esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Comparison Algorithms
#### 5.3.1 Comparison Methods: PSO-LSTM  
PSO-LSTM (Particle Swarm Optimization-Long Short-Term Memory) [32] uses particle swarm optimization to optimize some of the hyper parameters, so as to find the best performance point. Based on simulating the number of evolution and population size, the initial learning rate and the number of hidden layer neurons are updated as follows:  
\[v_{I+1}=v_{I}+c_{1}\times rand\times(\mathit{pbest}_{I}-Loc_{I}) \tag{21}\]  
\[+c_{2}\times rand\times(\mathit{gbest}_{I}-Loc_{I})\]  
\[Loc_{I+1}=Loc_{I}+v_{I} \tag{22}\]  
Here, \(v_{I}\) is the velocity of the \(I\)-th particle. \(c_{1}\) and \(c_{2}\) are learning coefficients. \(\mathit{pbest}_{I}\) denotes the best position the \(I\)-th particle has ever experienced. \(\mathit{gbest}_{I}\) is the best position ever experienced by all particle, and \(Loc_{I}\) is the location of the I-th particle.  
PSO-LSTM uses the above formulae to calculate the particle velocity and then updates the particle position. The updates of population, individual optimal and population optimal are carried out to find the global optimal parameters, and then, LSTM learning is carried out.  
The advantage of PSO-LSTM is that the parameters of LSTM can be automatically adjusted and optimized without manual search. However, the parameter adjustment of PSO-LSTM can only adjust the two important parameters, which leads to a long adjustment time.
# Blockchain-Assisted QoS-Aware Routing for Software-Defined Wide Area Network
## 1 Introduction
The core motivation for using smart contracts is to enable an automated, trustless payment mechanism that activates only when the QoS requirements have been met by participating ISPs. This automation eliminates the need for manual settlement or third-party verification, which can be particularly valuable in multi-provider environments where there is no central authority to enforce SLA commitments across administrative boundaries. This article mainly focuses on integrating the features of blockchain in cloud-based provisioning, which has the following research objectives: (1) control the delay of data processing and the overall processing time of a service or application to meet the QoS requirements; (2) minimize the amount of network overhead for cloud service access; (3) proper infrastructure design for corresponding money exchange between participating entities.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 1 Introduction
To solve this problem and obtain the optimal power for each of the paired users in a subchannel, a power allocation solution is proposed based on Karush-Kuhn-Tucker (KKT) conditions (PKKT). Then, the closed-form optimal power allocation solution for the multiplexed users is obtained. The PKKT solution is applied after pairing users based on HNG to obtain the optimal sum rate in NOMA. Thus, we refer to this technique as NOMA-PKKT-HNG. * The energy efficiency maximization problem is formulated with power constraint consideration, and SIC is applied to reduce complexity at the receiver. To solve this problem and obtain the optimal power for the multiplexed users, we propose a power allocation solution using PDKL. The formulated objective function is in fractional form. Therefore, the DKL algorithm is used to transform into linear form (subtractive function) and iteratively solve the problem with considerable error tolerance. The PDKL solution is applied after pairing users based on HNG to obtain the optimal energy efficiency in NOMA. Thus, we refer to this technique as NOMA-PDKL-HNG. The proposed solutions have been evaluated and compared with NOMA with difference of convex programming (NOMA-DC), NOMA with fractional transmitting power allocation (NOMA-FTPA) and convectional Orthogonal Frequency-Division Multiple Access (OFDMA) in terms of sum rate, energy efficiency and fairness. The rest of this work is organized as follows: Related works are exploredin Section 2. In Section 3, the system model and performance metrics of the NOMA system are described. Section 4 describes the proposed resource allocation techniques and shows the mathematical formulation for the sum rate and energy efficiency optimization process. In addition, the HNG process to obtain the optimal user pairing in the system is also presented. Then, the optimal power allocation solutions are provided. In Section 5, the complexity analyses for the proposed algorithms are discussed. The analysis of simulation results is presented and discussed, showing the significant improvement made by the proposed algorithms. Lastly, in Section 6, the work is concluded.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 4 Photonic Devices and Components
### Technological Platforms  
Actually, the majority of the commercial telecommunication devices are hybrid integrated components based on silicon substrates and monolithically integrated devices. The choice of the technological platform for SDP will be a crucial decision, since the approaches used today for long-haul networks and data centers could not be viable or convenient in the metro and access segment. In this field, a challenging trade-off between two main requirements will be necessary: a higher density of integration, in order to reduce the size of devices, and a simpler fabrication process, since an higher production volume at a lower cost will be required. Especially at the access network level, where equipment is shared by fewer users, cost is the major issue and the evolution of photonic integrated circuits (PICs) will be essential.  
