esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Composite Laminates Toughened by Nano-Particles
### Two-Dimensional (2D)  
2D nanoparticles have two dimensions outside of the nanometric size range. These layered particles are in the form of single or multiple layers of sheets such as junctions (continuous islands), branched structures, nanoprisms, nanoplates, nanosheets, nanowalls, and nanodisks [122]. Fully or partially exfoliated clays and silicates belong to this family. The effects of 2D nanoparticles on mode-I and mode-II fracture toughness of composites laminates have been studied mainly with nanoclay and occasionally by graphene [77; 78; 123; 124; 125].  
Better low-velocity impact properties [79; 126; 127; 128; 129; 130; 131; 132], CAI [74; 78], residual tensile strength after impact [133], post-fire low velocity impact behavior [80; 134; 135] were reported using nanoclays, with more effect on low-energy levels [125]. The dispersion of clay in polymer matrix shows considerable improvement in energy absorption and ballistic limit of the composite laminates [81; 133; 135; 136]. The fracture toughness and the threshold to crack initiation under cyclic loading were also interestingly improved for the clay modified matrix [127]. Improved toughness and impact behavior was attributed to the change in the failure mechanisms, that shifted from interlaminar failure to a mostly intralaminar failure [125] and increased the stiffness and the resistance to damage progression of the nanophased laminates [137]. The formation of massive microvoids/cracks and the increase of the fracture surface area due to crack deflection were identified as the major toughening mechanisms in highly exfoliated epoxy/clay nanocomposites [93]. Figure 15 shows the fracture morphologies after interlaminar shear tests for a carbon/epoxy laminate modified with nanoclay, indicating a strong adhesion between the fiber and matrix by adding the nanoclay [78]. High content of nano clay causes agglomeration and leading towards limiting the improvement in impact resistance [78; 138]. So there is an optimal content  
Figure 14: SEM of fractured Mode II DCB coupons at different magnifications for (**a**,**c**) baseline laminate and (**b**,**d**) SWCNT-modified laminate. The existence of larger hackles can be seen for SWCNT-modified laminate [73].  
reported for the highest damage resistance and CAI, and the improvement was linked to the transition of failure mechanisms during the CAI test, from the brittle buckling mode to more ductile, multi-layer delamination mode [78].  
Significant improvement in low-velocity impact performance was noticed for the hybrid nanoparticle-reinforced composite samples (hybrid of 1D multi-walled carbon nanotubes and 2D nanoclay Nanoparticles) compared with their individual reinforcement [139].
## 3 Results  
### Dates and Time Elapsed for Interpretation  
There was a substantial difference between the acquisition dates of the images selected for interpretation for the different image types. For PlanetScope imagery, 85% of the images used were from May 2017, and only 2.4% were from March and earlier. For Sentinel-2 imagery, 51% were from May and April, and only a marginal number were from pre-2017 (<1%). Meanwhile, for RapidEye, none of the images interpreted was from April and May 2017, and only 23% were acquired in 2017. However, most of the images were acquired during the dry season (December-May), except for 13% of the images from RapidEye, which were acquired in October and November. There was no identified effect of any differences in the time of acquisitions.  
On average, 83 s were used for each location to record the LULC class and canopy coverage. The difference between RapidEye and Sentinel-2 was minor (68 s versus 73 s), while for PlanetScope, 107 s on average were needed to carry out the interpretation for each location. The seven sites were forming three clusters with respect to time consumption. The time consumption for the three groups were 67-75 s (sites A, F, and G), 87-89 s (sites C, D, and E) and 103 s (site B).
