esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrospun Nanofibers for Integrated Sensing, Storage, and Computing Applications
## 4 Applications of Electrospinning in Memory: Storage Electronics  
In recent years, the usage of the semiconducting device as a memory device for storage purposes has seen huge growth. On the other hand, electrospinningning is one of the most cutting-edge methods for producing fine fibers from a wide range of materials and can synthesize ultrafine fibers ranging from micrometers to nanometers with simplicity in preparation and the potential for large-scale production. Several decades of development have brought benefits to the morphology and performance of the electrospun nanofibers. Combing the memory device and electrospun nanofibers will undoubtedly be highly prospective for high-performance memory electronics at a low cost. As one of the key tasks of modern computers, a memory device's basic objective is to provide an approach for storing and accessing binary digital data sequences of '1's and '0's. According to this premise, an electrospun nanofiber-based memory device stores data based on different electrical conductivity states (ON and OFF states) in response to an applied electric field, which entirely differs from the traditional silicon-based memory devices in which data are stored based on the amount of charge stored in the memory cells. However, there have not been many reports of electrospun materials being applied for memory devices and the number of published papers is limited. Many studies for memory materials or devices involving the electrospun nanofiber mats are still in their infancy. Non-woven bead-free highly crystalline T-Nb\({}_{2}\)O\({}_{5}\) nanofibers with a length of 100 \(\upmu\)m and a diameter of 80-200 nm were produced by the sol-gel aided electrospinningning method [107] (Figure 15a). Incorporated with nitrogen-doped diamond-like carbon (DLC) and Pt electrode, these Nb\({}_{2}\)O\({}_{5}\) fibers exhibited memristive properties. Such as reproducible resistive switching with an on/off resistance ratio as high as 2 \(\times\) 10\({}^{4}\), as shown in Figure 15b. I-V characteristics for the embryonic memory device had a bipolar nature which began with a threshold voltage of 0.8-1.7 V and progressed to continuous conductivity development. Besides, a vertical sandwich Pt/erbium (Er)-doped sodium-potassium niobite (Er:NKN) fibers/Au structure was observed to have memristor-type current-voltage (I-V) characteristics with the changing resistance ratio of 10\({}^{2}\) from low-resistance to high-resistance state [108] (Figure 15c). The electrospun Er:NKN nanofibers (Figure 15d) provided the channel for the electrochemical migration of oxygen vacancies which started at the n-type Er:NKN oxide/high work function Pt cathode interface, and in turn brought about electric field-induced resistance switching characteristic for the devices. In addition, negative differential resistance was the key feature for the perovskite (CH\({}_{3}\)NH\({}_{3}\)PbI\({}_{3}\)) decorated electrospun TiO\({}_{2}\) nanofibers (Figure 15e,f) due to the
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
###### Abstract  
In the radiocommunication area, we may observe a rapid growth of new technology, such as 5G. Moreover, all the newly introduced radio interfaces, e.g., narrowband Internet of Things (NB-IoT), are strongly dependent on the software. Hence, the radiocommunication software development and optimization, as well as the 3 GPP technical specification, should be introduced at the academic level of education. In this paper, a software-defined NB-IoT uplink framework in the field of design is presented, as well as its realization and potential use cases. The framework may be used as an academic tool for developing, investigating, and optimizing the digital transmitter paths. The proposed realization is focused on the key elements in the physical layer of the NB-IoT interface used in the sensor devices. Furthermore, the paper also highlights the need of the data processing optimization to minimize the power consumption and usage of the resources of the NB-IoT node during transmitting gathered telemetric data.  
framework; NB-IoT; academic testbed; simulations +
Footnote †: journal: Sensors
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 3 Image Space Multidirectional Occlusion Shading Model
### Light Model  
Figure 1a illustrates the ISMOS model. The color and depth buffers were rendered as rectangles whose texture coordinates were between \((0,0)\) and \((1,1)\). The \(z\) values from the depth buffer ranged between zero and one. Note that we used 1.0--z--as the depth value in this work. All units in this section were relative to the texture coordinates. We then placed a hemisphere with a radius \(R\), at the center of the rectangle, which was \((0.5,0.5,0.0)\). The light source was defined as a disk whose radius was \(d\), and its center was located on the hemisphere. We then sampled \(N\) light points uniformly on the disk. \(L_{sp}\) indicates the light sample point in the light source.  