Silicon photonics is universally considered a promising technology for telecommunication devices [62]. In particular, silicon-on-insulator (SOI) technology is suitable for commercial applications for two main order of reasons. SOI technology is fully compatible with conventional microelectronic fabrication processes which have been consolidated for decades to produce complementary metal-oxide semiconductor (CMOS) circuits [63]. This takes advantage in terms of cost (no complex new fabrication techniques are needed) and integration of photonic and electronic components onto the same substrate. Moreover, SOI technology is extremely versatile, since waveguides, photo-detectors, and modulators have already been demonstrated [62]. Although silicon is an indirect band gap semiconductor, even optical sources can be realized in a hybrid approach by exploiting the Raman effect to amplify the radiation generated by an external laser [64].  
In order to realize SDP devices, mechanisms for tunability need to be provided. Silicon photonics can exploit several physical effects to achieve this goal, such as thermo-optical effects [65; 66] and plasma-dispersion effects. Non-linear effects can be also exploited, such as stimulated Raman scattering, the Kerr effect, and two-photon absorption (TPA) inducing free-carrier absorption (FCA) [67]. Newly developed materials, such as passive polymer technology, have been also proposed in order to exploit the advantages of the simple low temperature fabrication processes, ease of integration, and low power consumption for thermally actuated optical elements [56].
## 3 Resolution of Degeneracy
### Representation of Line Features  
In this paper, the symbols \((\cdot)^{w}\), \((\cdot)^{b}\), and \((\cdot)^{c}\) represent the world coordinate system, the IMU coordinate system, and the camera coordinate system, respectively. The z-axis in the world coordinate system is aligned with the direction of gravity. In order to add line features to the monocular VIO system, the ELSED algorithm is used to detect and extract line features in images, and the LBD algorithm is used for tracking and characterization.  
Point features in space can be visualized in 3D coordinates. Line features are more complex to represent compared with point features. In this paper, we use two representations of line features in PL-VIO [18], the structural lines are reconstructed using Plucker coordinates and the parameters of the line features are optimized using orthogonal coordinates. The Plucker coordinates are a six-parameter representation of the normal \(n\) and direction vectors \(d\) of a line feature in the Plucker coordinate system:  
\[\mathcal{L}=(n^{T},d^{T})^{T}\in R^{6} \tag{1}\]  
Plucker coordinates have great advantages in the triangulation and reprojection process of line features, but they are somewhat redundant in state optimization, so a four-parameter representation of standard orthogonal coordinates is introduced:  
\[\mathrm{O}=[\psi_{1},\psi_{2},\psi_{3},\sigma] \tag{2}\]  
where \(\psi_{i}\) denotes the rotation matrix of the line expressed in Euler angles in the camera coordinate system and \(\sigma\) denotes the minimum distance from the center of the camera to the line. The two linear representations can be interconverted with each other by QR [43] decomposition.
# An Efficient Advantage Distillation Scheme for Bidirectional Secret-Key Agreement
## 6 Performance Analysis  
In this section, we evaluate performance of the proposed AD scheme over BSC channel in terms of BER and \(C_{s}\) and performance of the proposed AD scheme over AWGN channel \(C_{s}\). The length \(n\) of raw key \(M^{i}\) is 10,000. BER simulation results are shown in Figure 8. With the increasing crossover probability \(p\), the BER increases as well. This indicates that performance of the main channel between Alice and Bob is superior to that of the eavesdropper's channel under the same number \(l\) of rounds and length \(n\) of the secret sequence.  
We also show the capacity of main channel, eavesdropper's channel, and the difference between them that is secret-key capacity over BSC channel in Figure 9 and over AWGN channel in Figure 10. Assuming that \(\alpha=\beta\leq 0.09\), the secret-key capacity increases as \(\alpha\) and \(\beta\) increase. However, when \(\alpha=\beta\geq 0.1\), the secret-key capacity decreases with the increase of \(\alpha\) and \(\beta\). In the AWGN channel, the \(C_{s}\) is close to 0.3 when the signal-to-noise ratio (SNR) is from 0 to 50.  
