esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Driver Visual Workload of Bridge-Tunnel Groups on Mountains Expressways
## 4 Discussion  
Previous studies on driver behavior and psychological characteristics in tunnels have mostly focused on individual tunnels, with a particular emphasis on the effects of lighting, landscapes, colors, and other factors on pupils. Research on bridge-tunnel groups primarily centered around operational safety and accident-prone sections. There has been relatively limited investigation of the mechanism of visual workload on drivers in bridge-tunnel groups with high bridge-to-tunnel ratios. In this study, two bridge-tunnel groups were selected for real vehicle tests, during which eye-tracking data from 20 drivers were collected to analyze visual characteristics and driving workload. The driver's fixation area is the key to determining whether the driver can obtain effective road information. As can be seen from Figures 7 and 8, when driving in bridge-tunnel groups, the driver's fixation area is mainly concentrated in the lower right of the road area ahead, and in a longer tunnel, there are more gaze points. The reason may be that the driving environment in bridge-tunnel groups is relatively simple, and the drivers must observe the highway management regulations that prohibit lane changes, in addition to contending with the restrictions imposed by lighting conditions, visual distance, and other factors. In this condition, the drivers face a large visual load and are eager to drive out of the tunnel; therefore, they prioritize the information in the area ahead of the road. In addition, when drivers enter or leave the tunnel, they will experience sudden changes in the light condition, which will reduce the field of vision and the visual distance. This requires the drivers to pay more attention to the area closer to the road. Therefore, it is suggested to streamline the setting of traffic signs on the connecting sections of tunnels and bridges to reduce the disturbance of non-essential and non-urgent traffic information. Solid lines should be drawn throughout the connecting section, and vehicles should be prohibited from lane changes. From Figures 13 and 14, it can be seen that the distance between adjacent tunnels has an effect on pupil diameter. When the tunnel spacing is 206 m, the driver's pupil diameter at the tunnel entrance and exit is the smallest. Under the condition of adjacent tunnel spacing of 35 m, as the driver approaches the entrance section, the pupil diameter changes slowly, and after entering the section, the value rapidly increases and then gradually stabilizes with the adaptation to the dark environment; the pupil diameter in the exit section is the largest. When the distance between adjacent tunnels is 77 m or 169 m, the pupil diameter of the driver in the tunnel entrance section falls in the largest range, and the pupil diameter in the tunnel exit section is in the middle range. When the distance between adjacent
# Deformation Behaviour of Cold-Rolled Ni/CNT Nanocomposites
###### Abstract  
Metal matrix nanocomposites (MMNCs) reinforced by carbon nanotubes (CNTs) are good candidates to produce structural components in the mobility industry, given their unique properties. The manufacture of these components can involve plastic deformation. Therefore, it is crucial to understand whether reinforcement can influence the deformation behaviour of these nanocomposites. Thus, this work aims to study the deformation behaviour of MMNCs, given their importance and the lack of studies on this topic. Although nickel is not the most widely used metal as a matrix of nanocomposites, it presents mechanical properties superior to other matrices, such as aluminium. In addition, this metal has proven to establish a strong interface and integration of carbon nanotubes, making it an exciting material for the production and study of these nanocomposites. In that sense, nickel matrix nanocomposites are reinforced by 1.00 %vol. CNTs were produced by powder metallurgy using ultrasonication as a dispersion/mixture method. For comparison purposes, a nickel matrix was produced under the same conditions. Samples with and without CNTs were cold-rolled with thickness reductions between 10 and 60% (logarithmic strains between 0.11 and 0.92) to investigate the deformation behaviour. Microstructural characterization was performed using scanning electron microscopy (SEM) and electron backscattered diffraction (EBSD). Microhardness tests were applied to evaluate their mechanical properties. The results revealed that the nanocomposites exhibited a softening for small strains (0.11 and 0.22). This decrease in hardness was attributed to the decline in dislocation density observed by EBSD, due to the rearrangement and annihilation of pre-existing dislocations that originated during production. A possible inversion can explain the decrease in dislocation density when minor strains are applied in the dislocation or deformation trajectory, known as the Bauschinger effect. The difference in the texture evolution of the nanocomposites can be explained by the initial crystallographic orientations, which are influenced by the presence of CNTs.  
