esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
## 2 RANS Computation Method
### Initial Conditions and Boundary Conditions  
When dealing with the initial conditions in this research, the flow parameters about the free stream are usually used to assign initial values for the entire computational domain. However, for some simulations containing extremely complex flow, the initial conditions of the flow field have greater impacts on the subsequent computation results: if the initial values are not appropriate, the calculation can diverge. Therefore, for the high-precisionCFD simulation of the supersonic inlet/isolator, the low-order steady-state simulation results can be used as the initial condition. Boundary conditions are a crucial part of CFD computation, and whether they are handled properly has a great impact on the final result. This research mainly involves four types of boundary conditions:  
(1) Inlet Boundary Conditions  
a. Subsonic: set total temperature, total pressure and flow direction at the entrance;  
b. Supersonic: all the parameters are decided by far-field free stream, including density, static pressure and velocity;  
(2) Outlet Boundary Conditions  
a. Subsonic: set static pressure at the outlet, and the remaining parameters are extrapolated from the inside of the flow field;  
b. Supersonic: all the parameters are extrapolated from the inside of the flow field;  
(3) No Slip Wall Boundary Condition  
For viscous flow, no slip and no penetration conditions are imposed on the wall;  
(4) Symmetry Boundary Condition  
This boundary condition can be used to reduce computation.
# Rapid and Cost-Effective Fabrication and Performance Evaluation of Force-Sensing Resistor Sensors
## 3 Results
### Resistance of Fabricated FSR Sensors  
The resistance value of the FSR sensing surface was measured before assembling the sensor and then compared with commercial products to determine the fixed resistance value for the voltage divider. As shown in Figure 5, the resistance was measured at five locations, each approximately 8 to 9 mm apart, on the FSR sensing surface using a digital multimeter (KeySight, Santa Rosa, CA, USA).  
Table 3 presents the average resistance values for hand-made and commercially available FSR sensors. It is important to note that using a digital multimeter for these measurements may introduce slight inaccuracies. The resistance values for the hand-made FSR sensors, excluding the mixture of 9B pencil lead and silicone sealant, were in the lower kilo-ohm range. However, the mixture of 9B pencil lead and silicone sealant exhibited resistance values that fell between those of commercial products such as Interlink FSR-402 and Flexiforce A201-1. This phenomenon is attributed to the non-conductive silicone sealant infiltrating between the conductive 9B powder (carbon graphite) particles, thereby increasing the overall resistance.
###### Abstract  
The 5G vision embraces a broad range of applications including the connectivity in underserved and remote areas. In particular, for these applications, satellites are going to play a role in future 5G networks to provide capacity on trains, vessels, aircraft, and for base stations around the globe. In this paper, a 5G edge node concept, developed and evaluated with over-the-air tests using satellites in the geostationary orbit, is presented. The article covers a testbed demonstration study in Europe with a large-scale testbed including satellites and the latest standardization for the network architecture. The main goal of this testbed is to evaluate how satellite networks can be best integrated within the convergent 5G environment. The over-the-air tests for 5G satellite integration in this article are based on a 3 GPP Release 15 core network architecture.  
5G; satellite backhaul; satellite communications, satellite-terrestrial integration 2019
# Investigation of 2D-WH/TS OCDMA Code Stability in Systems with SOA-Based Device
## 5 Conclusions  
The impact of SOA-induced wavelength redshift on 2D-WH/TS multi wavelength picosecond code carriers was investigated for the first time. For the SOA bias current of 50 to 250 mA, the amount of induced redshift was found to be 0.08 to 0.8 nm. Next, the detrimental effect of 0.8 nm redshift on (8, 53)- and (4, 53)-WH/TS OCDMA systems was investigated. It can be stated that, when the amount of SOA-induced redshift is equal to the code carriers' wavelength channel spacing, then a (\(w\), \(N\))-WH/TS OCDMA system will only perform as a (\(w-1\), \(N\))-WH/TS OCDMA system to maintain the \(Pe\) of \(10^{-9}\)  
Figure 8: Probability of error as a function of K simultaneous users for a (4, 53)(3, 53) and (8, 53)/(7, 53) 2D-WH/TS OCDMA system without/with the deployment of an SOA, respectively, the latter causing a one channel code carriers’ redshift.  