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 4 Electronic and Transport Properties: Effect of Interactions with the Surroundings
### Temperature-Dependent Transport: Scattering Mechanisms
In the case of EG/3C-SiC(111), the mobility shows a sharp increase after 200 K up to a value of \(\sim\)375 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\) at 250 K, which, again, indicates charge impurity scattering, and then a decrease to \(\sim\)330 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\) at 300 K. The negative temperature dependence of mobility for the EG/3C-SiC(111) above 250 K has been related to scattering at the buffer layer in EG on 6H-SiC [133]. At low temperatures, the charge impurity scattering is dominated [56; 127] and, since the temperature is increased, the scattering decreases [25]. In addition, the constant value of conductivity (\(\sim\)3 \(\pm\) 1 (e\({}^{2}\)/h)) obtained for EG/3C-SiC also indicates that the charge impurity scattering at the interface dominates the transport [21]. Figure 16: Temperature dependent mobility between 80 and 300 K for (100) and (111) EG/3C-SiC. Reprinted from Reference [21]. Copyright (2020) American Chemical Society. Figure 15: Temperature dependent sheet resistance of EG on SiC(000\(\overline{1}\)) andnitrophenyl functionalized graphene (NP-EG). Reprinted with permission from Reference [132]. Copyright (2009) American Chemical Society.
# Numerical Study of Mixed Convection and Heat Transfer in Arc-Shaped Cavity with Inner Heat Sources
## 3 Results and Discussion
### Flow Structures  
Figure 6 shows the temperature isotherm and streamline under different Re at fixed time (\(\tau\) = 4620). It can be seen that the flow in the arc cavity is in a transition gradually from asymmetric multicellular structure to bi-cellular structure with the increase in Re. When Re is small (Re = 2.19 \(\times\) 10\({}^{5}\)), the flow structure in the curved cavity is asymmetric, and the streamline is disordered. There are multiple convection cells, and the vertical temperature on both sides of the heat source presents obvious stratification, indicating that heat conduction plays a leading role. With the increase in Re (6.58 \(\times\) 10\({}^{5}\)\(\leq\) Re \(\leq\) 2.19 \(\times\) 10\({}^{6}\)), the cold intrusion flow on both sides gradually overflows the top of the heat source  
Figure 4: Temperature time series and PSD of the mixed convection at P6, P6\({}^{*}\) with different Re.  
Figure 5: Evolution of isotherms and streamlines in a period for Re = 7.67 \(\times\) 10\({}^{6}\).  
and acts with the rising hot plume in the middle to form a front. With the increase in Re, the temperature in the middle-high temperature zone decreases, and the temperature stratification on both sides of the heat source is still visible. As Re continues to increase (\(9.86\times 10^{6}\leq\) Re \(\leq\) 1.14 \(\times\) 10\({}^{7}\)), the intensity of the hot and cold plume fronts near the two heat sources gradually increases and oscillates alternately in the middle area of the arc-shaped cavity, resulting in the rapid release of high-temperature air in the core region and the formation of large convective cells (the convection cell on the right is clockwise and another one on the left is counterclockwise) on both sides of the cavity, without obvious stratification of vertical temperature.  
In addition, it is worth noting that the temperature of the region between the two heat sources does not always decrease with the increase in Re. When Re is greater than \(1.18\times 10^{7}\), the intensity of cold intrusion flow on both sides of the cavity is much stronger than that of the hot plume near the heat source. The cold and hot plume fronts are affected by the inertial force and they directly reach the top outlet position, forming two symmetric convection cells in the core area, which is not conducive to the heat discharge in the core area.
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution
### FoxS-GSC Route Service Operation
#### 3.7.1 Data Gathering and Data Processing  
The information acquisition process is carried out as follows. Vehicles periodically send a message (_Agg_info_message_) with aggregated data (average speed and occupation) of roads in their sectors to a nearby cloudlet to acquire knowledge of road conditions. To aggregate a vehicle's knowledge of the roads in its current sector, a beacon message is sent containing the vehicle's ID and position along with data about the road on which it is traveling. The vehicles that receive this beacon store the data for aggregation with data acquired from other beacon messages. Data aggregation is performed using the Exponential Moving Average (EMA) technique, as described in [15], to obtain the updated road weight.  
Vehicles periodically send information about the roads in their current sector to the nearest cloudlet.  
To prevent several vehicles in the same sector sending similar information in a short period, the following procedure is illustrated in Figure 6. All vehicles in the scenario schedule to send messages in N seconds (two vehicle beacons and a time of 4s each was used) for a message containing data on the roads in their sector. When this time is reached, the message is sent and the counter restarts (Figure 6 stage A). When vehicles inside the same sector listen to a message with aggregated information, they aggregate the new information and reschedule message dissemination for the next N-second interval (Figure 6, stage B).  