The light sample points were obtained by rotating the sample points on the disk with the radius \(r\), whose center was at \((0.5-R,0.5,0.0)\), to simplify the calculations. We rotated the light sample points by angle \(\phi\) about the \(y\)-axis (\(R_{y}(\phi)\)) and then rotated them once more by angle \(\theta\) about the \(z\)-axis (\(R_{z}(\theta)\)). The rotation matrix was obtained as follows.  
\[R_{z}(\theta)R_{y}(\phi)=\begin{pmatrix}cos(\theta)cos(\phi)&-sin(\theta)&- cos(\theta)sin(\phi)\\ sin(\theta)cos(\phi)&cos(\theta)&-sin(\theta)sin(\phi)\\ sin(\phi)&0&cos(\phi)\end{pmatrix}\]  
where \(0\leqq\theta\leqq 2\pi\) and \(0\leqq\phi\leqq\pi\). Note that whenever the light source moved, we just needed to compute the light sample point positions only once for all pixels. However, all previous image-space approaches generate similar samples per pixel since a hemisphere is defined per pixel. In our model, it was possible to use a sphere instead of the hemisphere for the light source location in Figure 1a. Then, the center of the sphere was \((0.5,0.5,0.5)\), and \(\phi\) was between zero and \(2\pi\).  
We also defined the light intensity, \(I_{L}\), assuming that the intensity at all light sample points from one light source was the same, to simplify computations. The intensity was attenuated by the distance between the pixel and the center of the light source. It was proportional to the inverse of the distance square as follows. \(I_{L}\propto\frac{1}{\|I_{L}-\|^{2}}\), where \(I_{c}\) is the center of the light source and \(P\) is the pixel position. Note that \(P\) is represented as \(P=(P_{x},P_{y},z(P_{x},P_{y}))\), where \(z(P_{x},P_{y})\) is the depth value at a position \((P_{x},P_{y})\) on the rendered image. We could manage the light intensity of each light source.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 2 Non-Terrestrial Networks in 5G/6G
### The Evolution of Non-Terrestrial Networks  
Non-terrestrial networks (NTNs) were first mentioned in the use case of fifth-generation (5G) communication standards, and the concept has drawn extensive attention from research scientists for the grand vision of space-air-ground-sea integrated networks (SAGSINs). The 5G ecosystems have included satellite communications as an important means of producing NTNs. The initial research of NTNs started right after the Third-Generation Partnership Project (3 GPP) organization announced its first release of the 5G communication standards containing the NTN concept. Since then, a variety of research papers have been published concerning NTN technology. The evolution of 3 GPP NTN standards of 5G communication is illustrated in Figure 1. In release 15 of 3 GPP 5G standards, TR 38.811 was introduced as \"NR Research Supporting Non-Ground Networks\", which illustrated the application scenarios of NTN communications and pointed out the unresolved problems that may have a great influence on the application of NTNs. The next evolutionary step of 5G NTN was finished with release-16 of 3 GPP, which put forward solutions for supporting NTNs, in which satellite communications were listed as a priority for NTN applications. In release 17 of the 3 GPP 5G standards, LEO (low Earth orbit) and GEO (geostationary transfer orbit) satellite applications were emphasized, while high-altitude platform station (HAPS) applications were also taken into account. Finally, 3 GPP froze the release 17 standards at its 96 th plenary session. A practical architecture of a NTN communication network is shown in Figure 2, which can be divided into space, air, and ground layers. The top part of the NTN network architecture is the space network, which is usually made up of a number of inter-connected satellites, while the middle part of the network is composed of HAPS (high-altitude platform stations), LAPS (low-altitude platform stations), and air-to-ground networks utilizing UAVs and airships. The ground part is the conventional terrestrial network [18; 19; 20; 21; 22; 23].  