Comparing to the AD schemes using the original two-way wiretap channel, the advantage of our scheme is that we need fewer rounds of communication between [PERSON] and [PERSON] to obtain the same amount of keys. Furthermore, since we need fewer rounds of communication, we also need less XOR operations to obtain the same amount of keys. In each round of the unidirectional AD scheme, using the original two-way wiretap channel, [PERSON] and [PERSON] need 1 XOR operation, respectively, in (3) and (6). In each round of the proposed bidirectional AD scheme, [PERSON] needs 2 operations in (12) and (21), and [PERSON] needs 2 operations in (15) and (18). Therefore, in each round of the proposed bidirectional AD scheme, the number of XOR operations is two times higher than that in the unidirectional AD scheme. However, our bidirectional scheme also has a transmission rate of secret keys which is two times greater than that of the unidirectional AD scheme. Therefore, our scheme needs the same number of XOR operations to obtain the same amount of secret keys as the unidirectional AD scheme. We have listed the transmission rate, XOR operations of the unidirectional AD scheme and the proposed bidirectional AD in Table 4.  
Figure 8: The BER performance of the main channel and eavesdropper’s channel over binary symmetric channel (BSC).  
Figure 9: The secret-key capacity and capacities of the main channel and eavesdropper’s channel over BSC channel.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Delivery Mode
#### 3.2.1 Real-Time Delivery  
A vast majority of WSNs applications are real-time, such as radiation monitoring, fire monitoring and medical surveillance, etc. and require a high level of temporal accuracy, otherwise sensed data become useless, or their value is decreasing after the time-bound. These applications are referenced as real-time applications. In WSNs, communication delays are dominant over processing delays. Therefore, communication latency must be bounded to enable real-time information delivery in such networks [41; 42; 43; 44; 45; 46].  
In the following, we quote some real-time routing protocols designed for the WSNs.  
* **Real-Time Power-Aware Two Hop routing (PATH)** PATH is designed as a real-time routing protocol using the two-hop neighbor information for routing decisions [47]. It uses dynamic power control to manage energy consumption and packet delay to reduce packet dropping and improve real-time routing. Its main components are the forwarding metric and policy, the delay estimator and the initiative drop control. In [48], the authors show that PATH reduces packet dropping and communications reliability.
* **Contention-based Beaconless Real-time Routing protocol (CBRR)** CBRR exists in the two versions, namely CBRR one-hop and CBRR two-hop protocol. It uses a contention mechanism to collect the information of neighborhoods to ward off the limitations of beacon-based schemes and to enhance energy conservation. It uses a reactive mechanism to select the suitable next-hop forwarder, which offers the shortest wait delay. Experience shows that this protocol presents good results in terms of end-to-end delay, delivery ratio and energy management [49].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Application Type**} & & \\ \cline{2-5}
**Protocol.** & \multicolumn{3}{c}{**Event-Driven**} & & **Reliability** & **Congestion** & **Energy** \\ \cline{2-5}
**Time-Driven** & \multicolumn{3}{c}{**Sink-Centric**} & **Node-Centric** & & **Coatrol** & **Efficiency** \\ \hline \hline
**(RT) 2** & \(\checkmark\) & & N A & N A & \(\checkmark\) \\ \hline LTRES & \(\checkmark\) & & N A & \(\checkmark\) & N A \\ \hline RRTT & \(\checkmark\) & & \(\checkmark\) & N A & N A \\ \hline SMESRT & \(\checkmark\) & & \(\checkmark\) & N A & N A \\ \hline Collect & & \(\checkmark\) & \(\checkmark\) & N A & N A \\ \hline EELLER & & \(\checkmark\) & N A & N A & \(\checkmark\) \\ \hline IQAR & & \(\checkmark\) & N A & N A & \(\checkmark\) \\ \hline ERP & & \(\checkmark\) & \(\checkmark\) & N A & N A \\ \hline EEDP & & \(\checkmark\) & Low & \(\checkmark\) & Low \\ \hline
**WB-TEEN** & \(\checkmark\) & & \(\checkmark\) & N A & N A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of application type-based routing protocols for WSNs.
## 5 The Cyber-Physical Performance Evaluation of the ADR Control Strategy
### The ADR Performance Considering Real Communication  
In this paper, packet receive rate (PRR), which may be considerably affected by the wireless channel behavior, is introduced as a metric of link quality. The PRR is defined as Equation (6).  
\[\text{PRR}=\frac{\text{The number of receive messages}}{\text{The number of send messages}}\times 100\% \tag{6}\]  
Receive signal strength index (RSSI), link quality index (LQI) and signal noise ratio (SNR) are three indexes belong to the family of hardware-based link quality estimators (LQE) [24]. In the condition of IEEE 802.15.4-compliant wireless communication, several previous studies have revealed that PRR can be approximated by above hardware-based parameters with sigmoid curve (As depicted in Figure 15) [25; 26].  