deformation behaviour; cold-rolling; metal matrix nanocomposites; carbon nanotubes +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Inorganic nanoparticles have opened new avenues of research in biomedicine, where a novel paradigm in therapy and diagnostics in the clinical practice is arising [1]. Plasmonic nanoparticles typically composed of gold or silver appear as a highly promising building blocks due to their photothermal features, among other functionalities. Despite the fact that gold and silver are the most popular metals for fabricating the core of the plasmonic nanoparticles, efficient alternatives can be found using other metals [2]. Photothermal therapy consists of the remote activation of the plasmon field of nanoparticles by irradiation with a resonant light wavelength, leading to a local increase in temperature [3]. This localized heating can kill cancer cells and tissue in an effective manner, with no drugs or biochemical treatment required [4; 5; 6; 7; 8; 9].  
However, one of the most relevant barriers to be overcome by plasmonic nanoparticles that are to be used in actual clinical treatments is the complex biological media in which the nanoparticles must be dispersed [10]. The ions and biological molecules present in biological media influence the aggregation state of the nanoparticles in most cases, leading to a significant modification of the plasmonic properties of the nanoparticles, which is highly sensitive to interparticle distance [11].  
Herein, we aim to determine a fundamental description of the effect of aggregation of gold nanoparticles induced by inorganic ions on the photothermal performance of such nanoparticles. A simplified scenario has been used: small spherical gold nanoparticles coated by citrate ions and sodium chloride as the aggregating-ion. Despite the simplicity of the model, we expect to obtain significant insights that might be used as the first step towards more sophisticated models of nanoparticles and biological media, possibly comprising anisotropic gold nanoparticles capped with different ligands and both inorganic ions and biological macromolecules in the media.
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 6 Results and Discussions
### Average Packet Loss Ratio  
The reinforcement learning agent converges after 50 generations for each TTI. Average packet loss ratio results values were compared with values obtained with maximum throughput (MT), Proportional Fairness (PF), Earlier Deadline First (EDF) and Largest Weighted Delay First (LWDF) [54] algorithms, which results were calculated with a confidence interval of 95%, as shown in Figure 9. We implemented these algorithms as well.  
It can be seen from the figure that our proposed resource allocation algorithm showed the best packet error loss ratio compared to MT, PF, EDF and LWDF resource allocation algorithms. The figure shows that packet error loss ratio significantly increased with the number of served users. It is worth nothing that our proposed resource allocation algorithm is the only algorithm that gave packet error loss rate below conversational voice QoS requirement, which is 1% for packet error loss ratio [39]. The packet loss values of our proposed GA algorithm were between 0.03% and 0.67% while packet loss values of other algorithms were between 2.7% and 7.3%, which are far greater than voice QoS requirement. This difference can be explained by other algorithms using metrics that are mathematical functions and the algorithm always being able allocate resources to users that have the maximum value of corresponding metric. The advantage of the proposed GA algorithm is that it is a heuristic method.  