During the transmission of aggregated messages to the cloudlet, vehicles within the communication radius of the relay vehicles listen to the message. Therefore, the vehicles collect the information contained in the message about the conditions of the roads in the originating sector of the message. This information is needed for the congestion control process presented in the next step (Section 3.7.2, Service Delivery).  
As cloudlets only receive knowledge on the sectors closest to their geographical position, road condition information is shared among all cloudlets in order to provide full knowledge of the scenario. For this, a FOXS publish/subscribe protocol based on MQTT (Message Queuing Telemetry Transport, described in [15]) is used.
# Trends in LPWAN Technologies for LEO Satellite Constellations in the NewSpace Context
## 3 Architectures and Protocols for IoT Communications via Satellites
### Lpwan
#### 3.2.2 LoRa/LoRaWAN  
The LoRa connectivity protocol, patented by Semtech, enables long-range communication links [51]. It operates in unlicensed bands within the industrial, scientific, and medical (ISM) bands using a modulation technique called the chirp spread spectrum (CSS) at frequencies below 1 GHz, such as 169 MHz, 433 MHz, 868 MHz (in Europe), and 915 MHz (in North America) [52]. The LoRa protocol's physical layer is proprietary and establishes the basis of communication, while LoRaWAN is an open medium access layer (MAC) specification based on the LoRa physical layer. LoRa technology offers six orthogonal spreading factors (SFs), ranging from SF7 to SF12. The choice of SF directly impacts the data transmission rate, transmission distance, and resistance to interference [53]. The SF value determines the transmission duration of a data packet and the transfer rate, affecting the appropriate choice of SF for a given scenario. The LoRa frame structure consists of three main parts: the preamble, the header, and the message payload [54]. The LoRaWAN specification defines three different device classes, Class A, Class B, and Class C, each with its unique transmission and power consumption characteristics [53; 55; 56]. In terms of security, LoRaWAN uses the AES128 encryption and provides two options for connecting devices to the network: over-the-air activation (OTAA) and activation by personalization (ABP) [57]. The long-range frequency-hopping spread spectrum (LR-FHSS) is an enhancement of LoRa that aims to improve the capacity and reliability of LoRa networks in high-device-density and data-traffic-congested environments [58]. The LR-FHSS provides more flexibility and scalability, offering adjustable data rates. It utilizes a rapid FHSS modulation and  
Figure 4: NB-IoT architecture for satellite communications in an LEO. spread spectrum, which reduces interference and enables long-range communication. This technology is an excellent candidate for implementing dense and long-range networks in the context of satellite IoT, and it has been developed to enhance network capacity and collision resistance [59]. It is used in satellite constellations, like Echostar, and has also been adopted by Lacuna, which initially used terrestrial LoRa with CSS [60]. The LoRa Alliance has endorsed the development of LR-FHSS as a crucial extension of LoRa for long-distance applications [59]. Table 4 shows a comparison between LoRa and NB-IoT for satellite communications.
## 4 Experiments
### Monte Carlo Simulation  
The Monte Carlo simulation randomly generates N solutions to select the schedule with the best result in the objective function. In Step 1, N chromosomes are created, and the jobs are randomly assigned to establish the sequence of operations. Figure 12 shows an example of a solution (chromosome) sequencing five jobs (1, 2, 3, 4, and 5), which have 4, 4, 3, 3, and 3 operations, respectively. Then, Step 3 and Step 4 of the EDD heuristic are applied to each chromosome, obtaining the fitness value of each solution. Finally, the solution that provides the best fitness value is selected. A total of 2000 iterations (random solutions) will be used to compare this heuristic with the proposed genetic algorithm.  
The genetic algorithm and heuristics used as benchmarks were coded in Visual Studio 2013 developed by Microsoft Corporation (Redmond, Washington, USA), and the experimental instances were tested on a PC (CPU Intel Xeon (4-Core) E3-1220v5-3.0 GHz, 16 GB RAM).
