esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvement of Mechanical Properties of 3D Bioprinted Structures through Cellular Overgrowth
## 1 Introduction
A property that is characteristic of gelatin is its ability to undergo thermo-reversible physical cross-linking by cooling to form hydrogels by forming locally ordered regions that link with hydrogen, electrostatic, and hydrophobic bonds. This property gives gelatin-based bioinks the ability to undergo extrusion 3D printing for different shapes and structures [37; 38; 39]. In addition, gelatin has bioactive arginine-glycine-asparatic acid (RGD) sequences in its structure, creating a favorable microenvironment for cellular interactions that enable cell adhesion, migration, proliferation, and differentiation [9; 16; 39; 40]. Oligopeptide sequences containing the RGD peptide also affect the more favorable controlled degradation kinetics of alginate-gelatin scaffoldings with respect to hydrogel structures produced only based on sodium alginate [39]. Therefore, appropriate compositions of alginate-gelatin hydrogels are materials that combine the most desirable features of the individual components with the purposes of obtaining synergistic effects and creating a bioink with unique properties. Thanks to the combination of sodium alginate and gelatin, it is possible to obtain high levels of cell adhesion, diffusion, spreading, migration, and proliferation, and to eliminate the limitations resulting from the poor mechanical strength of gelatin [29; 39; 40; 41; 42]. In 3D bioprinting techniques, the development of hydrogel printouts with the appropriate geometry is necessary to ensure the metabolic activity of the cells contained in the 3D structures. Obtaining thin-walled printed geometries is desirable to guarantee uniform nutrient availability and gas exchange during cultivation [43]. Using 3D bioprinting techniques, we can control the mechanical properties, porosity, permeability, and rigidity of the printouts' hydrogel matrix [42]. The research work carried out by our team to date has enabled the development, using 3D extrusion bioprinting techniques, of three-dimensional thin-walled tubular-shaped bioprinted structures that allow for the even distribution of biological material inside the polymer matrix, as well as the diffusion of oxygen, nutrients, and metabolites from and into the cells [44; 45]. Within the framework of this study, tubular printouts were developed based on sodium alginate and gelatin containing immortalized fibroblast cells of the NIH/3T3 line. Directly after the 3D bioprinting process, structures with such a geometry are characterized by extremely low mechanical properties. The aim of this research work was to perform a comparative analysis of the viability and spreading ability of the biological material contained in the printouts during their incubation for 8 weeks while monitoring the cellular proliferation effect on changes in the mechanical properties of the structures.
## 3 Results
### Changes in Lipid Peroxidation and Antioxidant Defense System in Lung Tissue  
In the PM\({}_{2.5}\) control group, lipid peroxidation (MDA level) and ROS increased significantly in the lung tissue (\(p<0.01\)), along with a decrease in the GSH level and CAT and SOD activity when compared with the normal vehicle control group. On the other hand, a significant reduction (\(p<0.01\)) in lipid peroxidation and ROS and increases in the CAT, GSH, and SOD activity were observed in the lung tissue of mice treated with all three IAP extract doses. In addition, these effects were in a dose-dependent manner. Particularly, the IAP400 group exhibited comparable antioxidant activity to the DEXA-administered group (Table 4).  
Alterations in the mRNA Expression of MUC5B and MUC5 AC, Which Are Related to Mucus Production in Lung Tissue  
In the PM\({}_{2.5}\) control group, the expression of mucus-production-related MUC5 AC and MUC5B mRNA in the lung tissue significantly increased (\(p<0.01\)) when compared with the normal vehicle control group. On the other hand, compared to the PM\({}_{2.5}\) control group in the lung tissue, MUC5 AC and MUC5B mRNA expression were reduced significantly (\(p<0.01\)) in all three IAP groups in a dose-dependent manner. In particular, MUC5 AC and MUC5B mRNA expression was comparable between the IAP400 and DEXA groups (Table 5).  
Figure 6: Lung substance P and ACh contents in vehicle or PM\({}_{2.5}\)-treated pulmonary injured mice. Values are expressed mean \(\pm\) SD. ACh = Acetylcholine; DT3 = Dunnett’s T3. \(p<0.01\) versus vehicle control via DT3 test. \(p<0.01\) versus PM\({}_{2.5}\) control via DT3 test.  
