esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Preparedness  
Preparedness refers to the development of policies and protocols, incident command systems, training, planning, coordination, and public awareness for potential emergencies. Simulation software may be used for analysis and training [13; 14], while disaster databases and GIS systems may help identify evacuation routes, shelters and relevant resources [15].
## 5 Conclusions and Future Work  
In this paper, we proposed a new GraphDIVA model. This model was based on the variational reasoning DIVA model, which was analyzed from the two dimensions of the network model structure and calculation process. It introduced the GraphSAGE algorithm into the path reasoning module. It was used to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. The GraphDIVA model reduced the time required for model training while improving the accuracy rate, thereby realizing the feature perception of the adjacent graph structure and enhancing the learning and reasoning capabilities of the model. We trained the model on a public dataset. The experiments then established the efficiency and effectiveness of the model, which proved that our method had a better accuracy rate and training time than the baseline models using several benchmark datasets such as FB15k-237 and NELL-995.  
By introducing the GraphSAGE algorithm into the path reasoning module, this paper solved the problem of the DIVA model's inability to perceive the structural features of graphs. Although the performance of the new model was improved compared with the original model, the path search module became a new potential performance bottleneck. Because the path search can only realize a one-dimensional path representation, the vision of the path reasoning module is limited to only the narrow subnet centered on the path. Therefore, an essential future research direction will be further adjusting the architecture of the variational reasoning model to expand the range of graph features it can perceive.  
H.T.: literature research, data processing, methods, and writing; W.T.: funding acquisition, innovation analysis, and supervision; R.L.: literature research and experimental analysis; Y.W.: innovation analysis, project management, and supervision; S.W.: supervision and writing review; L.W.: innovation analysis, supervision, and writing review. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China, Grant No. 61901016.  
Figure 5: (**a**) Depiction of FB15K-237 best result training epoch; (**b**) depiction of NELL-995 best result training epoch.
## 5 Conclusions  
The present paper investigated the effects of two additives (ZycoTherm--liquid, warm mix additive; Titan 7205--solid, high-temperature-improving additive) used simultaneously, on the contents of carbonyl, sulfoxide, polybutadiene and polystyrene FTIR chemical indices in a 50/70 paving-grade bitumen and 45/80-55 polymer-modified bitumen. Additionally, the high-temperature performance of the asphalt binders was assessed using a direct shear rheometer, and relationships between these characteristics were evaluated. The major findings of this study include the following:  
* The simultaneous use of both additives significantly decreased the FTIR indices related to carbonyl species in the asphalt binders before and after RTFOT ageing; the decrease amounted up to 90% in relation to the levels seen in non-aged binders without additives; and these effects were far less pronounced after PAV ageing.
* The sulfoxide FTIR indices in non-aged binders were affected to a similar extent (a decrease of up to 37% in relation to the levels seen in non-aged binders without additives), but the driving factor in this change was the ZycoTherm warm mix additive; the values of this I\({}_{\text{S=O}}\) index after PAV ageing were less affected; and the Titan 7205 significantly increased the I\({}_{\text{S=O}}\) index in both non-aged binders when used alone (up to 72% increase).
* The changes in the polybutadiene and polystyrene indices in the polymer-modified bitumen did not exceed 27%; small increases and decreases (up to \(\pm\)15%) of these indices were seen in non-aged and RTFOT aged binders; and after PAV ageing, both I\({}_{\text{PB}}\) and I\({}_{\text{PS}}\) indices clearly decreased when more of the additives were used.
* Although the additives clearly affected the high-temperature performance of both asphalt binders, the evolution of these rheological properties due to RTFOT ageing was not affected despite their significant effects on the FTIR indices.  
Based on these findings, it can be concluded that estimation of the rheological performance based on known relationships with carbonyl and sulfoxide contents may not be reliable in the case of asphalt binders containing these investigated additives.  