**Author Contributions:** Conceptualization, U.A.K. and A.L.S.; formal analysis, M.A., A.L.S. and I.G.; investigation, M.A. and U.A.K.; Methodology, M.A.; resources, I.G.; Software, M.A. and W.C.K.; supervision, I.G.; validation, M.A., W.C.K. and I.G.; writing--original draft, M.A.; writing--review & editing, A.L.S., W.C.K. and I.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the European Union's Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie under Grant 734331.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Territorial Continuity of Care for Patients with Spinal Cord Injury: A Focus Group Study
###### Abstract  
Spinal cord injury (SCI) represents a complex pathological event, which requires continuous and highly specialized territorial interventions. The literature highlights disadvantages faced by people with SCI in accessing local services. This study aimed to investigate the concept of territorial continuity for SCI, reconstructing patients and healthcare professionals' perspectives, to identify facilitators, barriers and opportunities. Methods: a Focus Group (FG) qualitative study was conducted at Piacenza Ausl; four FGs were carried out; a research team member moderated FGs using thematic guides, while an observer was responsible for noting non-verbal aspects. Meetings lasted between 90 and 120 min and were audio-recorded. Transcript analysis involved the identification of units of meaning, which were grouped into general themes. Results: Final analysis highlighted 435 verbatim, grouped into 21 initial themes, that converged into 5 emerging themes: \"SCI continuity as multidisciplinary teamwork\"; \"The need for rehabilitation as a driver of territorial continuity\"; \"Reinventing everyday life after SCI\"; \"Barriers and facilitators\"; and \"The future of territorial care\". Discussion: Patients and healthcare professionals highlighted the absence of a defined treatment path and the lack of reference points, thus generating disorientation and need for information. Among the proposals, telemedicine, empowering the case manager's role, promoting specific training courses and rethinking community hospitals were supported.  
spinal cord injury; continuity of care; Focus Group; qualitative study; territorial continuity; long-term outcome
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 3 Materials and Methods
### Network Training Method  
The input data structure of each training is shown in Figure 3. The size of the input data is \(m\times s\times N\), where \(m\) is the batch size, namely the number of samples for each training. The size of each sample \(F_{i}\) is \(1\times s\times N\) where s represents s historical moments, \(N\) represents the N-dimensional trajectory features and \(N=15\). In Figure 3, \(1\leq i\leq m,1\leq j\leq s\), where \(i\) represents the \(i\)th sample and \(j\) represents the \(j\)th moment.  
Figure 3: The input structure of the CNN prediction model.  
In order to eliminate the effect of index scale, the input features of the model are normalized by dividing the maximum value, that is \(f_{i}=f_{i}/f_{\text{max}}\), where \(f_{i}\) is the \(i\)th feature, and \(f_{j\text{max}}\) represents the maximum value of \(f_{i}\). The normalized matrix is used as the input of the model.  
The true position at the \(s\) moment is denoted by \(Y=(\lambda,\varphi)\). The label value \((p_{1},p_{2})\) of the model output is calculated as Equation (15):  
\[\left\{\begin{array}{l}p_{1}=\frac{\lambda}{180}\\ p_{2}=\frac{\varphi}{90}\end{array}\right. \tag{15}\]  
The actual output of the model is represented by \((y_{1},y_{2})\). Since the \(\tanh\) activation function is used in the output layer of the network model, the final predicted position \((\hat{\lambda},\hat{\varphi})\) is calculated as Equation (16):  
\[\left\{\begin{array}{l}\hat{\lambda}=180y_{1}\\ \hat{\varphi}=90y_{2}\end{array}\right. \tag{16}\]  
The network model is trained to minimize the loss function, which includes mean square error and L2 regularization as Equation (17):  
\[Loss=\frac{1}{m}\sum_{i=1}^{m}\left(\left(y_{1i}-p_{1i}\right)^{2}+\left(y_{2 i}-p_{2i}\right)^{2}\right)+c\|\theta\|^{2} \tag{17}\]  
where \((p_{1i},p_{2i})\) represents the label value of the \(i\)th sample, and \((y_{1i},y_{2i})\) represents the model output value of the \(i\)th sample. \(c\) is the regularization coefficient of weight parameters to avoid overfitting.  