\begin{table}
\begin{tabular}{l l} \hline \hline Cells number & 19 \\ Cell radius & 1 km \\ Interfered cells & 6 \\ Actives Users per cell & 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600 \\ User distribution & randomly in entire network \\ System bandwidth & 5 Mhz \\ Resource blocks per cell & 25 \\ TTI duration & 1 ms \\ Simulation duration & 3000 ms \\ HARQ type & asynchronous \\ Number of HARQ processes & 8 \\ Retransmission interval & any time \\ Interference model & Explicit \\ Traffic model & Limited buffer size \\ Traffic source & Conversational voice \\ Traffic distribution & Poisson distribution \\ Packet inter arrival time & 20 ms \\ Cell layout & Hexagonal grid \\ Antenna configuration & omni-directional \\ Fading model & Rayleigh fading \\ Channel Estimation & Perfect channel estimation \\ Traffic type & voice \\ Inter arrival time & 20 ms \\ Maximum Hol Delay & 100 ms \\ Transmission Queue size & 100 \\ Retransmission queue size & 100 \\ Tx power interval & [35 dBm, 43 dBm] \\ Tx power for uniform allocation & 43 dBm \\ \hline \hline \end{tabular}
\end{table}
Table 4: System model parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline Population size & 500 \\ Cross over method & one point \\ Mutation method & six points \\ Generation & 50 \\ Cross over rate & 0.4 \\ GA Mutation Rate & 0.1 \\ Selection rate & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: GA parameters.
## 2 Model Description
### Thermodynamic Properties and Equation of State  
The thermodynamic and thermo-physical properties of the fluid need to be calculated when solving the conservation equations. The properties of the fluid in a given node were computed from its pressure and enthalpy using available libraries that calculate the properties of the most common pure fluid.  
\[\begin{cases}T_{k}=f(h_{k},p)\\ \rho_{k}=f(h_{k},p)\\ c_{p,k}=f(h_{k},p)\\ \mu_{k}=f(h_{k},p)\\ k_{k}=f(h_{k},p)\end{cases} \tag{16}\]  
In this context, we adopted _CoolProp_[45], a C++ library that implements pure and pseudo-pure fluid equations of state and transport properties for 122 components. The equations of state used in CoolProp are based on Helmholtz energy formulations and can predict the fluid's properties in the different states in which it can exist: subcooled liquid, saturated liquid, vapour, saturated vapour, and superheated vapour.  
In the presence of a mixture, additional equations must be considered to evaluate the enthalpy associated with each species and then estimate the thermodynamic properties of the fluid. Following the assumptions of a homogeneous model, the distribution of enthalpy in the species can be determined iteratively from the enthalpy and mass fraction of the mixture by performing an energy balance and imposing an equal temperature between the components of the mixture in the control volume:  
\[\begin{cases}T_{i,k}(h_{i,k},p_{i})=T_{i,k+1}(h_{i,k+1},p_{i})\qquad\text{with} \quad k=1,2,\ldots,n_{s}-1\\ \sum_{k=1}^{k=n_{s}}\beta_{i,k}\,h_{i,k}=h_{i}\end{cases} \tag{17}\]  
The properties of a homogeneous mixture of multiple species can be modelled from the properties of the component species. As shown in Equation (18), the density was estimated in this context using [PERSON]'s Law of Partial Volumes, while the other properties were calculated as the molar averages of the component properties.  
\[\begin{cases}\frac{1}{\rho}=\sum_{k=1}^{k=n_{s}}\frac{\beta_{k}}{\rho_{k}}\\ c_{p}=\sum_{k=1}^{k=n_{s}}\chi_{k}c_{p,k}\\ \mu=\sum_{k=1}^{k=n_{s}}\chi_{k}\,\mu_{k}\\ k=\sum_{k=1}^{n_{s}}\chi_{k}\,k_{k}\end{cases} \tag{18}\]  
Finally, the state equation was adopted to compute the pressure in the control volume implicitly. For the \(i\)th fluid node, the mass can be expressed as:  
\[m_{i}=\rho_{i}\,V_{i} \tag{19}\]  
where the fluid density, \(\rho\), is a function of the fluid's enthalpy, mass fraction, and pressure in the control volume and the resident mass is calculated from the mass conservation equations.