NTN technology represents new prospects in wireless communication applications such as satellite communications and low-altitude communication utilizing unmanned aerial vehicles and airships for internet of things devices or autonomous systems. UAVs and airships acting as radio access network infrastructure are capable of providing lower-latency services than satellites, which is crucial for time-critical applications. NTN communication expands the application of 5G communication from terrestrial communication to space/air communication. The transformative advancement in communication technology promoted the shift from fifth-generation communication to sixth-generation, which highlights the vision of ubiquitous networks and space-air-ground coverage [24; 25; 26; 27].  
Figure 1: The evolution of 3 GPP NTN standards.
## 1 Introduction
Three porous materials, including perlite, wood shavings, and polystyrene, were tested in both dry and wet forms. The time courses of the volunteers' recorded inhaled and exhaled concentrations of oxygen and carbon dioxide were very similar between the materials. Perlite was identified as the most suitable material for simulating avalanche snow due to its uniformity, reproducibility, and ease of manipulation. Furthermore, perlite is an inexpensive, non-toxic, amorphous mineral used commercially for its low density and ability to hold large amounts of water [16]. The subsequent study conducted by [PERSON] et al. [17] aimed to compare dry and wet perlite with snow in terms of ventilation and gas exchange parameters. Thirteen male subjects underwent three breathing phases--in snow, wet perlite, and dry perlite. The resulting values of the gas-exchange parameters for the snow used in the study ranged between those for dry and wet perlite. According to a study conducted by [PERSON] et al. [18], the solubility of respiratory gases in snow or in the water contained in snow could affect the spread of gases when avalanche victims breathe under the snow. The solubility values of oxygen and carbon dioxide in water at 0 \({}^{\circ}\)C and standard atmospheric pressure are 1.46 \(\times\) 10\({}^{-2}\) g\(\cdot\)L\({}^{-1}\) and 3.3 g\(\cdot\)L\({}^{-1}\), respectively [19; 20]. The solubility of CO\({}_{2}\) in ice is significantly lower [21]. In dry snow, the absorption of CO\({}_{2}\) at the ice surface is insignificant [22], but the flux in snow containing liquid water could be affected by the absorption processes. Previous studies have investigated the diffusion of various gases through snow [5; 6; 7; 8; 9; 10] or the properties of snow that affect gas exchange [4; 18; 23]; other studies have compared snow with other materials based on ventilation parameters [15; 17]. However, no experimental study of the diffusion of respiratory gases in the snow layer and perlite in relation to avalanche survival has been conducted to our knowledge. The aim of the study was to experimentally determine the diffusion rate of oxygen and carbon dioxide in snow, wet perlite, and dry perlite simulating avalanche snow and to compare the materials in terms of diffusion.
# Seismic Response and Mitigation Analysis of a Subway Station in the Site with Weak Interlayers
## 3 Seismic Mitigation Effect of FPB in Weak Interlayer Site
### The Mechanical Model of FPB  
FPB is typically categorized into single-sliding-surface and double-sliding-surface bearings. The single-sliding-surface FPB is the most commonly used type. The FPB primarily consists of upper support, a slider, and lower support. The main structural material of the bearing is typically metal, ensuring high reliability and easy quality assurance. The friction surface is coated with low-friction materials such as polytetrafluoroethylene (PTFE). The specific structural configuration of the bearing is depicted in Figure 20.  