Alterations in the mRNA Expression of NF-\(\kappa\)B, p38 MAPK, Akt, PI3K, and PTEN, Which Are Related to Oxidative Stress and Inflammation in Lung Tissue  
In PM\({}_{2.5}\) control, PI3K, NF-\(\kappa\)B, Akt, and p38 MAPK mRNA expression increased significantly (\(p<0.01\)), and PTEN mRNA expression reduced significantly (\(p<0.01\)) compared to the normal vehicle control. In contrast, NF-\(\kappa\)B, p38 MAPK, PI3K, and Akt mRNA expression reduced significantly (\(p<0.01\) or \(p<0.05\)) and PTEN mRNA expression increased significantly (\(p<0.01\) or \(p<0.05\)) in the lung tissue in all IAP groups in a dose-dependent manner. In particular, in the IAP400 group, p38 MAPK, inflammation-related NF-\(\kappa\)B, Akt, PI3K, and PTEN mRNA expression and PM\({}_{2.5}\)-induced oxidative stress were comparable to those in the DEXA supplied group (Table 5).
## 4 Results and Discussion
### Analysis of Flame Propagation Velocity and Position of Frontal Surface  
Figure 13 shows the flame front position and flame propagation velocity over time for four angular obstacles in three positional conditions. It can be seen that the flame propagation velocity was basically the same (before 10 ms). Combined with Table 4, it was found that three accelerations of the flame velocity occurred for most of the conditions. From 10 ms onward, the flame propagation velocity rises considerably; at this time, the percentage increase in velocity was the largest of the three increases. At this point, the flame had not yet reached the obstacle and was in the laminar combustion stage, with rich fuel. At 20 ms, the flame touches the obstacle, which leads to a steady acceleration of the flame due to the strong turbulence effect of the flow field caused by the obstacle, until it reaches the maximum propagation velocity. It was found that the flame propagation velocity was maximum when the obstacle was located at 500 mm, regardless of the angle of the obstacle. It is especially obvious that the flame front surface reaches the deflagration port first when the obstacle was near the ignition source, compared to other working conditions. This is because the initial turbulence intensity is higher, and the transition to turbulence was easier when the obstacle was close to the ignition source. The sooner the flame passed through the obstacle gap, the longer the flame acceleration distance in the channel.  
Figure 12: Flame conditions at peak area for each operating condition.  
From the figure, it can be seen that in the early stage of the explosion (before 20 ms), the position of the flame front surface in each condition had a similar trend over time, showing a first-order linear increase. Combined with Figure 10, it can be seen that when the flame front surface reached the outside of the pipe, the deflagration overpressure peaked, and as the obstacle is closer to the vent, the later the flame reached the pipe outlet, the later the pressure peaked.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Angle**} & \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**First Acceleration**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Second Acceleration**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Third Acceleration**} & \multicolumn{2}{c}{**Percentage**} & \multicolumn{2}{c}{**Maximum Speed**} \\  & & **(m/ms)** & **of Increase** & **(m/ms)** & **of Increase** & **(m/ms)** & **of Increase** & **of Increase** & **(m/ms)** \\ \hline \multirow{3}{*}{\(30^{\circ}\)} & 200 mm & 0.00674
# Development of a CALPHAD Thermodynamic Database for Pu-U-Fe-Ga Alloys
## 4 Crystal Structure
### Extrapolation to An-Fe-Ga Systems (An = Pu, U)  
The extrapolation to the Pu-Fe-Ga and U-Fe-Ga systems are indicated at 1000 \({}^{\circ}\)C and room temperature in Figures 7-9. In the Pu-Fe-Ga system, there are no additional known ternary compounds so that the predicted phase diagrams are constructed via extrapolation of the pertinent binary systems. While there are some known intermetallics in the U-Fe-Ga system, there is no thermodynamic information about their stability above room temperature as all these compounds have been studied at T \(<\) 25 \({}^{\circ}\)C for magnetic purposes as indicated in the literature review. They are therefore not considered in this work, however, future experiments on these compounds would be useful for thermodynamic assessments. The Pu-Fe-Ga phase diagram is plotted at 1000 \({}^{\circ}\)C in Figure 7; phases from the binary systems which are stable at high temperature phases such as \(\alpha\)-Fe (BCC), \(\gamma\)-Fe (FCC) and the PuFe\({}_{2}\) intermetallic, and Pu-Ga compounds are among the first to form from the liquid phase. The U-analogous diagram in Figure 8 shows similar phase formation with the addition of \(\gamma\)-U (BCC) being stable for the uranium rich portion. More importantly, the room temperature phase diagrams show the formation of both Pu\({}_{6}\)Fe and U\({}_{6}\)Fe intermetallics for the An-rich region of the Pu-Fe-Ga and U-Fe-Ga systems (Figure 9a,b), respectively, which are accompanied by An-Ga phases within the actinide matrix. Closer interpretation is given to the phase stability and formation with respect to temperature for two different alloy compositions in the following section.  