# Security and Ownership in User-Defined Data Meshes
## 5 Framework Demonstration through a Real-World Case Study  
### The PARADISIOTIS Group (PARG) Factory Case Study  
As previously mentioned, this research utilizes a real-world case study from the area of smart manufacturing to demonstrate the applicability of the framework. Specifically, it utilizes data recorded at the PARG factory, the main business line of which is chicken farming and poultry meat production and distribution. PARG is a continuously growing company that over the years has invested in modern and technologically advanced equipment for breeding processes (e.g., automatic ventilation systems, technology-assisted mills for mixing ingredients and preparing chick food, etc.) and the production line (the cutting, mixing, and packaging of poultry meat). The management of the factory constantly seeks to improve performance and quality levels by frequently adapting production processes and adopting new technologies.  
Data are produced within the factory mainly by two systems. (i) CUBORA is a fully operational heating control system designed to produce and monitor data related to poultry heating and emissions into the feeding atmosphere. This system is essential for ensuring the healthy growth and well-being of chicks on farms. (ii) AGROLOGIC specializes in the field of automated climate controllers, feeding and weighting systems. AGROLOGIC is integrated with a Chore Time controller and collects metrics from several remote sensors that are distributed in the farms, such as CO2, temperature, humidity, air static pressure, and light intensity level. All metrics are recorded in a database and accessed through a Web application in real time. Furthermore, images of the farms and/or equipment may be recorded for shift managers to visually inspect when necessary. Finally, the system generates alerts if any of the metrics exceed pre-defined thresholds via an embedded GSM modem.  
A PARG case study presents all characteristics of big data originating from heterogeneous sources with atypical patterns, which produce various kinds of structured, semi-structured, and unstructured data in high frequencies. These heterogeneous data need to be treated differently from normal production speed data and be stored in more flexible and/or higher servicing speed data storage architectures or structures, compared to classic relational databases and data warehouses, such as big data warehouses, DLs, and DMs. To this end, the current study developed a dedicated DL for PARG in a controlled (lab) environment and applied the basic principles of SDB, Blockchain and NFT technologies for creating data products and domains. The latter are produced based on a DM constructed through the DL metadata mechanism. User requests for access to these data products are addressed to the data owner, and ownership may be granted through NFTs based on the relevant privileges, at the same time providing the ability to grant access and use the data only for a specific period of time.
# Adaptive Multi-Modal Ensemble Network for Video Memorability Prediction
## 4 Experiments
### Prediction Results Analysis
#### 4.2.2 Comparison of Experimental Results  
In this section, Table 4, respectively, shows the prediction results obtained using only video, image, or text sources. As mentioned in Section 3.4, video-memorability-outputs (\(\mathbf{Vmo}\)) is weighted by video-outputs (\(\mathbf{Vo}\)), image-outputs (\(\mathbf{Io}\)) and text-outputs (\(\mathbf{To}\)). So we attempt to weigh only the video source and the text source, and simultaneously weigh the video source and the text source, and compare the memorability scores predicted by the model after the weighting of the video, image, and text. (1) ResNet3D: Only the video features are used for prediction. (2) LBP: Only the image features are used for prediction. (3) Semantics: Make predictions just from text features. (4) ResNet3D+Semantics: The fusion features of video and text are used for prediction. (5) ResNet3D+LBP: The fusion features of video and image are used for prediction. (6) Semantic embedding model: The state-of-the-art method for comparison. (7) AMEN: The proposed method makes predictions by adaptively weighting text, image and video features. The experiments have proved that weighting the three media information sources has a positive effect on the prediction of the memory of the video. That is to say, the addition of image information and text information has a positive effect on the prediction of short-term or long-term memorability. Compared with the experimental results of the existing methods provided by [5], the results in Table 5 show that our method improves
# Mathematical Modeling of Capillary Drawing Stability for Hollow Optical Fibers
## 3 Numerical Study of the Capillary Drawing Stability
### Non-Isothermal Process  
The numerical results presented above refer to the isothermal process of capillary drawing. From a practical point of view, it is more interesting to consider fiber drawing under non-isothermal conditions. Since, at a qualitative level, the processes in isothermal and non-isothermal flows are largely similar, in this study we will focus on the effect of temperature distribution along the furnace surface on the stability of drawing quartz tubes. It is known that the temperature along the surface of the furnace varies, and in the central part of the furnace, a zone (core) of width \(H\) can be distinguished, in which the temperature is constant and much higher than near the edges [21].  