When the loss function decreases to convergence, the prediction model is accomplished, which can be used for real-time trajectory prediction. The overall process of position prediction based on deep learning is shown in Figure 4.
## 3 Results
### Analysis of the VOCs Under Different Storage Temperatures by GC-IMS
#### 3.4.2 Analysis of Fingerprint Spectrum
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Category** & **No.** & **Compound** & **CAS\#** & **MW** & **RI** & **RT [sec]** & **Dt [a.u.]** & **Aroma Characteristics** \\ \hline Ketones (4) & 15 & Cyclopentanone & 120-92-3 & 84.1 & 783 & 261.57 & 1.32827 & Mint \\  & 16 & 2-Pentanone & 107-87-9 & 86.1 & 700.5 & 190.91 & 1.39642 & Spicy, Fruity \\  & 17 & 3-Pentanone-M & 96-22-0 & 86.1 & 695.7 & 187.418 & 1.11127 & - \\  & 3-Pentanone-D & 96-22-0 & 86.1 & 695.7 & 187.418 & 1.11127 & - \\  & 18 & 1-Penten-3-one-D & 1629-58-9 & 84.1 & 682.8 & 179.511 & 1.30614 & Tangering, Onion \\  & 1-Penten-3-one-M & 1629-58-9 & 84.1 & 682.8 & 179.511 & 1.30614 & Tangering, Onion \\ Aldehydes (4) & 19 & 1-Hexanal & 66-25-1 & 100.2 & 798.2 & 276.055 & 1.28327 & Fruity, Vegetable aroma \\  & 20 & 2-Hexanal & 505-57-7 & 98.1 & 858.9 & 342.224 & 1.17677 & Leaf aroma \\  & 21 & 3-Methyl butanal & 590-86-3 & 86.1 & 654.6 & 165.965 & 1.40331 & Chooclate, Cocoa, \\  & 22 & (E)-2-Pentenal-M & 1576-87-0 & 84.1 & 749.3 & 230.009 & 1.10366 & - \\  & & (E)-2-Pentenal-D & 1576-87-0 & 84.1 & 749.3 & 230.009 & 1.10366 & - \\ Furans (2) & 23 & 2-Butylfuran & 4466-24-4 & 124.2 & 893.4 & 386.729 & 1.17853 & - \\  & 24 & 1-(2-Furanyl)-ethanoneone- & 1192-62-7 & 110.1 & 912.1 & 415.535 & 1.12334 & Fruity, Sweet \\  & & 1-(2-Furanyl)-ethanoneone- & 1192-62-7 & 110.1 & 912.1 & 415.535 & 1.12334 & Fruity, Sweet \\  & & D & 112-Dimethoxyethane & 110-71-4 & 90.1 & 635.4 & 157.328 & 1.09696 & Pungent odor, Sweet \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In summary, IT storage can be applied to the postharvest handling and storage of fresh blue honeysuckle to retain the original flavor of the fruits, while FT storage has shown that blue honeysuckle can be used for flavoring by adding it to cold drinks such as ice cream.
# Enhancing Task-Oriented Dialogue Modeling through Coreference-Enhanced Contrastive Pre-Training
###### Abstract  
Pre-trained language models (PLMs) are proficient at understanding context in plain text but often struggle with the nuanced linguistics of task-oriented dialogues. The information exchanges in dialogues and the dynamic role-shifting of speakers contribute to complex coreference and interlinking phenomena across multi-turn interactions. To address these challenges, we propose **C**oreference-**E**nhanced **C**ontrastive **Pre-**training (CECPT), an innovative pre-training framework specifically designed to enhance dialogue modeling. CECPT utilizes unsupervised dialogue datasets to capture both semantic richness and structural coherence. Our experimental results demonstrate that the CECPT model significantly outperforms established baselines in three critical applications: intent recognition, dialogue act prediction, and dialogue state tracking. These findings suggest that CECPT is more adept at following the information flow within dialogues and accurately linking statuses to their respective references.  
