esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Study
### Objective Evaluations
#### 4.1.2 Experimental Results of Objective Evaluations  
The goal of this evaluation is to determine the effectiveness of the proposed course prediction. This evaluation is based on the course prediction algorithm shown in Algorithm 2. In this experiment, \(k\) is 50. That is, in Step 1 of the prediction algorithm, the 50 most relevant users were determined to calculate the unknown course degrees. Table 4 shows the experimental results, which can be decomposed into five concepts: average, standard error, standard error of averages, relationships, and \(t\)-test for paired samples between the predicted set and ground-truth set. For the average, the averaged degrees for the predicted set and ground-truth set are similar. For standard error and standard error of averages, those of the predicted set are lower than those of the ground-truth set, showing that the variance for the predicted set is small. That is, the prediction performance is stable. For the paired sample relationship, the relationship between the predicted and ground-truth sets is close (\(p=0.00\)). For the paired sample \(t\)-test, because the \(p\)-value 0.434 is larger than 0.01, the difference between the predicted and ground-truth sets is very small. In summary, the objective evaluation results show that the proposed prediction algorithm performs well and can provide the learner with a reliable guideline for choosing courses.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Course 1** & **Course 2** & **Course 3** & **Course 4** & **Course 5** & **Course 6** \\ \hline user 1 & 0 & 3 & 0 & 0 & 1 & 0 \\ user 2 & 4 & 0 & 2 & 5 & 4 & 2 \\ user 3 & 0 & 3 & 0 & 2 & 2 & 3 \\ user 4 & 4 & 3 & 3 & 2 & 3 & 0 \\ user 5 & 0 &? & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Example of a user-to-course degree matrix.
# Dynamic Behavior of Track Bridge on Serviced Long-Span Road/Railway Bridge
## 4 Results and Discussions
### Natural Frequency Analysis  
We conducted a numerical analysis to investigate the natural frequency vibration modes with respect to the track girders. Figure 9 shows the mode-specific natural frequencies of the Yeongjong Bridge track girder system.  
Figure 8: Analytical results of vertical and horizontal accelerations of rail and track girder. (**a**) Rail vertical acceleration; (**b**) Track girder vertical acceleration.  
Figure 9: _Cont._  
We calculated the natural frequencies of the track girder system by using the free vibration waveform of the acceleration signal measured when a train was running on the Yeongjong Bridge direct fixation track. The results of the free vibration region (excluding the forced vibration region) of the calculated acceleration response were used to conduct a fast Fourier transform (FFT) analysis. The natural frequencies of the Yeongjong Bridge direct fixation track were then compared and analyzed.  
Figure 10 shows an example of the results of the free vibration response analysis of the acceleration response of the track girders, and the measurement results of the natural frequency obtained through the FFT analysis of the free vibration region.  
Comparing the analysis results of the track girder natural frequency using the full-scale Yeongjong Bridge model to the measured natural frequency of 23.80 Hz during train operations, the former was moderately larger. The measured acceleration under train operation and the measured natural frequency calculation using this acceleration reflect the unsprung mass effect of the operating vehicle. The mass contribution level by the vehicle to the measured natural frequency increased, causing the measured natural frequency to be lower than the analytical value.