Therefore, during the studies, the temperature distribution was set in the following form:  
Figure 11: Dependence of the amplitude and period of oscillations on the drawing ratio.  
Figure 12: Dynamic system attractor.  
Therefore, there are optimal parameters of the heating element: the core width \(h\), its temperature \(T_{f2}\), the temperature at the edges of the furnace \(T_{f1}\) and the radius of the furnace \(r_{f}\), at which the stability of the drawing process of quartz tubes increases significantly (several times).
## 3 The Evaluation Experiment and Analysis  
In the experiment, the line-structured light calibration method with multiple geometric constraints was evaluated, and the number of parallel constraints is discussed. The thickness of the gauge block was used as an index to evaluate the calibration accuracy. The images and the stripe center point detection method used in the measurement process of the gauge block were completely the same; only the light plane parameters obtained by different calibration methods were different. Therefore, it is reasonable to evaluate the calibration method by measuring the accuracy of the gauge block. The schematic diagram of the experimental model is shown in Figure 5, and the equipment parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equipment Name** & **Model** & **Main Parameters** \\ \hline Camera & MER-125-30 UM & Resolution: 1292 \(\times\) 964 pixel \\ Lens & Computer M2514-MP & Focal length: 25 mm \\ Laser & LH650-80-3 & Power: 0–20 mW \\ Light source & CCS LFL-200 & Luminous area: 200 \(\times\) 180 mm \\ Checkerboard & & NANO CBC 25 mm-2.0 & Manufacturing accuracy: \(\pm\)1.0 μm \\ calibration board & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of equipment.  
Figure 4: Flow chart of the calibration method based on multi-geometric constraints (two parallel constraints): (**a**) flow chart of the calibration method; (**b**) flow diagram of the optimizing optical plane parameters.
# An Evaluation of the Effects of a Virtual Museum on Users' Attitudes towards Cultural Heritage
## 4 Results
### Attitudes  
#### 4.2.1 Overall Index of Attitudes  
We analyzed the moderating role of psychological presence, direction of thoughts and type of multimedia experience on the general attitudes toward the heritage (overall index of attitudes). The overall index of attitudes was subjected to a moderation analysis, where no main effects on said index were found for presence (B = \(-\)0.1376, t(97) = 1.32, \(p\) = 0.18, CI [\(-\)0.6, 0.34]), direction of thoughts (0.6107, t(97) = 1.57, \(p\) = 0.11, CI [\(-\)0.15, 1.3]) or the device used (B = \(-\)0.3997, t(97) = \(-\)0.79, \(p\) = 0.42, CI [\(-\)1.39, 0.59]).  
Likewise, interactions between presence and direction of thoughts (B = \(-\)0.0378, t(97) = \(-\)0.32, \(p\) = 0.74, CI [\(-\)0.27, 0.19]) as well as interactions between presence and the device used (B = 0.1827, t(97) = 1.15, \(p\) = 0.24, CI [\(-\)0.13, 0.49]) were non-significant. Interaction between direction of thoughts and multimedia experience was found significant (B = 1.2872, t(97) = 2.12, \(p\) = 0.036, CI [0.8, 2.48]), showing that the direction of thoughts had a greater effect over the attitudes of participants in the virtual museum, where positive or negative thoughts were better predictors of positive or negative attitudes toward the multimedia experience than in the interactive website condition.  
Finally, presence level, direction of thoughts, and the direction used had marginally significant triple interaction effects on the overall index of attitudes toward the museum experience (B = \(-\)0.3450, t(97) = \(-\)1.92, \(p\) = 0.057, 95 CI: [\(-\)0.7010, 0.0110]). This interaction revealed that predictive power of direction of thoughts and type of multimedia experience is more evident in low psychological presence conditions (B = \(-\)0.3828, F(1,90) = 8.00, \(p\) = 0.005). This means that, when people take part in a simulated experience about cultural heritage, high presence reduces the effect that thoughts (especially negatives) have on attitudes. In the case of visiting a interactive website, the effect of the direction of thoughts on attitudes occurred regardless of high or low psychological presence conditions (see Figure 4).