Further studies should be conducted to evaluate the performance of these additives and their effects on performance and FTIR responses in the scope of more severe ageing protocols.  
Conceptualization, K.M. and A.C.-K.; data curation, J.B.; investigation, K.M. and J.B.; methodology, K.M.; resources, A.C.-K.; supervision, A.C.-K.; visualization, K.M. and J.B.; writing--original draft, K.M. and J.B.; writing--Review and editing, K.M. and A.C.-K. All authors have read and agreed to the published version of the manuscript.
## 2 System Model
### Energy Arrival and Primary User Models  
In this paper, the CUs have a finite capacity battery, \(E_{bat}\), which can be constantly recharged by the solar energy harvesters. Therefore, the CUs can perform their other operations and harvest solar energy simultaneously. For many reasons (such as the weather, the season, different times of the day), the harvested energy from solar resources may vary in practice. Herein, we take into account a practical case, where the harvested energy of \(CU_{i}\) in NOMA group \(G_{m}\) (denoted as \(e_{im}^{h}\)) follows a Poisson distribution with mean value \(\xi_{avg}\), as studied in [37]. The arrival energy amount that \(CU_{im}\) can harvest during time slot \(t\) can be given as \(e_{im}^{h}\left(t\right)\in\left\{e_{1}^{h},e_{2}^{h},...,e_{v}^{h}\right\}\) where \(0<e_{1}^{h}<e_{2}^{h}<...<e_{v}^{h}<E_{bat}\). The cumulative distribution function can be given as follows:  
\[F\left(e_{im}^{h}\left(t\right);\xi_{avg}\right)=\sum_{k=0}^{e_{im}^{h}\left( t\right)}e^{-\xi_{avg}}\frac{\left(\xi_{avg}\right)^{k}}{k!}. \tag{6}\]  
Herein, we use a two-state Markov discrete-time process to model the state of the primary channel, as depicted in Figure 3. We assume that the state of the primary channel does not change during the time slot duration, \(T_{tot}\), and the primary channel can switch states between two adjacent time slots. The state transition probabilities between two time slots are denoted as \(P_{ij}\left|i,j\in\left\{F,B\right\},\right.\) in which \(F\) stands for the _free_ state, and \(B\) stands for the _busy_ state. In this paper, we consider cooperative spectrum sensing, in which all CUs collaboratively detect spectrum holes based on an energy detection method, and they send these local sensing results to the CBS. Subsequently, the final decision on the primary users' activities is attained by combining the local sensing data at the CBS [36]. The performance of the cooperative sensing scheme can be evaluated based on probability of detection \(P_{d}\) and probability of false alarm \(P_{f}\). \(P_{d}\) is denoted as the probability that the PU's presence is correctly detected (i.e., the primary channel is actually used by the PUs). Meanwhile, \(P_{f}\) is denoted as the probability that the PU's is detected to be active, but it is actually inactive (i.e., the sensing result of the primary channel is busy, but the primary channel is actually free).
## 5 Discussion
### Calibration Coefficient  
In the equation of radar transmission loss, we established a calibration coefficient to balance the intensities between backscattering and the grazing angle direction. However, the precise setting of this coefficient is not possible. We observed that this correction resulted in an increase in error as the grazing angle (which is related to scattering angles) increased. Although we believe that the scattering coefficient setting is somewhat excessive, we have no other reasonable assumption except to refer to the coefficient of Fresnel reflection in the absence of a clear physical model. Furthermore, it is necessary to correct for the application of the ITU-R recommendations to radar. As the radar elevation increases, the angular dependent loss in the ITU-R recommendations increases due to the decrease in the transceiver shared scattering volume. However, the radar transceiver shared volume remains constant. Therefore, as the elevation angle increases, the transmission loss of the ITU-R recommendations rapidly increases. Although the change in radar transmission loss is limited, the trend of the two changes with distance is similar. Therefore, we can see in Figure 18 that the radar transmission loss at an elevation of 1.45\({}^{\circ}\) is farther from the ITU-R recommendation than the radar transmission loss at an elevation of 0.5\({}^{\circ}\). As the elevation angle of the radar increases, this difference becomes more apparent. Thus, when applying the recommendations of the ITU-R, it is necessary to consider the overestimation caused by volume loss at the large radar elevation.