dialogue system; contrastive pre-training; graph modeling +
Footnote †: journal:  
pre-training on dialogue structures. These complementary representations collectively enhance dialogue modeling, resulting in improved performance in downstream tasks. Our experimental findings demonstrate that our pre-trained dialogue model, CECPT, outperforms strong baseline models across three key dialogue applications. This underscores the importance of leveraging comprehensive information in dialogue modeling. Furthermore, our evaluation analysis indicates that coreference-aware models excel at tracing information flow among conversational participants and accurately associating statuses with corresponding mentions.  
Our contributions can be summarized as follows:  
* We introduce an innovative dialogue modeling framework that leverages contrastive pre-training and explicitly integrates coreference-guided information.
* Our approach encompasses both semantic and structural aspects by incorporating coreference information into the pre-training process within a unified framework.
* To the best of our knowledge, this study marks the first exploration of dialogue modeling both semantics and structure through graph contrastive training, yielding promising results across several downstream tasks.
## 5 Discussion
### RQ2: VR Games to Support Cultural Heritage  
Concerning interaction in VR gaming environments, it is important that players have meaningful choices. The game structure and rules should allow players to have an effect on events and influence the game outcomes. Players need to clearly understand how their actions change the outcomes and believe they have some control over the game. It is also important to find a balance between too much or too little ambiguity. The game should have surprise elements but not to the point where the player believes that she has no control. This implies that the elements of the game work in combination to create a cultural heritage environment that is appropriately challenging, but which players still perceive as fair and equitable, balancing fun and learning. Sometimes finding the right balance between a game that is too hard or too easy to play is difficult. In addition, such games need to find a balance between gaming elements and cultural content that respects historical and cultural data.  
VR games in the field of cultural heritage can be used to increase its sustainability. When used within a specific sustainability strategy, the games can have maximum effect [81]. UX methodologies are now mature enough to be used in cultural technologies and VR games in order to provide smooth visitor experiences and to allow useful evaluation of visitor experiences [82]. In addition, the field of games in cultural heritage is expanding, with new types of games emerging, like cinematographic videogames [83]. It is important to see where VR games stand compared to other types of games for cultural heritage and explore what is cost effective, maximizes visit effects, increases learning and engagement, etc. Moreover, it is also important to consider the physical aspects of VR games, such as the equipment used, as well as where they will be played (e.g., at the museum, at people's homes) ([80], p. 8). Finally, it is crucial to consider organizational issues around the use of VR games in cultural heritage, such as possible organization resistance issues, acceptance levels, as well as the mode of use (i.e., whether we will isolate the individual in the virtual world or we will support virtual social interactions). As previous study reports, the spectatorship experience for VR games differs strongly from its non-VR precursor [84]. The immersive full-body interaction is a crucial part of the player experience. Bystanders and viewers prefer the first-person version, which allows them to better focus on in-game actions and experience greater involvement [84].
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 5 Conclusions and Future Works  
The high velocity of UEs and the high density of SCs in a 5G V2X network have ushered in significant challenges in maintaining stable connections and efficient handovers. Frequent connection transfers between SCs due to high speeds lead to a high number of unnecessary HOs and HO failures, while increasing latency and packet loss. This study demonstrated that integrating TOPSIS with ST  
Figure 14: Average energy expenditure against velocity and SC density.  
Figure 13: Comparative analysis of packet losses and latency vs. various SC densities.  
and Q-Learning in IHD-V2X can efficiently manage HOs in high-velocity and high-density network environments. The deployment of this mechanism results in the prevention of unnecessary handovers by effectively predicting optimum transitions that reduce signaling overhead and, consequently, improving the successful handover by learning prior HO experiences and adapting to changing requirements and conditions. Furthermore, this method involves the minimization of packet losses and latencies, which significantly improves application-specific requirements. Moreover, it is incorporated in the optimization of energy usage by lowering unnecessary consumption in highly mobile and highly dense networks.  
