esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber-Security Challenges in Aviation Industry: A Review of Current and Future Trends
## 4 Cyber-Attack Surfaces and Vulnerabilities in the Civil Aviation Industry
### Electronic Flight Bag  
The Electronic Flight Bag (EFB) displays digital documentation, such as navigational charts, operations manuals, and airplane checklists by the flight crew. It can also be used by crew members to perform basic flight planning calculations. Advanced EFB now perform many complex flight-planning tasks and are integrated into flight management systems, alongside other avionic systems, to display the real-time position of an aircraft on navigational charts with weather information [57]. [PERSON], [PERSON] and [PERSON] [73] assert that EFBs are valuable elements as a replacement of traditional paper references carried on-board as part of the flight management system, thus yielding added benefits by reducing weight. Advanced EFBs integrated into flight management systems, in contrast with the traditional paper-based method that were stand-alone, now present a new attack surface, e.g., a malware-infected EFB will gate denial-of-service attacks to other connected on-board systems [57; 73; 74; 75].
# Impact of Virtual Reality on Student Motivation in a High School Science Course
## 5 Discussion  
Using the Mel Chemistry application, this study made it possible to implement a virtual reality experience with 304 fourth semester students of a chemistry subject. Because the concepts studied are abstract and challenging to present in laboratory environments, VR was conducted. Considering that the use of immersive technologies, such as VR, can help to better understand and visualize the information [1], is in this study, we sought to know the motivation that is generated in the student by using emerging technologies for the study of science subjects in high school.  
This research presents a proposal for the use of VR that was carried out with students to identify different organic compounds, who responded to a questionnaire to identify the different dimensions of both intrinsic and extrinsic motivation. The results were mostly positive for all the dimensions, for both types of motivation. These findings agree with studies conducted by other authors. The use of this type of interaction allows students to explore the concepts and increase their motivation to study the subjects [8; 9].  
In the four dimensions suggested by [PERSON] [18] for motivation, this research showed satisfactory results regarding students' opinions about the immersive experience using VR. The dimensions of attention and satisfaction obtained the highest positive percentage, related to intrinsic motivation, which is the most difficult to achieve in educational interventions [29]. This result agrees with other studies, in which virtual reality technology motivates students by providing real experiences and the interest generated by using this immersive technology [8; 17]. The findings also establish that attention was more significant for women than for men, with a significant difference. However, there is a positive response for both genders in this dimension. In this sense, the study of [PERSON] [16] indicates that they found a difference in female students' interest compared to males in the subjects studied using virtual reality. Still, they did not find differences in other variables.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Female (\(n=153\))** & **Male (\(n=151\))** & & \\  & **M(DE)** & **M(DE)** & **t** & \(p\) \\ \hline Attention & 4.11(0.65) & 3.91(0.72) & 2.51 & 0.01 \\ Relevance & 3.76(0.64) & 3.65(0.71) & 1.38 & 0.16 \\ Confidence & 3.76(0.74) & 3.82(0.67) & \(-0.62\) & 0.53 \\ Satisfaction & 4.07(0.80) & 3.95(0.85) & 1.25 & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of Student’s \(t\)-test results by gender.  
In the case of confidence, this was lower for women. Although there were no significant differences between both genders, training is necessary due to the VR program's degree of difficulty for those who are unfamiliar with these methods [13]. So, with more frequent use, students could improve the results in this dimension.
# Topology Optimization of Functionally Graded Structure for Thermal Management of Cooling Plate
## 2 Methodology  
### Heat-Fluid Coupling  
The internal heat transfer process in the cooling plate includes heat conduction in the solid region and convective heat transfer in the fluid region. The convective heat transfer part is further influenced by the fluid flow. Therefore, it is necessary to analyze the conjugate heat transfer process involving both heat conduction and convective heat transfer, as well as the fluid flow process simultaneously.