## Appendix S3 _symmetry_
### Article
###### Abstract  
Symmetry in nodes operation in underwater wireless sensor networks (WSNs) is crucial so that nodes consume their energy in a balanced fashion. This prevents rapid death of nodes close to water surface and enhances network life span. Symmetry can be achieved by minimizing delay and ensuring reliable packets delivery to sea surface. It is because delay minimization and reliability are very important in underwater WSNs. Particularly, in dense underworks, packets reliability is of serious concern when a large number of nodes advance packets. The packets collide and are lost. This inefficiently consumes energy and introduces extra delay as the lost packets are usually retransmitted. This is further worsened by adaptation of long routes by packets as the network size grows, as this increases the collision probability of packets. To cope with these issues, two routing schemes are designed for dense underwater WSNs in this paper: delay minimization routing (DMR) and cooperative delay minimization routing (CoDMR). In the DMR scheme, the entire network is divided into four equal regions. The minor sink nodes are placed at center of each region, one in each of the four regions. Unlike the conventional approach, the placement of minor sink nodes in the network involves timer based operation and is independent of the geographical knowledge of the position of every minor sink. All nodes having physical distance from sink lower than the communication range are able to broadcast packets directly to the minor sink nodes, otherwise multi-hopping is used. Placement of the minor sinks in the four regions of the network avoids packets delivery to water surface through long distance multi-hopping, which minimizes delay and balances energy utilization. However, DMR is vulnerable to information reliability due to single path routing. For reliability, CoDMR scheme is designed that adds reliability to DMR using cooperative routing. In CoDMR, a node having physical distance from the sink greater than its communication range, sends the information packets by utilizing cooperation with a single relay node. The destination and the relay nodes are chosen by considering the lowest physical distance with respect to the desired minor sink node. The received packets at the destination node are merged by fixed ratio combining as a diversity technique. The physical distance computation is independent of the geographical knowledge of nodes, unlike the geographical routing protocols. This makes the proposed schemes computationally efficient. Simulation shows that DMR and CoDMR algorithms outperform the counterpart algorithms in terms of total energy cost, energy balancing, packet delivery ratio (PDR), latency, energy left in the battery and nodes depleted of battery power.  
**Keywords:** delay minimization; network reliability; delay minimization routing (DMR); cooperative delay minimization routing (CoDMR); underwater WSNs
## 1 Introduction
Through a series of comprehensive experiments, ABSA exhibits significant enhancements in predictive capabilities, and the GRU model with ABSA achieves the highest performances of RMSE, MAE, and R\({}^{2}\) among all, which may establish a benchmark in airfare forecasting methods by thorough empirical validation. The key contributions of this paper are summarized as follows:  
* ABSA-driven airfare forecasting approach: This study proposes using deep learning models with ABSA. This methodology is a shift from classical machine learning models, making use of both ticket transaction data and sentiment data to enhance predictive performance. The proposed model steps forward in airfare forecasting;
* Aspect extraction and correlation analysis: It involves extracting key service aspects from customer reviews and performing correlation analysis to see the correlations among the aspects and the airfare. It not only reveals how different service elements affect the customers' perceptions and airfares but gives valuable information for improving the airline services;
* Aspect categorization and sensitivity analysis: The aspects are categorized into nine aspect groups, and accordingly, sensitivity analysis was conducted to determine how each aspect group impacts the airfare. This analysis provides deeper business insights into customer preferences and sentiments. The rest of this paper is organized to provide a comprehensive understanding of our study. Section 2, Related Work, examines existing research and sets the theoretical foundation for our approach. Section 3, Methodology, is subdivided into three parts, namely datasets and preparations, ABSA, and the GRU model, detailing the procedures and techniques employed in our research. In Section 4, Results and Discussion, we present and analyze our findings, exploring their implications and significance. finally, Section 5, Conclusions, summarizes the study, reflecting on its contributions and potential future directions.