## 3 System Model and Problem Formulation
### Proof of the NP-Hard Problem  
In our heterogeneous network resource allocation scenario, due to the existence of DC, each BS in our environment is not an independent individual. The bandwidth allocation strategy selected by one BS is usually closely related to the strategy selected by other BSs. To prove that our optimization problem is an NP-Hard problem, we map the optimization problem to the traveling salesman problem (TSP) for illustration. Assuming that there are \(V\) cities in a TSP problem, and the cost from city \(V_{t}\) to city \(V_{o}\) is \(c_{l,o}\), the goal of this problem is how to find a route \(V_{\min}\) that minimizes the total cost \(P_{t}\) when passing through all cities. The TSP problem can be modeled as  
\[\min\;P_{t}=\sum\limits_{(l,o)\;\in\;V}c_{l,o}d_{l,o} \tag{9}\]  
\[s.t.\;\text{C}_{1}:d_{l,o}=\begin{cases}1,&\text{traveling salesman from city }l\text{ to city }o\\ 0,&\text{otherwise.}\\ &\text{C}_{2}:\sum\limits_{l\
eq o}d_{l,o}=1\end{cases}\]  
where the binary variable \(d_{l,o}\) represents whether the traveler departs from \(V_{l}\) to \(V_{o}\). Constraint C\({}_{2}\) means that each city is passed only once. If one considers the simplified form of the optimization problem in this paper, that is, one only considers throughput as an optimization objective in the scenario, then the optimization objective of this simplified problem can be expressed as  
\[\max\limits_{B_{i,k}^{n}}\text{R}_{t}=\sum\limits_{i=0}^{l-1}x_{i,m}B_{i,m}^{ n}\log_{2}(1+SNR_{m})+x_{i,k}B_{i,k}^{n}\log_{2}(1+SINR_{k}) \tag{10}\]  
\[s.t.\;\text{C}_{1}:x_{i,k}=\begin{cases}1,&\text{the useriassociate withBS }k\\ 0,&\text{otherwise.}\end{cases}\] \[\text{C}_{2}:\sum\limits_{j=0}^{k}x_{i,j}\leq 2\] \[\text{C}_{3}:W_{k}=\sum\limits_{n=0}^{N}B_{i,k}^{n}\]We map the TSP to our optimization objective, the traveling salesman in the TSP corresponds to the BS \(k\) in our environment, and the city \(V\) available for the traveling salesman to choose corresponds to the bandwidth allocation action \(B^{n}_{i,m}\) of the BS. The total cost \(P_{t}\) corresponds to the system throughput \(\mathrm{R}_{t}\). In TSP, if we want to know whether a trip with the lowest total cost exists, we have to check all possible travel arrangements in the worst case. Likewise, in our model, given an arbitrary bandwidth allocation strategy, the throughput can be calculated. However, to find the optimal decision among many actions, this theoretically requires testing all actions. Since the objective function \(\mathrm{R}_{t}\) can obviously be completed in polynomial time, and TSP is an NP-Hard problem, the optimization objective \(\mathrm{R}_{t}\) is also an NP-Hard problem. Furthermore, because of \(\mathrm{R}_{t}\in P\), it can be concluded that the objective function \(P\) is also an NP-Hard problem. Traditional methods to solve such problems are very challenging. To overcome this challenge, in this paper, we provide a DRL-based framework to solve this optimization problem, which will be described in detail in the next section.
# Sustainable Approach of Generating Water and Energy:
## 3 Mathematical Model
### System Cost Model  
This section describes in detail the economic model used to estimate the total annual cost, which is calculated as the sum of the loan payment and annual operating cost. The capital cost of the proposed system shown in Equation (13) comprises the capital and insulation cost of purchasing a photovoltaic hybrid thermal system, the cost of direct contact membrane desalination, the annual cost for the auxiliary heating system, and the initial cost of the absorption chiller.  
\[C_{c}=PVT_{cost}+DCMD_{cost}+CC_{Hx}+Q_{abs-chiller} \tag{13}\]  
The initial system capital cost is treated as an investment that is returned with a fixed-interest loan. The loan periods considered in this study is 20 years. Equation (14) shows the annual loan payment [32].  
\[Annual\ Loan\ Payment=C_{c}*i*(1+i)^{n}/\left((1+i)^{n}-1\right) \tag{14}\]  
The overall annual operating cost can be expressed in Equation (15) [28].  