Figure 6: Calculated (**a**) Pu-U-Fe ternary section at room temperature with (**b**) a magnification of the Pu-rich part based on the present CALPHAD assessment.  
Figure 8: Calculated U-Fe-Ga ternary section at 1000 \({}^{\circ}\)C based on the present CALPHAD assessment.  
Figure 7: Pu-Fe-Ga ternary section at 1000 \({}^{\circ}\)C based on the present CALPHAD assessment.  
Figure 9: Calculated (**a**) Pu-Fe-Ga and (**b**) U-Fe-Ga ternary sections at room temperature based on the present CALPHAD assessments.
# Evaluation of Low-Cost GNSS Receiver under Demanding Conditions in RTK Network Mode
## Abbreviations  
BDS BeilDou Navigation Satellite System  
CEP Circular Error Probable  
GAL Galileo Navigation Satellite System  
GLO GLO GLONASS-GLobal'naya NAVigatsionnaya Sputnikovaya Sistema  
GNSS Global Navigation Satellite System  
GPS Global Positioning System  
Hz horizontal or hertz (unit of frequency), depending on meaning  
IPx International Protection Rating  
LIC/A L2C, L1 OF, L2 OF, E1B/C, E5b, B1I, B2I--GNSS signals  
MSD Mean Standard Deviation  
PCO Phase Center Offset  
PCV Phase Center Variation  
ppm part per million  
RMSE Root Mean Square Error  
RTCM Radio Technical Commission for Maritime  
RTK Real Time Kinematic Network  
V vertical
# Underlying Security Transmission Design for Orthogonal Time Frequency Space (OTFS) Modulation
## 3 2D Permutation-Aided OTFS Secure Transmission  
In this section, we propose a so-called 2D permutation-aided OTFS secure transmission scheme, which aims to generate control sequences by utilizing the instantaneities and uniquenesses of wireless channels. Then, the control sequence is used to perturb the OTFS modulation domain, which ensures the underlying security of the OTFS transmission. Furthermore, we develop the SLM algorithm to guarantee the low PAPR feature of the OTFS secure transmission.
## 4 Numerical Approach and Validation  
The finite element method was numerically implemented in order to solve the partial differential equations governing the nanofluid flow and heat transfer (Navier-Stokes equations). The investigated nanofluid domain is discretized into a free triangular node mesh calibrated as predefined finer with a maximum and minimum element size around 0.014 and 2 \(\times\) 10\({}^{-4}\), respectively. Three meshes, having a number of elements around 2422, 4036, and 10,584, were tested. The obtained numerical results for the investigated coarse and fine meshes were very close (the difference was less than 1%). The used complete mesh consisted of 10,584 elements. The relative tolerance was set small enough (around 10\({}^{-5}\)) to guarantee the convergence. Figure 3 depicts the meshing used for the investigated nanofluid domain.  
The validation of the computer code was carried out by implementing the same problem investigated by [PERSON] and [PERSON] [21]. As it is depicted in Figure 4, a very good agreement was obtained between our numerical results and those presented in [21].  
Figure 4: Results validation.  
Figure 3: Free triangular node mesh applied to the investigated nanofluid domain.
# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 2 Artificial Intelligence
### AI Algorithms: Introduction to Unsupervised Learning  
UL is a crucial branch of ML that focuses on algorithms using data that lack explicit labels or predefined outcomes [8]. This approach contrasts with SL, where models are trained on labeled data with known input-output pairs [9]. In UL, the system is designed to autonomously explore and analyze the structure of the data, allowing it to uncover hidden patterns, relationships, and features that may not be immediately recognizable [10] (see Figure 1).  