## 2 Theoretical Discussion  
### Dynamic Factor Considering the Stiffness or Modulus of the Track  
Dynamic factor is a parameter for track design and dynamic stability evaluation for maintenance and track design [6]. The principle idea of the dynamic factor is to understand, or evaluate, the dynamics of the railway track structure, where the load caused or applied by the moving train on the track is compared to the load expected by the load capacity of the track (represented by the static load of the vehicle design specification) in a form of a coefficient. Research trends related to this subject are as follows. Researchers in University of Illinois Urbana-Champaign shows various ways of calculating dynamic factors in all over the world [7]. Also, they have conducted the calculation to derive the dynamic factor depending on the calculation method they mentioned and improved the difference of values they calculated. Refer to Figure 1 and Table 1 below. [PERSON] et al. provides a research regarding optimal track elasticity area in terms of track maintenance cost and vehicle energy consumption and as follows. As the track elasticity is abundant, the track maintenance cost decreases proportionally because the track load is relatively reduced. However, the energy consumption for train operation increases [8]. As such, the elastic change of the track shows opposite characteristic, and it is important to find the optimum point. [PERSON] continues to claim that optimum track elasticity is proposed approximately 65-85 kN/mm [8]. Figure 1 shows the relation between track modulus and power dissipation in 300 km/h high-speed when a vehicle static load is 80 kN. This is because a light rail shows large displacement due to low stiffness which indicates low track modulus. In this regard, if the track modulus is increased by increasing track stiffness, that is, if the rail displacement decreased by increasing the track elastic modulus, it can lead to lower energy consumption. [PERSON] et al. compares a dynamic factor calculation results using data where the vehicle is considered as a moving mass over simply supported girders using FEM analysis and compares the results with those of the standard design practice [9]. [PERSON] et al.
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 3 Protocol and Results
### Test Scenarios and Results  
We center our experiments around two environments: indoors and inside a vehicle. For each scenario, we exchange several pairs of keys between each pair of smartphones (or with an Android head unit in case of the in-vehicle experiments). The results of these experiments are detailed next.
# A Performance Evaluation for Software Defined Networks with P4
## 1 Introduction
### Contributions  
This paper makes the following contribution to improving network performance by utilising the faster packet processing of P4. In particular, we have:  
* Investigated the overhead created due to the slow path utilisation of OvS and the performance variation in comparison to a P4 target switch. With more and more packets requiring processing using a controller, a network model that utilises a slow path approach, such as OvS will potentially lead to an exponential growth in traffic congestion.
* Evaluated the performance of networks when _SDN+P4_ is employed rather than _SDN+OvS_. The evolution of 5G and beyond has led to the need to evaluate methods for reducing the delay at the core. Initialising programmability in the network has been shown to increase performance at the core. To the best of our knowledge, current literature does not evaluate the performance of the network when the control plane and data plane programmability (_SDN+P4_) is employed in comparison to the control plane (SDN+OvS) programmability.  
* For a time-sensitive application with minimal latency requirements, reducing the delay at the core can be essential. For example, Vehicle-to-Vehicle (V2V) and Ultra Reliable Low Latency Communication (URLLC) applications. A solution, that processes packets in parallel as opposed to sequential processing in OvS, has been considered in this research and its effect on performance in applications. Our research has established that with the initialisation of _SDN+P4_ with parallel processing of packets, various applications have better performance in comparison to applications run over _SDN+OvS_.