Figure 19: Tensile damage of the station with different shear wave velocities. (**a**) Shear wave velocity of 80 m/s. (**b**) Shear wave velocity of 90 m/s. (**c**) Shear wave velocity of 100 m/s. (**d**) Shear wave velocity of 110 m/s. (**e**) Shear wave velocity of 120 m/s.  
Figure 18: Maximum displacement of the station side wall and column. (**a**) Side wall. (**b**) Upper column. (**c**) Lower column.  
Within the friction pendulum bearing, the slider, under the influence of seismic motion, experiences a vertical load denoted as \(W\). The sliding friction force on the bottom surface of the slider is represented as \(f\). The horizontal restoring force of the friction pendulum bearing is designated as \(F\). The normal force on the sliding surface is labeled as \(N\). The radius of the sliding surface is \(R\) and the horizontal sliding distance of the slider is \(D\). The angular displacement of the slider relative to the axis of symmetry is represented as \(\theta\). As illustrated in Figure 21, when analyzing the forces acting on the bearing, the friction force can be expressed as follows, with \(\mu\) denoting the friction coefficient of the sliding surface and the normal force being represented by \(N\). The equilibrium of forces on the slider can be expressed as  
\[F\cdot R\cos\theta=W\cdot D+f\cdot R \tag{5}\]  
When the angular displacement of the slider is small, \(N\) is almost equal to \(W\), and Equation (5) can be simplified to  
\[F=\frac{W\cdot D}{R}+f=\frac{W\cdot D}{R}+\mu W\text{sgn}(\theta) \tag{6}\]
## 5 Discussion
### Other Implementation Implications  
The cost is a significant factor to consider when making decisions about adopting blockchain technology. Some argue that the benefits of streamlined operations, reduced risks, and compliance costs outweigh the implementation expenses. However, in high-tech industries where product features are prioritized and profit margins are high, the cost may receive less emphasis. It is becoming clear that blockchain is suitable only for applications where the additional transaction costs can be justified by the overall benefits after a careful analysis [76]. Privacy is another important factor to consider, as higher visibility often means sacrificing some level of privacy. IT firms will play a crucial role in developing the necessary infrastructure, platforms, and products for implementing blockchain technology in specific industry segments. However, it is challenging to predict the exact timeline for the widespread use of this technology. Consequently, there is little incentive for either party to move to a blockchain platform.  
In the context of finance, blockchain technology's primary contribution is breaking down physical and temporal barriers, providing effective and efficient visibility, and enabling integration with the ATO's (Australian Taxation Office), employers', and super funds' ERP (Enterprise Resource Planning) systems. It is important to note that ERP systems like SAP and Oracle store and manage large databases internally within an organization, protected by firewalls.  
The lack of interoperability between different blockchain networks can result in data and asset silos, which are challenging to integrate and utilize across multiple networks. Additionally, blockchain technology consumes substantial energy, particularly in proof-of-work blockchain, where miners compete to solve complex mathematical problems for transaction validation and rewards.  
Despite the computerization of payroll processes, blockchain-based payroll applications are currently limited [41]. This can be attributed to the significant barriers to entry, further compounded by the absence of a fiat cryptocurrency that facilitates international payroll transactions without significant foreign exchange risks. Moreover, it is essential to carefully consider the use cases and implementation of different blockchain networks and protocols to maximize their potential benefits.  
In summary, while blockchain technology holds the potential to revolutionize numerous industries, it is crucial to acknowledge its limitations and thoroughly evaluate its use cases and implementation. Additionally, the operational aspects of blockchain models, such as breakdowns, delays, and bottlenecks, should be proactively addressed in the context of addressing challenges in the finance sector.