These hidden structures may reveal novel insights into the relationships between data points, enabling researchers to make discoveries that conventional analysis was unable to reveal. This type of learning is particularly well-suited for complex data processing tasks where manual labeling of data is impossible due to the complexity of the dataset. For example, in healthcare, UL can be used to uncover new disease subtypes or predict patient outcomes based on unstructured electronic health records and genetic data [11]. One of the primary applications of UL is clustering [12], where the algorithm splits large datasets into subgroups based on inherent similarities. Clustering methods are commonly employed in a variety of fields, including genomics [13] and image analysis, to segment data into coherent categories without prior knowledge of their structure [14]. In addition to clustering, UL is also effective in dimensionality reduction, where algorithms are used to simplify high-dimensional data while preserving their most important features [15]. This process is crucial in fields such as bioinformatics, where datasets often contain thousands of variables, making it challenging to extract meaningful insights without reducing their complexity. This learning category also plays a central role in anomaly detection, a task that involves identifying data points that deviate significantly from the norm [16]. Outliers are critical in medical imaging to detect anomalies, such as cancers, that radiologists might not be able to recognize [17]. Despite its strengths, since there are no labeled outcomes to guide the learning process, evaluating the performance of unsupervised models can be challenging. In many cases, domain-specific knowledge is required to interpret the results and determine whether the identified patterns are meaningful [18]. An in-depth analysis of existing unsupervised models, discussing their methodologies, applications, and potential limitations within various research contexts, is therefore necessary.
# Towards the Internet of Smart Trains:
## 2 Communication Systems in Railway Scenarios
### Inter-Car Connection  
Wireless communications and optical fiber can both be employed for inter-car communications. Nevertheless, the latter is less advised since it may be costly to wire a train for network access, and rewiring may be necessary each time the train is reconfigured [3].  
This scenario demands high data rates and low latencies. The APs are rearranged in each wagon such that every one acts as a client station for the AP in the previous car, and as an AP for all the stations within its car. The propagation channel of these communications has been investigated in literature. An example of wireless channel measurement in order to characterize the propagation environment for inter-car communications is described in [10]. Moreover, a measurement and analysis of a channel considering the use of Trans European Trunked RAdio (TETRA) is presented in [11].  
The communications between vehicles cover several use cases. For example, the information on-the-fly between two vehicles. It is frequently a disabled one, out of range of a communication network that transmits information to another vehicle passing nearby [12; 13; 14]. A different example to accelerate the coupling process is the virtual coupling of two vehicles (including car trains or wagons, subways and trans). Moreover, specific mechanical connectors that deteriorate rapidly under the rough vibration conditions in railway operations could be avoided. Nevertheless, for virtual coupling, train-to-train communications are essential to interconnect high-speed networks embedded in both vehicles.  
Currently, the main technologies for inter-car communications are Wireless Fidelity (Wi-Fi), Dedicated Short-Range Communications (DSRC), and Worldwide Interoperability for Microwave Access (WiMAX). Another candidate technology for the wireless connection is Ultra-wideband (UWB), the IEEE 802.15.4a standard. The UWB links are more robust to frequency selective fading. IEEE 802.11p [15] may be an option if high data rates are not required. New technologies at 60 GHz carrier frequencies like mmWave, IEEE 802.11 ad and Machine-to-Machine (M2M) communication systems are also being considered.
# Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety
## 3 Simulation
### Simulation Setup  
We simulated the proposed scheme using NS-3 simulator [22] to assess the network's performance. OpenStreetMap (OSM) was used to generate the topography, which resembles a square of size 600 \(\times\) 600 m. The vehicular movement traces are generated randomly using SUMO [23]. We used IEEE 802.11p as the underlying protocol that provides communication among vehicles. We considered three different traffic scenarios: light traffic, average traffic, and heavy traffic. The vehicle's arrival rate varies according to the desired number of vehicles in different scenarios. Vehicles are randomly inserted into the simulation at a uniform rate, which can be modeled as a binomial distribution of inserted vehicles for each edge. In large networks, this approximates a Poisson distribution, which adequately reflects the vehicle's arrival in real-world scenarios. The pedestrians follow a random mobility model with an average speed of one meter per second. The pedestrians' mobility is equivalent to people running and those who are scattered along the map. The simulation parameters are shown in detail in Table 1.  