## 3 SAMD Model for Time-Series Clustering
### Timespan Processing  
The timespan interval (\(S_{1}\) and \(S\), as shown in Figure 2) is a very important measurable parameter in determining the quality of density clustering because it affects the initialization of the density radius. If the timespan interval is not suitable for the spacing of the y-axis, it will lead to the clustering results being non-ideal. If the timespan interval selected is too large, it will lead to more vertical space clusters. For instance, the x-axis spacing is represented as a time series, such as 5 min or 10 min. If the timespan interval is too large, such as \(S_{1}\), the results of the density clusters will deviate from the actual cluster results, and some vertical-space, little clusters could be generated. If the suitable timespan interval (\(S\)) can be mined, these little clusters, such as \(A_{1}\), \(A_{2}\), \(A_{3}\), and \(A_{4}\), could be merged into one cluster, \(A\). This is the reason why we need to consider the timespan to suit the dimension of density clustering; it is possible for it to have a great impact on the clustering quality because of proportional variation and the position offset. The time interval transformation is shown in Figure 3.  
**Definition 1**.: _Unmapped sample points are indicated as \(\mathrm{\,SK}=\{(x_{1},\,y_{1}),\,(x_{2},\,y_{2})\,\ldots\,(x_{i},\,y_{i})\, \ldots\,(x_{n},\,y_{n})\}\), which are distributed among dimensional, non-uniform space. The weighted average distance is selected to adjust the scale of the x-axis, and the sample points are mapped to \(\mathrm{\,SRX}=\{(x_{r1},\,y_{1}),\,(x_{r2},\,y_{2})\,\ldots,\,(x_{ri},\,y_{i}) \,\ldots,\,(x_{m},\,y_{n})\}\) through the mapping relation \(P1\!\!\rightarrow\!\!P2\), after spacing conversion._  
Figure 2: Timespan interval (such as \(S_{1}\) and \(S\), et al.) selection.
## 4 Computational Experiments and Analysis
### Analysis of Composite Scattering Coefficient as a Function of Incident Angle  
Figure 8 shows the computed results of the composite scattering coefficient \(\sigma\) as a function of the incident angle, under the conditions of \(\theta_{i}=30^{\circ}\), \(40^{\circ}\), \(50^{\circ}\). Take \(\theta_{i}=30^{\circ}\) as \(\theta_{1}\), \(\theta_{i}=40^{\circ}\) as \(\theta_{2}\), \(\theta_{i}=50^{\circ}\) as \(\theta_{3}\).  
As shown in Figure 8, \(\sigma\) exhibits oscillatory variation with the scattering angle and shows a scattering enhancement effect in the specular reflection direction. \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) each exhibit distinct peaks at \(\theta_{s}=-50^{\circ}\), \(-40^{\circ}\), \(-30^{\circ}\). This phenomenon is attributed to the significant influence of the rough vehicle surface on the scattering coefficient. Although \(\theta_{1}\) and \(\theta_{2}\) show relatively small differences, both differ markedly from \(\theta_{3}\). Overall, \(\sigma\) tends to decrease with increasing incident angle across most of the angular range.
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 5 Result and Discussion
### Overall Performance Evaluation  
In addition, the performance of all possible scenarios should be evaluated by a joint consideration of the following six indicators (see Figure 13): the number of DCs, total travel time, total travel distance, standard deviation of travel distance, standard deviation of travel time, and number of required trucks. Given that all indicators have different units, we compared them with the relative score, which was obtained by dividing the score of scenarios with the maximum value among them.  
Among eight scenarios, scenario 1 as the existing DC showed the worst performance in five indicators except for the number of DCs (see Figure 13a). Interestingly, even though we assumed a different number of DCs, the number of required trucks of all scenarios was almost identical. It is clear that alternative scenarios can guarantee a better performance in travel time, distance, and fairness among drivers as compared to scenario 1. Therefore, in order to choose the best scenario to apply to the practical transportation, we compared the performance among only alternative scenarios. The results are shown in Figure 13b.  
In summary, the results can be interpreted in three aspects. First, when the budget of a new facility investment was considered to be a top priority, scenario 3 should be selected as the best one, as it requires the minimum level of resources, number of DCs (2 DCs), and the number of required trucks (70). However, due to the longer travel time and distance, scenario 3 was found to require more operating costs. Second, when the operating costs were considered as a top priority, scenario 8 had the best score of the total travel time and travel distance. Finally, scenario 7 showed the best score in terms of fairness among drivers based on STDEV of time and distance.  