# A Survey of Vehicular VLC Methodologies
## 4 Technical Challenges in Vehicular VLC and Solutions
### Impact of Reflection  
Line-of-sight (LoS) communication is essential in vehicular VLC. However, its availability is a critical challenge in outdoor applications due to reflections from buildings, vehicles, land, and other objects, which form non-line-of-sight (NLoS) links. Due to these NLoS links, the reliability of the connection cannot be guaranteed, because the signal strength in the receiver device depends mainly on the reflection properties [17; 95; 96]. Moreover, climatic conditions such as snow, ice, and rain affect the strength of reflections, affecting the road surface and thus influencing the reflectivity and connection strength of NLoS [95; 96]. In some instances, the road may be under highly reflective conditions. These reflections  
Figure 5: The general structure of the V-VLC system. The figure, on the left, shows the transmitter device, including signal processing (encoding, modulation), LED driver, and transmitter front end. The transmitter and receiver are linked by a channel affected by interference from weather effects, reflections, and natural and artificial light sources. The right side shows the receiving device, including the receiver front end and signal processing (demodulation and decoding).  
from the ground surface are much more substantial due to the angle of reflection, so the wet road is emulated by using a shiny linoleum inner sidewalk. Within certain distances of both the receiver and sender side, the non-line-of-sight components have the potential to have signal strengths comparable to those of the line-of-sight parts. Figure 6 shows the impact of reflection (LoS and NLoS).  
The characteristics of the vehicular VLC propagation channel are primarily affected by optical signal distortion caused by reflection, dispersion, and attenuation. These factors significantly impact the performance of the vehicular VLC system, leading to the emergence of research focusing on modeling the vehicular VLC channel under LoS transmission assumptions. In [76], a ray tracing simulation method is proposed to develop a headlight LoS path loss model. Similarly, ref. [73] presents a taillight loss model. Additionally, ref. [30] introduces a path loss model that considers asymmetric lighting conditions. However, there are limited studies addressing the NLoS vehicular VLC channel. The effects of reflections on road surfaces are explored in [96; 97], emphasizing their impact on vehicular VLC performance. Furthermore, ref. [95] investigates specular reflections on the road surface and their detrimental effects. Another study, ref. [98], focuses on the reflection from surrounding vehicles. Table 3 provides an overview of the studies discussing the influence of LoS, NLoS, and reflections on vehicular VLC.
# Adsorption of Lead and Nickel on to Expanded Graphite Decorated with Manganese Oxide Nanoparticles
## 2 Materials and Methods
### Data Analysis  
#### 2.4.1 Adsorption Isotherms  
The experimental equilibrium data of Pb\({}^{2+}\) and Ni\({}^{2+}\) adsorption onto the prepared MONPs-EG in single and binary metals systems were characterized using Langmuir and Freundlich isotherms [28; 29; 30]; their mathematical expressions are given by the following Equations (2) and (3), respectively.  
\[q=q_{\max}\frac{K_{L}C_{e}}{1+K_{L}C_{e}} \tag{2}\]  
\[q=K_{F}C_{e}{}^{1/n} \tag{3}\]  
where \(q_{max}\) is the maximum adsorption capacity of MONPs-EG for the metal ions (mmol/g), \(K_{L}\) is the Langmuir constant related to the affinity of the adsorption sites (L/mmol), \(K_{F}\) is the Freundlich constant related to the adsorption capacity (mmol/g) (L/mmol)\({}^{1/n}\), and \(1/n\) is the Freundlich constant related to the adsorption intensity (dimensionless).  
The comparison between adsorption performances of the prepared MONPs-EG with its precursor or with various manganese oxide-based adsorbents were evaluated. In addition to adsorption capacity, we also investigated the partitioning coefficient (PC) of the adsorbent toward target adsorbate, which represents the ratio of the adsorbate amount in the solid adsorbent phase to its concentration in the liquid phase at equilibrium [31; 32]. The \(PC\) can be calculated by the following Equation:  
\[PC=\frac{q_{e}}{C_{e}} \tag{4}\]  
where \(PC\) (mmol/g/mM) is the partitioning coefficient, \(q_{e}\) (mmol/g) is the adsorption capacity at equilibrium, and \(C_{e}\) (mM) is the concentration of metal ions at equilibrium.