The simulation focuses on comparing the network overhead of our proposed scheme with a baseline vehicle-to-vehicle scheme. Specifically, we implemented two scenarios. The first scenario \"Without Pedestrian Protection\" comprises pure vehicle-to-vehicle communication, where vehicles exchange BSM messages without any pedestrian involvement in such communication. In the second scenario \"With Pedestrian Protection\", we implemented vehicle-to-vehicle aside from vehicle-to-pedestrian, where pedestrians communicate with vehicles and run our scheme to detect a potential collision. When a node detects a potential collision with another node, it sends a warning message to this node, as shown in Figure 5. We used C++ on top of NS-3 to implement both schemes. Our study included three traffic scenarios:  
* Low traffic scenario: 50 vehicles/10 pedestrians;
* Average traffic scenario: 100 vehicles/30 pedestrians;
* High traffic scenario: 145 vehicles/60 pedestrians.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter Name** & **Description** & \\ \hline Number of lanes & 1 lane/direction & \\ \hline Map area & 600 \(\times\) 600 m & \\ \hline Lane width & 2 m & \\ \hline Transmission range & 500 m & \\ \hline Data rate & 27 Mb/s & \\ \hline Average runs & 30 & \\ \hline Warning message & 50 Bytes & \\ \hline Traffic scenarios & Low traffic & Average traffic & High traffic \\  & 50 vehicles/10 pedestrians & 100 vehicles/30 pedestrians & 145 vehicles/60 pedestrians \\ \hline Vehicle velocities & \multicolumn{2}{c}{Vary in the range of 65–85 km/h} \\ \hline Maximum simulation time & \multicolumn{2}{c}{300 s} \\ \hline Basic safety message (BSM) interval & \multicolumn{2}{c}{200 ms} \\ \hline Basic safety message (BSM) size & \multicolumn{2}{c}{254 bytes} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
## 4 Experimental Evaluations  
A series of experiments were performed using the monitoring data to evaluate the performance of our proposed PM2.5 forecasting approach. The experiment was divided into two parts: the first part was for the testing of various parameter settings and the second part was for comparison of our proposed approach to two methods, based on PCC and IDW, in terms of the air quality forecasting errors.
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 2 Methods
### Comparative Analysis  
To evaluate the advantages and disadvantages of the ODC grid-based satellite imagery data management strategy over the traditional scene-based strategy, we proposed a comparative approach in terms of time efficiency and memory efficiency. For the 2014-2021 GF-1 image data that were loaded and managed in the ODC grids, we selected the Miyun Reservoir and its surroundings as the study area and conducted a comparative analysis of the monitoring of spatial changes in water bodies and vegetation of the Miyun Reservoir. Specifically, we first conducted the comparative analysis of time efficiency by calculating the time spent on GF-1 data loading and processing in the case study. Then, the comparative analysis of memory efficiency was performed by calculating the memory consumption during data loading and processing in the case study. Lastly, the spatial subdivision technique was used to study the spatial and temporal variation of surface monitoring indicators in the longitude and latitude dimensions of the Miyun Reservoir.
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 5 Result Evaluation and Discussion
### Linear Network of a Width of Three  
In the third trial, we noted the network performance at D by considering both cases, as shown in Figures 8 and 9. To evaluate the performance of the network, as shown in Figure 8, we sent 500 packets to D in the presence of all active relay nodes (R-1, R-2, R-3...R-9). We calculated the PRR at D with respect to all active relay nodes. From the experimental results, the net PRR at D was recorded to be about 89.25%, as shown in Figure 12.  
To evaluate the network, as shown in Figure 9, we sent 500 packets from S to D in the presence of all active relay nodes along with a physical offset between the relay nodes. The results showed the PRR via the relay nodes be about 98%, as shown in Figure 12. As with the linear network of a width of two, these results successfully demonstrated the non-destructive characteristic of LoRa transmission through reduced packet loss when nodes are offset physically.  
Performance evaluation using three relay nodes  
Figure 11: Overall performance using two relay nodes at each hop.  
Figure 12: Overall performance using three relay nodes at each hop.  
During the third experimental trial, by adding the additional relay node at each station, we also realised that replacing identical nodes can significantly impact the overall performance. We discuss the impact of hardware imperfection by considering different manufactures along with identical nodes in Section 6.