Figure 12: The fairness index (standard deviation) of travel time and distance.  
Figure 13: **(a)** comparison with different indicators, all possible scenarios; **(b)** comparison with different indicators, only alternative scenarios.
# SOPRENE: Assessment of the Spanish Armada's Predictive Maintenance Tool for Naval Assets
## 4 Unified Architecture
### Failure Diagnose  
The diagnostic block is responsible for determining the failure mode by using the prediction data and the evaluation of the anomaly detection block when it occurs and the probability of each failure mode. In order to establish the probability, a set of Multilayer Perceptron [62] classifiers is used, which ranges from one to two hidden layers. In all the approaches, the number of neurons of the first hidden layer's neurons triples the number of variables and decreases linearly until the output layer. In addition, RELU activation functions were used in all the hidden neurons, while sigmoid activations were used in the output layer to obtain the probabilities of the failures. Since labeled datasets corresponding to all possible failure modes are not available to train the perceptron, the decision was made with respect to implementing an artificial data generator in order to produce them. By studying the theoretical characteristics of the failure modes included in the FMECA document of the engine (variables involved, nominal values, range top values, etc.), the generator allows the creation of normal and anomalous datasets.  
The system employs a perceptron to classify each failure mode. Each perceptron is trained with one failure mode associated data; thus, when new data arrives, it issues the probability of occurrence of this failure mode. In this manner, each perceptron receives in its input layer the state of an engine (as many neurons as sensors) and emits in its output layer the probability of occurrence of the failure mode for which it has been trained (one neuron).  
This classifier is used in combination with the mask generated by the anomalies detection block to perform the engine diagnostics. The utility of the failure mask is to narrow down the number of possible failure modes. Given a data record of an instant of time, for which the anomaly mask contains \(m\) variables marked as anomalous and \(n\) marked as normal, only the failure modes in which any of the \(m\) take part in will be considered as possible anomalous variables. In this manner, the failure modes in which all the variables involved have been considered normal by the anomaly detector will be omitted.
# Utilization of ICT as a Digital Infrastructure Concerning Disaster Countermeasures in Japan
## 3 Change in the Information Communication Environment in Japan from the 1990s Onward
### Changes in the Information Communication Environment from the 1990s Onward  
Figure 1 shows the penetration rate of the PC, internet and mobile information terminal over the last 20 years (1993-2012). The great Hanshin-Awaji earthquake was in 1995, the Mid Niigata Prefecture Earthquake in 2004 and the great East Japan earthquake in 2011. As shown in Figure 1, the progress of the advanced information communication network is significant since the great Hanshin-Awaji earthquake up until the present day, and has greatly changed the information communication environment surrounding the general public. At the time of the great Hanshin-Awaji earthquake, the internet was used from PCs and mobile information terminals were also becoming increasingly common. In contrast, the information communication environment today is more complex and anyone can easily spread information, as the internet can be accessed from mobile information terminals such as smartphones and tablets in addition to PCs.  
According to the data for recent years, which is obtained from the White Paper on Information and Communications 2016 [28], due to the restrictions on obtaining data, the penetration rate in 2015 was 95.8% for mobile phones and PHS, 72.0% for smartphones, 76.8% for PCs and 33.3% for tablets. The rapid spread of mobile information terminals such as smartphones and tablets in recent years has diversified communication methods, and enabled individuals to easily send and receive interactive information right then and there as well as search for information.