* Evaluated the quality of applications and the effect _SDN+P4_ had on the network traffic of ICMP, TCP, UDP, SIP and CDN in comparison to _SDN+OvS_. The statistics, such as increased bps and throughput, reduced delay jitter, packet loss, delay, and buffering time, have led to a higher quality of performance at the receiver's end
## 7 Simulation Results and Discussion
### Analysis of Proposed Scheme Results against Existing State of the Art
#### 7.2.1 Fraction of Void Nodes  
Figure 8 depicts the fraction of failure in proposed and baseline schemes. The behaviour of GDGOR-SM shows that when node density is varied from 100-150, the fraction of node failure is decreasing gradually, however, as the quantity of nodes increased to 200, then sudden down fall is observed in the results of Figure 8. Further, after deploying more number of nodes up to 200-500, the trend shows continuous decrease. This scheme has less failure because of mobile sonobuoys which dive into the water from the surface to retrieve data directly and return data to specified destination. Similarly, in GRMC-SM, the trends of decreasing node failure at various node densities are almost similar to GDGOR-SM, however, the failure rate is higher due to the consideration of multihop transmission when mobile sonobuoy is not in range of a node.  
Whereas, AUV-CH and EnOR performance starts declining because in opportunistic routing multiple sensor nodes participate in communication, and the reliability of delivering data is although high but the chances of communication failure are also high in both schemes. On the other hand, EnOR is focusing on rotating the forwarder node and has no mechanism for void avoidance, therefore it has high fraction of void nodes. The GEDAR utilizes sonobuoys which are positioned at the surface of water, whereas, lack of sonobuoys mobility exposes GEDAR scheme to communication failure. Thus, it is evident that 30% nodes lie in the category of void nodes in sparse network in both GEDAR and GDGOR-IA. Thus, the fraction of node failure is high when less number of nodes are deployed in the network and after increasing the number of nodes, it tends to reduce significantly in all the schemes. Fraction of void nodes is reduced in GEDAR and GDGOR-IA by opting depth adjustment mechanism. Whereas, the fraction of void occurrence is more in RE-PBR when the network is sparse because, it is hard to find forwarder node with high link quality along with the highest remaining battery and lower depth node. Moreover, the quantity decreases significantly as the density increasesfrom 150-450. The reason of sudden decrease was the availability of more nodes in the transmission range of source node. As it is illustrated in Figure 8, RE-PBR only beats EnOR, while in other schemes, the mechanism of recovery is available which makes them more effective and efficient in terms of handling energy consumption.
## 6 Simulation Results
### First Stage: One Day Evaluation
#### 6.1.6 Enhanced Linear Exponential Smoothing (ELES) Method  
Simulation results for the ELES method are shown in Figure 16. It is noted that, although results were similar, there was a violation at t = 9:50 for \(\alpha\) = 0.07. A summary of the simulation results for the ELES method is shown in Table 8. The least degradation without having PRR violations occurred when \(\alpha=0.06\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measurement** & \(\mathbf{\omega_{n}}\) **= 1/15** & \(\mathbf{\omega_{n}}\) **= 1/25** & \(\mathbf{\omega_{n}}\) **= 1/35** \\ \hline D(\%) & 0.0063 & 0.0101 & 0.0136 \\ \hline NC(80\%) & 0.6286 & 1.0117 & 1.3634 \\ \hline Violations (mins) & 18 & 0 & 0 \\ \hline SOC (end of the day) -SOC(ref) & \(-\)29.9978 & \(-\)34.8233 & \(-\)32.3034 \\ \hline \(\sigma^{2}(\text{PRR}_{\text{out}})\) & 14.4757 & 4.6293 & 2.4287 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the simulation results for the SOLPF method.  
Figure 15: Simulation results for the SOLPF method. (**a**) One-day data results showing the total output power (\(\text{P}_{\text{out}}\)), the ESS output power (\(\text{P}_{\text{ess}}\)), the PRR at the output (\(\text{PRR}_{\text{out}}\)), and the state of charge (SOC). (**b**) Zoomed figures of the corresponding black squares.