Future research directions include exploring AI-driven mobility prediction models to further enhance the network performance and scalability in next-generation V2X implementations. Realistic environmental constraints can be considered to enhance the algorithm.  
Conceptualization, F.R.A.A.H., A.T., N.A. and F.A.S.; Methodology, F.R.A.A.H. and A.T.; Software, F.R.A.A.H.; Validation, F.R.A.A.H.; Formal analysis, F.R.A.A.H., A.T. and F.A.S.; Investigation, F.R.A.A.H., A.T., N.A. and F.A.S.; Data curation, F.R.A.A.H.; Writing--original draft, F.R.A.A.H.; Writing--review and editing, F.R.A.A.H., A.T. and F.A.S.; Supervision, A.T., N.A. and F.A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# Development of Rockburst Research: A Comprehensive Review
## 5 Rockburst Intensity Classification and Prediction
### Microseismic Monitoring Technology for Rockburst Prediction  
Rockburst is a complex process of rock fracture. Data of microseismic monitoring contain the information of stress and fracture development trends in rock, which are used to establish a microseismic early warning system in engineering. Table 5 lists the commonly used microseismic indicators for rockburst prediction.  
[PERSON] et al. proposed a microseismic multiparameter prediction method based on apparent volume, energy, spatial correlation length, fractal dimension and \(b\) value to predict large deformation and rockburst [4]. [PERSON] et al. used real-time microseismic data and a rockburst early warning formula in the rockburst risk of the Jinping II hydropower station deep tunnel during excavation, which focused on a rockburst database, with a selection of typical rockburst cases [113].  
[PERSON] et al. summarized the characteristics, mechanisms and roles of geological structures of rockburst according to the rockburst events counted in engineering cases and then used microseismic monitoring technology to reveal the relationship between the temporal and spatial evolution of microseismic activity and rockburst [70].  
When rockburst is about to occur, the energy of microseismic events presents a temporary fractal behavior. The aggregation and spatial distribution self-similarity of microseismic events can be used to warn the of location of a potential rockburst, and the temporary distribution of measured energy and the number of microseismic events can be used to warn of the severity of rockburst [89,113,114].  
Based on in situ microseismic monitoring and acoustic emission test data of laboratory rock samples, [PERSON] et al. established a fuzzy comprehensive evaluation model to predict the occurrence of rockburst [115]. [PERSON] et al. established a multiparameter microseismic index system of coal dynamic failure, including bursting strain energy (BSE) index, time-space-magnitude independent information (TSMII) indices and time-space-magnitude compound information (TSMCI) indices, and then quantitatively analyzed prewarning rockburst risk in the Huijahe Coal Mine (China) via introducing the R-value scoring method to calculate the weights of each index [116].
# Corrosion Assessment of Zr2.5 Nb Alloy in Ringer's Solution by Electrochemical Methods
## 3 Results and Discussions
### Potentiodynamic Polarization  
Potentiodynamic polarization testing is one of the most widely used techniques for assessing the corrosion resistance of a material. The potential difference was measured between the reference electrode and the working electrode. We applied the difference between the two electrodes and measured the current. The measured current indicates the corrosion rate that occurs on the working electrode in terms of current per unit area, known as current density. This method determines the domains of the active/passive potentials and helps to understand the different processes that take place on the surface of the tested sample [21].  
For the Zr2.5 Nb alloy, the potentiodynamic polarization test was performed in a potential range from \(-\)1.5 V vs. Ag/AgCl at +1.2 V vs. the reference electrode with a scanning rate of potential equal to 3 mV/s. The potentiodynamic polarization curve of the Zr2.5 Nb alloy immersed in [PERSON]'s solution is shown in Figure 6.  
Figure 6 shows that the passive domain for the zirconium alloy immersed in the [PERSON]'s solution after approximately 39 h of immersion is on a large domain of potential between \(-\)856 \(\pm\) 37 mV to \(+\)986 \(\pm\) 31 mV vs. Ag/AgCl, having a \(\Delta\)E = 1842 mV. In general, if the passive range covers a larger potential range and the value of the passivation current is lower, then the material has a higher corrosion resistance and the ability to passivate in the tested solution [18].