## 2 Related Work
### Reinforcement Learning Based Routing Protocol  
With the development of artificial intelligence, RL technologies have shown prominent advantages in dynamic routing optimization. Some works utilize deep RL to autonomously learn routing decisions, which causes nodes to have independent processing capabilities, and full intelligence to handle complex tasks and understand an environment. The paper of [PERSON] and [PERSON] [34] is a groundbreaking work that used RL in a communication network. After its publication, many studies followed the original RL idea to improve the routing performance according to different features of networks and performance requirements [35; 36; 37]. The QGrid [38] routing protocol divides the whole network into different grids then selects the next hop with the optimal grid which can avoid areas and achieve the optimal distance to the destination, but QGrid does not consider link status, which may cause a suboptimal solution. To settle this problem, the authors of [39] proposed a Q-learning-based geographic routing protocol (QGeo), which considers the packet link error and location error to improve the packet delivery rate. However, the limitations of the paper are that its methods present difficulties concerning convergence when Q-table is very large. In [40], the authors proposed a hybrid routing protocol which proposed position prediction to overcome the directional deafness problem in the MAC layer and, furthermore, they proposed a Q-learning routing protocol in a network layer, but it has the same routing problems as QGeo. Therefore, it cannot be applied to large-scale networks. In [41], the authors proposed a full-echo Q-routing algorithm with simulated annealing interference (FEQSAI) to adaptively control the exploration rate of the algorithm. It is an online learning algorithm that learns by adjusting the adaptive temperature parameter which is related to the energy and the speed of nodes. It is suitable for emergency FANET scenarios that cannot be trained in advance, but it cannot obtain a better performance at the beginning and needs to retrain in new scenarios. In order to adapt to large-scale networks, a deep Q network enhanced GPSR protocol (QNGPSR) is proposed in [42]; it improves routing performance by using a deep Q network and two-hop node estimation to avoid the probability of packet forwarding to void areas. However, QNGPSR cannot register load information. To solve this problem, a traffic-aware QNGPSR (TQNGPSR) protocol was proposed in [43]. TQNGPSR achieves load balance and adapts to large traffic scenarios. In Table 1, we list some of the routing protocols, highlighting the characteristics of differentprotocols and comparing them with our proposed routing protocol. Although the previous works provide relatively effective improvements, there are still some drawbacks that need to be optimized.
# Timbre Preferences in the Context of Mixing Music
## 2 Study Motivation  
Given the ambiguities and difficulties of the timbre concept, most research has focused on recognition of instrumental timbres or variations thereof [5; 6], using paired similarity ratings toconstruct timbre spaces via multi-dimensional scaling methods [7]. Studies focusing on the musical implications or impacts of timbre often focus on the evoked emotions of particular music sections [8] or genre-specific timbre attributes in conjunction with genre-identifying algorithms [9]. When it comes to preferences for timbral features, research has extensively looked into instrument choices of primary school children [10; 11].  
In [12], a group of sound recording Masters students was asked to create different mixes of 10 musical pieces and evaluate them by preference. Participants showed strong preferences not only for their own mixes but also for higher power in the 4 kHz octave-band. In addition, a specific ratio of power between the _mid_ and _side_ channels of a mix (a representation of the stereo width of a mix) was shown to have an effect on the participants' rating.  
Timbre is of particular interest to the field of music mixing since it is almost the sole defining feature of a \"mix\" that is independent of the loudnesses of the individual parts of a mix. Put differently, every aspect of a mix that is not related to loudness is of a timbral nature. Therefore, a listener judging a particular mix of a musical piece is, in large part, judging the timbre of the said mix.  
In the present study, instead of expert participants, we recruited inexperienced participants to investigate whether their mixing efforts matched up with their preferences. The mixes were done on different musical pieces/loops with each comprising tracks of different musical roles (bassline, chords, arpeggio). To investigate this in a context representative of a real-world timbre manipulation, we designed an experiment within the Max/MSP 7 environment where participants would transform musical loops with some of the timbral manipulation methods established in earlier studies (see Section 3). Additionally, we aimed to assess which particular mixing choices would inform preference ratings and whether any specific clusters of mixing settings would emerge. Rather than trying to relate the timbre to single audio descriptors, such as one would get from an audio analysis, the approach is instead focused on listeners' reactions to, and preferences for, different timbres. This way, the research further increases the knowledge in the nebulous field of timbre research, and also informs mix engineers about the range of options for their timbre related manipulations by exploring the diverse timbre preferences of the listeners.