\[ATotal\ Cost=Annual\ Loan\ Payment+Annual\ Grid\ Cost-Annual\ Grid\ Sell \tag{15}\]  
Table 2 shows the variables considered when calculating the cost of a system. It also includes the study's unit prices (along with references).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Components** & **Parameters** & **Value** & **Ref** \\ \hline \multirow{8}{*}{PVT} & Capital and installed cost & 1800 [\(\$\)/kW] & [33] \\ \cline{2-4}  & Replacements & 1800 [\(\$\)/kW] & - \\ \cline{2-4}  & O\&M & 5\% of total capital cost & [33] \\ \cline{2-4}  & Lifetime & 25 [years] & - \\ \cline{2-4}  & Capital & 50 [\(\$\)/m\({}^{2}\)] & - \\ \hline \multirow{8}{*}{Membrane cost} & Replacements & 50 [\(\$\)/m\({}^{2}\)] & - \\ \cline{2-4}  & O\&M & 1200 [\(\$\)/year] & - \\ \cline{2-4}  & Lifetime & & \\ \cline{1-1} \cline{2-4}  & Absorption chiller & Capital & 1800 [\(\$\)/ton] & [34] \\ \hline \multirow{8}{*}{Geometrical and energy characteristics of a house} & Floor Area & 320 m\({}^{2}\) & \\ \cline{2-4}  & overall heat transfer coefficient U\({}_{\text{Wall}}\) & 0.34 [\(\text{W}\)/m\({}^{2}\)-K] & \\ \cline{1-1} \cline{2-4}  & overall heat transfer coefficient U\({}_{\text{roof}}\) & 0.2 \(\text{W}\)/m\({}^{2}\)-K & \\ \cline{1-1} \cline{2-4}  & Electric plug load & 3.5 \(\text{W}\)/m\({}^{2}\) & [35] \\ \cline{1-1} \cline{2-4}  & Lighting Power Density & 4 W/m\({}^{2}\) & \\ \cline{1-1} \cline{2-4}  & Window-to-Wall Ratio & 15\% & \\ \hline \multirow{8}{*}{Parameters used for the proposed system} & Energy consumption to supply m\({}^{3}\) of water from nearest desalination plant to the selected location & 14 kWh & [36] \\ \cline{1-1} \cline{2-4}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \cline{1-1} \cline{2-4}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \cline{1-1} \cline{2-4}  & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \cline{1-1} \cline{2-4}  & Electricity cells to grid & 0.05 \(\$\)/kWh & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hybrid system’s cost details and house’s characteristics used in the simulation.
## 6 Open Challenges and Future Research Directions  
In this section, open challenges and future directions are presented for UWSNs.  
* The channel models for noise and attenuation calculation in UWSNs are empirical. This area is still open for the analysis and investigation of new models developed in an analytical or computational manner. In addition, experimental models can also be developed to reflect the characteristics of the underwater medium.
* Sensor nodes move with water currents by which they change their positions constantly. As a result, their position information also changes. This makes the localization of the sensor nodes achallenging task. The movement of nodes also requires constant updates about the new positions. This inherently adds delay and energy consumption as nodes have to exchange such information. Future research needs to address these issues.
* The routing protocols for UWSNs make use of the network layer to deliver data packets from the bottom to the surface of water. All the routing protocols described in this survey consider only the network layer. To optimize network performance, the network layer can be combined with the MAC layer to reduce the waiting time of the packets in the queues of the sensor nodes. This will also reduce the interference during packet transmission and, consequently, the energy consumption. This is because interference leads to packet loss, so the lost packets are retransmitted, which consumes additional energy.
* Since the underwater medium is highly unpredictable and challenging, protocols focusing on the reliability of data transmission from the bottom to the surface of the water are worth investigation in future research.
* In all the existing routing protocols, nodes close to the water surface are frequently selected as forwarders, since these nodes are close to the surface sink. This leads to high data load on these nodes. As a result, such nodes drain their batteries rapidly and die. The death of such nodes disconnects the data traffic from the bottom to the surface of the water, which as a consequence, threatens the reliable operation of the network. Future research needs to cope with the early death of the sensor nodes.
* The variation of the acoustic speed with depth, salinity and temperature of the water causes the acoustic waves to travel on curved paths. This creates regions where the sensor nodes are not able to communicate with other nodes in their vicinity. This is a challenging issue for the design of future routing protocols.  
As a PhD candidate, [PERSON] wrote the manuscript under the supervision of [PERSON]. [PERSON], [PERSON] and [PERSON] contributed in organizing and streamlining the flow of the manuscript. [PERSON] and [PERSON] refined the overall manuscript and responses to the reviews.  
This research work has been partially supported by University of Malaya Research Grant (UMRG) scheme (RP036-15 AET) and Postgraduate Research Grant (PG035-2016A).  