## 5 Discussion
For example, in the smart home domain, the S2 NeTM can be customized to support the social relationships between appliances, sensors, and other devices in the home. This customization can enable more efficient energy consumption, automation of tasks, and personalized services for users. In the healthcare domain, the S2 NeTM can be adapted to support the social relationships between medical devices, patients, and healthcare providers. This customization can improve patient outcomes, reduce costs, and enhance the overall quality of care. In the transportation domain, the S2 NeTM can be customized to support the social relationships between vehicles, traffic infrastructure, and other devices in the network. This customization can enable more efficient traffic management, reduce congestion, and improve safety on the roads. Furthermore, the S2 NeTM can be adapted to support industry 4.0 applications, which involves the integration of physical and digital systems in manufacturing and industrial processes. The S2 NeTM can support the social relationships between machines, sensors, and other devices in the production line, enabling more efficient production processes and reducing downtime. In each of these domains, the S2 NeTM can be customized to support specific use cases and applications, leveraging the power of social relationships between devices to enable more efficient and effective IoT deployments. To facilitate customization and adaptation, the S2 NeTM provides a modular architecture that allows for the addition and removal of components as needed. For example, different SEs can be used to support different ontologies and reasoning mechanisms, depending on the specific use case and domain. The semantic repository can also be customized to support different databases and data structures, such as the use of Neo4 for graph-based data storage.
# AI Enhance for Linguistic E-Learning System
## 2 Related Literature
### Speech Synthesis Systems  
Speech synthesis, also known as text-to-speech (TTS), is a process that generates human speech sounds from text. Speech synthesis has been a hot topic since the later part of the 20 th century. The early computer-based speech synthesis approaches include articulatory synthesis, formant synthesis, concatenative synthesis, and statistical parametric synthesis [19].  
With the development of neural network technology, deep-learning-based end-to-end speech synthesis models have been proposed and have become the main methods used in TTS research. A modern TTS system usually consists of three basic components: a text analysis module, an acoustic model, and a vocoder. As shown in Figure 2, the text analysis module converts a text sequence into linguistic features; the acoustic models generate acoustic features from linguistic features; then, the vocoders synthesize waveforms from acoustic features. Tacotron 1, Tacotron 2, Deep Voice, and Fast Speech are all end-to-end TTS examples [20; 21; 22; 23].
## 2 Materials and Methods  
### Data Sources and Processing  
#### 2.1.1 Data Sources  
The Arabian Sea features a diverse range of vessel types, primarily including light purse seine fishing vessels, trawlers, purse seineers, fishing boats, small bulk carriers, large container ships, oil tankers, and naval vessels from various countries. In this paper, identification information for high-sea light purse seine vessels was acquired from the High Seas Purse Seine Technical Group of the East China Sea Fisheries Research Institute (ECSFRI), China. Light purse seine vessels typically have a medium to large carrying capacity, making them suitable for fishing operations in the open sea. A total of 96 vessels were compiled utilizing maritime mobile service identity (MMSI) information for mainland China's high-sea light purse seine vessels between 2021 and 2022. The AIS data, sourced from the US satellite operator Spire, were stored and managed in an SQL database. The analysis and visualization of the AIS data were performed using MATLAB (version R2018b) and R (version 4.2.3).