## 1 Introduction  
Control of population transfer in multiphoton transitions in atoms and molecules with laser pulses is a fundamental tool that has been extensively investigated in quantum optics since the advent of ultrashort pulses due to its value in several areas, including nonlinear spectroscopy [1], femtochemistry and biology [2], or in quantum information and in the quantum engineering of light states [3], where quantum systems need to be fully controlled. Several techniques have been reported for control of population transfer between a pair of quantum states involving multiphoton transitions, such as spectral shaping of the fields illuminating the medium [4, 5], adiabatic methods [6, 7, 8, 9] and \(\pi\)-pulse polychromatic control [10, 11, 12, 13].  
The coherent interaction between a three-level system and two fields has been studied theoretically for different processes [14, 15, 16, 17, 18, 19, 20, 21]. In a three-level ladder system with resonant fields the solution for \(\pi\)-pulse bichromatic control is analytical [17]. The pulse area for complete transfer of the occupation probability from the ground state to the highest state is in this case \(\sqrt{2}\pi\) for both fields, with the pulse area defined as \(\int_{-\infty}^{\infty}\Omega(t)dt\), where \(\Omega(t)=\mu E(t)/\hbar\) is the Rabi frequency of the transition, \(\mu\) is the electric dipole moment and \(E(t)\) is the electric field amplitude of the laser pulse. The analysis of coupled differential equations arising from coherent multiphoton interactions however, even in simple multilevel quantum systems, is a complex task that often leads to expressions that lack any physical insight, so that very few have addressed the general problem analytically [22]. It is therefore desirable to develop efficient numerical algorithms for the solution of the more complicated systems.  
For the case of a four-level ladder system, population transfer schemes combining adiabatic and \(\pi\)-pulse techniques have been proposed [23]. Full \(\pi\)-pulse polychromatic control techniques are however preferred in some cases because the intensities required for optimal excitation are on the order of the intensities for resonantly pumped single-photon transitions, and therefore they result energetically more efficient than adiabatic methods. In this paper we present a numerical solution for complete population inversion in a four-level ladder system by using full \(\pi\)-pulse control.
## 3 Proposed BC-UAC Method
### The Transmission Scheme Based on Segmented TFC
#### 3.3.1 For BC-UAC-TD  
In BC-UAC-TD, the TFC segment is divided into \(M\) subsegments, and each subsegment is modulated according to its time-delay and shifted by \(\Delta f\) as a marker. The duration of a subsegment is \(T_{0}=T_{\text{sym}}/M\).  
The time-delay of the \(k\)-th symbol is \(\tau_{k}=b_{k}T_{0}\), where \(b_{k}\) is the \(k\)-th element of the coded sequence, and \(k=1,2,\ldots,K\). The frequency offset to characterize the time-delay in BC-UAC-TD is illustrated in Figure 7.  
Hence, the modulated frequency of symbol \(k,f^{\prime}_{\text{whistle},k}[n]\), is:  
\[f^{\prime}_{\text{whistle},k}[n]=\left\{\begin{array}{cc}f_{\text{whistle },k}[n]+\Delta f,&(k-1)T_{\text{sym}}+\tau_{k}<n\leq(k-1)T_{\text{sym}}+\tau_ {k}+T_{0}\\ f_{\text{whistle},k}[n]\,&\text{others}\end{array}\right. \tag{2}\]  
where,\(f_{\text{whistle},k}[n]\) is the initial frequency of whistle in (1), and \(\Delta f\) is the frequency offset to represent the time-delay.  
The whistle signal can be viewed as the weighted superposition of a series of cosine signals with envelope and frequency changes [33]. The bionic modulated signal in BC-UAC-TD can be expressed as:  
\[s_{\text{TD}}[n]=a[n]\cos\left(2\pi\sum_{i=1}^{n}\frac{f^{\prime}_{\text{ whistle}}[i]}{f_{\text{s}}}\right),\quad n=1,2,\cdots,T_{\text{whistle}}\times f_{ \text{s}} \tag{3}\]  
where \(a[n]\) is the envelope of the whistle signal at sampling time \(n\).