is at the proper levels.
# Space-Efficient, Fast and Exact Routing in Time-Dependent Road Networks +
###### Abstract  
We study the problem of quickly computing point-to-point shortest paths in massive road networks with traffic predictions. Incorporating traffic predictions into routing allows, for example, to avoid commuter traffic congestions. Existing techniques follow a two-phase approach. In a preprocessing step, an index is built. The index depends on the road network and the traffic patterns but not on the path start and end. The latter are the input of the query phase, in which shortest paths are computed. All existing techniques have large index size, slow query running times or may compute suboptimal paths. In this work, we introduce CATCHUp (Customizable Approximated Time-dependent Contraction Hierarchies through Unpacking), the first algorithm that simultaneously achieves all three objectives. The core idea of CATCHUp is to store paths instead of travel times at shortcuts. Shortcut travel times are derived lazily from the stored paths. We perform an experimental study on a set of real world instances and compare our approach with state-of-the-art techniques. Our approach achieves the fastest preprocessing, competitive query running times and up to 38 times smaller indexes than competing approaches.  
Keywords:realistic road networks; time-dependent route planning; shortest paths +
Footnote †: This paper is an extended version of our paper published in Proceedings of the 28 th Annual European Symposium on Algorithms (ESA 2020), Pisa, Italy, 7-9 September 2020.
# Diagnosis of the Multiepitope Protein rMLEEISH3 for Canine Visceral Leishmaniasis
###### Abstract  
Canine visceral leishmaniasisiasis (CVL) is a major zoonosis that poses a growing challenge to public health services, as successful disease management requires sensitive, specific, and rapid diagnostic methods capable of identifying infected animals even at a subclinical level. The objective of this study was to evaluate the performance of the recombinant chimeric protein rMLEEISH3 as an antigen in ELISA assays for the robust diagnosis of CVL. The protein was expressed in a bacterial system, purified by affinity chromatography, and evaluated through a series of serological assays using serum samples from dogs infected with _Leishmanina infantum_. ROC curve analysis revealed a diagnostic sensitivity of 96.4%, a specificity of 100%, and an area under the curve of 0.996, indicating excellent discriminatory power. Furthermore, rMLEEISH3 was recognized by antibodies present in the serum of dogs with low parasite loads, reinforcing the diagnostic potential of the assay in asymptomatic cases. It is concluded that the use of the recombinant antigen rMLEEISH3 could significantly contribute to the improvement of CVL surveillance and control programs in endemic areas of Brazil and other countries, by offering a safe, reproducible and effective alternative to the methods currently recommended for the serological diagnosis of the disease.  
recombinant antigen; _Leishmanina infantum_; dogs; ELISA; zoonosis 2025, 15, 8683
## 1 Introduction
A larger penetration depth can be obtained in comparison with X-ray diffraction since a neutron can penetrate several centimeters into the material. However, the relative cost for the neutron diffraction technique is higher [8]. The sectioning technique is a destructive method that can be used to determine residual stresses. A cut is made through the material without introducing any plasticity or heat in the surface of the material. During the cutting process, strains are released which are measured with strain gauges. These strains are used to determine the residual stresses [8]. In this paper, an orthotropic steel deck is used to perform residual stress measurements near a welded connection and a semi-destructive measurement technique is used. The incremental hole-drilling method is used to measure residual stresses in the welded steel components. This method is preferred because the measurements can be easily performed at the bridge site without affecting the performance of it. A small hole is drilled in an incremental number of steps and the corresponding strains are measured with strain gauge rosettes on the surface. Residual stresses are calculated with these measured strains and a residual stress distribution into the depth of the material is obtained [1]. By the evaluation of high residual welding stresses, plastic relaxed strains are introduced near the borehole. This plastic behavior can possibly introduce significant errors in the calculation of the residual stresses since the hole-drilling method only applies when the material behavior remains linear-elastic [9; 10]. In this paper, the incremental hole-drilling method is simulated using finite element modeling (FEM) with a material model including plasticity. First, uniform in-depth residual stress is simulated and a comparison is made between calibration coefficients specified by the American Society for Testing and Materials ASTM E837-13a and calculated coefficients including the effect of plasticity from the finite element model. Afterwards, non-uniform in-depth residual stresses are studied by evaluating experimental residual stress values of an orthotropic steel deck.