The authors declare no conflicts of interest.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 2 Related Work
### Security Monitoring and Trust Management
The European Telecommunications Standards Institute (ETSI) NFV Industry Specification Group is addressing the monitoring and management of mobile networks based on virtualization technologies. The group has defined [15] scenarios and requirements for monitoring and discussed related orchestration and management issues. The group has also published security and trust guidelines [16], illustrating use cases and trust issues between different domains in virtualized future telecommunication architectures. They consider the life cycle of trust (evaluating, re-evaluating, invalidating, re-establishing) and present different methods for trust evaluation--including reputational, game theoretical, probabilistic, and look-up tables. The guidelines classify trust relevant parameters in to the following categories: geographical or logical network location, jurisdiction, hardware and software capabilities, origin, ownership, execution history, last audit, and chain of trust, and security mechanisms (encryption, physical security etc.). The European Union Agency for Network and Information Security (ENISA) has surveyed threats in the 5G landscape [17]. Their recommendations include the development of incident response capabilities and information sharing practices among operators. Cooperation between operators requires both capable computer emergency response teams and organizations enabling cooperation as well as technical mechanisms for sharing of real-time information. Research efforts for security management approaches for mobile networks include, e.g., [PERSON] et al. [18], who defined a high-level NFV-based framework and requirements for adaptive 5G security management. [PERSON] et al. [19] proposed security assessment mechanisms for SDN-based 5G networks. The mechanism utilized attack graphs and an analytic hierarchy process to quantify security levels as well as to evaluate costs of attacks and security. Machine learning approaches [20] for monitoring the security state of mobile networks have also drawn academic interest. [PERSON] et al. [21] used a supervised learning algorithm to analyse IP packet streams from mobile terminals in order to detect distributed denial-of-service attacks. [PERSON] et al. [22] analysed mobile network traffic flows to detect malware. Situational awareness solutions monitor a network in order to make automated decisions based on analysed context knowledge. For 5G, [PERSON] et al. [23] studied a monitoring and security adaptation of 5G radio and access networks. [PERSON] et al. [24] proposed an incident management architecture that combines different layers of 5G information, including NFV and SDN aspects, conventional risk management schemes, and control loop for adaptive security.
# AIS for Malware Detection in a Realistic IoT System: Challenges and Opportunities
## 5 Results and Discussion
### Detection Performance  
The figure below (Figure 6) shows the results for the five system scenarios implemented in this project for both the NPS algorithm and MNSA. Since the F1-score takes both negative and positive detection into account, we used it as the main metric to evaluate the performance in this analysis. First, we started with systems 1 to 4 where we used only one IoT device in the implementation. As predicted in [3,9], the NPS algorithm succeeds in detecting malware better than the MNSA in all four systems, as evidenced by the higher F1-score by up to 20% than that of the MNSA. As anticipated, the performance of both the NPS algorithm and MNSA improved when we moved from system 1 to 4 by 8% and 10% for the NPS algorithm and MNSA, respectively. Increasing the volume size allowed for an increase in the number of \(D_{Train}\). Therefore, this resulted in better detection performance in all four systems for both methods.  
In system 5, we connected two IoT devices to the system. That system architecture was used to evaluate the method's transfer learning abilities. Since we trained the method  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **System 1** & **System 2** & **System 3** & **System 4** & **System 5** \\ \hline Volume size & 30 GB & 32 GB & 64 GB & 128 GB & 30 GB \\ \(\mid\!\!D_{Total}\!\mid\) & 12,000 & 14,000 & 28,000 & 400,000 & 24,000 \\ Number of IoT devices & One & One & One & One & Two \\ \hline \hline \end{tabular}
\end{table}
Table 4: IoT system specifications for all systems.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **NPS** & **MNSA** \\ \hline \(\mid\!\!C_{Pos}\!\mid\) & 30 & 20 \\ \(\mid\!\!\!C_{Neg}\!\mid\) & 30 & 150 \\ \(\mid\!\!\!\mid\!L\!\mid\) & 16 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detectors’ size used for the NPS and MNSA.  
on one IoT device, we could use the total number of \(D_{Total}\) to create the detectors. Both AIS solutions, the NPS algorithm and MNSA, showed the capability of transferring learning and protecting both IoT devices connected to the system. However, as anticipated, the NPS algorithm succeeded in a better detection performance when protecting the two IoT devices than the MNSA by 16%. This shows that the NPS algorithm has better transfer learning abilities than the MNSA. Therefore, the NPS algorithm is better at protecting distributed and robust IoT systems.  