## 4 Chitosan as Catalytic Support
### Catalytic Metal-Chitosan Composites
No crosslinking agents were used. The average size of the NPs was below 15 nm. CS-Fe/Ni was used for the removal of dyes (MG, MB) at various NP dosages, contact times and the initial concentrations of the dyes. At the chosen conditions (50 mg/L of dye solution (40 mL) and 2 g/L of the adsorbent; contact time 180 min), CS-Fe/Ni NPs revealed high removal efficiency: 99% and 88% of dye removal for cationic dye (MG) and anionic dye (MB), respectively. Recently, [PERSON] et al. [166] reported the synthesis of polymetallic ferromagnetic Pd-Fe-Co-Ni-CS composites for the Heck reaction in aqueous media in the presence of an ionic liquid. Synthesis of the composite was carried out by mechanochemical activation by grinding CS powder with corresponding metal salts with subsequent treatment with reducing agent (NaBH\({}_{4}\)). Additionally, the resulting composite was coated with palladium (Pd-Fe-Co-Ni-CS@Pd). The obtained catalysts were tested in Heck cross-coupling between different aryl bromides and iodides, allowing for a high yield of reaction products (93-98%) in the presence of 0.5 mol.% of Pd. Thus, the following intermediate conclusions can be pointed out:  
1. The ability of CS to effectively adsorb both organic and inorganic compounds can be used for the development of catalytic systems, in which the inorganic part, entrapped by CS, is in catalytically active phase while organic adsorbate is the subject of catalytic transformation;
2. In spite of vast experience in the field of absorbent development, researchers working in catalysis seem to rarely use all the accessible achievements. For example, crosslinking agents are typically not used, as well as certain shaping at macro-level of the resulting catalytic material that could be useful in continuously operating reactors. Adsorption of metal cations was found to proceed more effectively while using CS grafted with different functional groups (see Section 3); hence, it is surprising that in many reported studies devoted to the use of metal ions as catalysts, native CS without any modifications was applied. Thus, there is room for further improvement;
3. Magnetically separable catalysts obviously have the advantage of easy separation from the reaction mixture, but these catalysts have a limitation--sensitivity to low pH, since CS itself cannot prevent the dissolution of magnetic NPs. Thus, CS crosslinking and coating of magnetic NPs with a protective shell (carbonaceous or oxide), resistant to low pH, is a promising approach.
## 5 Conclusions  
This study describes a framework for imputing missing parts in UAV orthomosaics using a data intercalibration approach and the nonparametric random forest (RF) algorithm of an ICLS covered by grass at the time. The proposed framework produces highly accurate imputation of missing parts in UAV orthomosaics, up to 50% of the level of missing parts. It uses a random forest algorithm to leverage the wealth of practical information in optical satellite imagery (PlanetScope (PS) and Sentinel-2 (S2)). The imputed UAV spectral bands can also be successfully used to derive many other vegetation indices of agronomic interest.  
Associating PS images with high spatial resolution and S2 images with high spectral resolution improved the performance of the RF-based imputation models. However, when PS data accessibility is limited, the RF-based imputation can be generated using only S2 data at the expense of greater spatial detailing in the imputed UAV images. Additionally, the proposed framework can be readily adapted to use a wide variety of optical satellite imagery with improved spatial and spectral resolutions to impute missing parts in UAV orthomosaics.  
**Author Contributions:** Conceptualization, [PERSON] [PERSON], [PERSON] and [PERSON]; methodology, [PERSON] [PERSON], [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON] and [PERSON]; investigation, [PERSON] [PERSON], [PERSON]; and [PERSON]; data curation, [PERSON]. [PERSON] and [PERSON]; writing--original draft, [PERSON], [PERSON] and [PERSON]; resources, [PERSON] and [PERSON]; writing--review and editing, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]; visualization, [PERSON] [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by FAPESP (Process numbers 2017/50205-9 and 2018/24985-0) and, partly, by CAPES (Finance Code 001).  
Not applicable.  
The data presented in this work are available on request from the corresponding author. The data are not publicly available due to other ongoing studies.  
The authors would like to thank the owner and all collaborators of the Campina Farm ([PERSON]) and the graduate and undergraduate students, postdoctoral researchers, and technicians of NIPE, UNOESTE, IFAL, and FEAGRI/UNICAMP by the support with the field data collection and preparation.  
The authors declare no conflict of interest. The funders had no role in any process of the present study, including design, collection, analyses, interpretation, writing, or the decision to publish the results.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 4 Interference Mitigation Schemes and Techniques by 3 GPP
### Inter-Cell Interference Mitigation Techniques
#### 4.3.1 Enhanced Inter-Cell Interference Coordination  
elCIC [18; 186] improves cell-edge performance by introducing ABS, allowing macro-cell and SC to operate on the same frequency with temporal separation. This is particularly effective in macro-pico configurations using CRE.