We demonstrated the NPS and MNSA detection performance and lightweight abilities on one IoT device using systems 1 to 4. System 5 demonstrated the transfer learning abilities. In this implementation, by increasing the volume size of the device, we could use a larger number of dataset records in the detectors' generation stage. This resulted in a better learning curve and thus a better classification accuracy when detecting malware files. Furthermore, AIS solutions can secure the IoT system with multiple IoT devices if only installed on one IoT device as demonstrated in system 5.
## 4 Data Extraction
### Audio Tokenization  
A phone decoder is built using the reduced set of phonetic units described in Table 1 and an off-the-shelf close to state-of-the-art end-to-end neural network-based ASR system: Facebook AI Research wav2 letter++ (consolidated into Flashlight), applying the Gated ConvNet recipe presented in [40]. Note, that the phone decoder requires neither lexical models nor a language model and is applied to the audio chunks without any phonological restrictions. In this way, a long sequence of phonetic units along with their corresponding timestamps (the recognized sequence) is obtained from each audio chunk. Initially, the phone decoder is trained on generic speech datasets for Basque and Spanish which amounts to 332.2 h of speech (77.8 h in Basque and 254.4 h in Spanish): Mozilla CommonVoice (cv-corpus-5.1-2020-06-22) [12], OpenSLR (SLR76) [13], Aditu [18] and Albayzin [41]. Then, in successive iterations of the procedure, the phone decoder is re-trained on the set of training segments extracted from Basque Parliament sessions in the previous iteration and then applied to audio chunks to obtain new and hopefully better sequences of phonetic units.
## 1 Introduction  
Antenna placement in large structures is not a novel issue, but is drawing more attention due to the advent of new mobility technologies. In particular, there is a need to place antennas for an affordable price on trucks or cars, which will be used to precisely (sub centimeter precision) compute each vehicle's position with GNSS systems. For solutions using a brute force method, the full simulation model must run at each of the antenna's possible locations, which might lead to very long times (on the week-long scale) taken to find the best position for the antenna. Thus, to avoid brute force localization methodologies, powerful optimization algorithms may be used to find this ideal position and ensure greater accuracy of partial and final results, while obtaining time savings. The need for new solutions arises from the need to frequently redesign vehicles (due to commercial aesthetic requirements), on which an antenna must be included. That design, which is conditioned by antenna localization, will benefit if it minimizes the need for anechoic-chamber-mediated measurements of full vehicles, a costly and time consuming process. In this way, faster and predictable solutions based on commercially available software must be investigated, which will simplify finding the correct position of a small antenna on a large vehicle. Defining the best position for an antenna on a fixed structure, such as the roof of an autonomous vehicle, requires the implementation, in electromagnetic simulation software, of all the relevant characteristics of the antenna and vehicle, in order to obtain a model as close as possible to the real scenario, and requires the use of a methodology that will determine the correct localization for the antenna while fulfilling the requirements for a given application. In addition to being accurate, such a model should be able to calculate the antenna's radiation characteristics as fast as possible, since the antenna's properties will be recalculated each time the placement methodology needs a new trial position. However, to have optimization algorithms working efficiently, the accuracy of radiation characteristics used for the objective function is mandatory, and as more details of the vehicle and antenna are included in the model, and the more accurate the electromagnetic simulation solvers are, the more time and computational resources will be required for the simulation to converge. Thus, the response that is provided by electromagnetic simulation solvers must reflect, as much as possible, the desired radiation characteristic of the antenna to ensure the effectiveness of the search algorithms, without increasing the computational time significantly. This work proposes a solution that uses a simplified electromagnetic model, implemented using commercial software and machine learning tools, to maintain accuracy in simulations for large electromagnetic bodies and improve computational time, while defining the correct antenna placement.