Due to sigmoid correlation between LQE and PRR, it is impossible and unnecessary to construct a wireless network of which PRR = 100%. According to our experience, the PRRs of most ZigBee nodes are no more than 95%. Figures 16 and 17 illustrate the ADR performance in the condition of PRR =  
Figure 14: The indoor temperature curve of 30 rooms with ideal communication.  
Figure 15: The correlation between RSSI and PRR (95% confidence level) [26].  
95% and PRR = 80% respectively. The simulation of each PRR is performed 50 times. Figures 16a and 17a are the indoor temperature curve of 30 rooms in one of those results. Figures 16b and 17b are standard box plots of the sum of absolute deviation between the desired temperature region and the real indoor temperature of 30 rooms at each timeslot. The red cross can be regarded as the maximal deviation. As we can see, even if PRR = 95%, the violation of comfort band happens occasionally which means that the ADR strategy without consideration of link quality deteriorate significantly in real cyber-physical scenario.  
Power consumption and electricity fare of the ADR strategy from PRR = 50% to PRR = 100% are respectively presented as box plots in Figure 18a,b (the simulation of each PRR is executed 50 times). As shown in Figures 16-18, unstable communication exacerbates the ADR strategy performance, that is, not only user comfort cannot be ensured but also electricity fare increases.  
Figure 16: The ADR performance of PRR = 95%: **(a)** The indoor temperature curve of 30 rooms; **(b)** The absolute deviation between the desired temperature region and the real temperature.  
Figure 17: The ADR performance of PRR = 80%: **(a)** The indoor temperature curve of 30 rooms; **(b)** The absolute deviation between the desired temperature region and the real temperature.
# CFD Simulation of the Safety of Unmanned Ship Berthing under the Influence of Various Factors
## 3 Multi-Factor Berthing Safety Study on an Actual Unmanned Ship
### Analysis of the Berthing Characteristics of Actual Unmanned Ship
#### 3.2.2 Pressure Characteristic Analysis  
The pressure characteristics at different speeds obtained are shown in Figure 9.  
Figure 8: The fluid velocity distribution and air-water volume fraction at different speed.  
As can be seen from Figure 9, the bow part of the unmanned ship when sailing is positive pressure, the tail of the unmanned ship will form a negative pressure zone, and with the increase in speed, its absolute value is increasing rapidly, such as when the speed is 4 m/s, the maximum pressure at the first of the unmanned ship is 6874 N, when the speed is 24 m/s, the maximum pressure can reach 238,869 Pa, and the pressure at the bow of the ship and the pressure difference between the middle part of the ship is also increasing. As can be seen from [PERSON]'s principle, different flow rates can lead to different pressures, and where water flows faster, unmanned ships are less under pressure. The front and rear parts of the unmanned ship are subjected to a large pressure, which can be reinforced in the process of the design of the unmanned ship structure, and improve the pressure resistance of the unmanned ship. At the same time, the low pressure at the tail will cause fluid back-flow, so the fluid velocity near the tail will be slow. Depending on the ship type, the water and air flowing in the Y direction will partly acquire lateral velocity when they hit the wall of the unmanned ship.
# Speed-Flow-Geometric Relationship for Urban Roads Network
## 5 Discussion
For instance, for no median, one lane, and high side friction situation, the reduction of average travel speed (ATS) due to traffic calming speed device was 1.28 km/h. While the same density of traffic calming speed device with the identical cross-section features (except the number of lanes = 2) could reduce the average travel speed (ATS) value by 0.22 km/h for the urban road. However, for any other cross-sectional characteristics, the traffic calming speed device did not reduce the average travel speed (ATS). Similarly, for the density of access driveway, the average travel speed (ATS) was reduced by 0.52 km/h, and 0.26 km/h on the two cases with median, two lanes, and high side friction and no median, one lane, low side of friction, respectively. However, there was no decrease in average travel speed (ATS) for any other cases of the cross-sectional features. Due to the density of the intersection, the average travel speed (ATS) value was decreased by 1.26 km/h only for one case (no median, one lane, and high side friction). Due to the density of the right-turn driveway, the regression analysis showed no reduction in average travel speed (ATS) for all cases. The developed models affirmed that the impact of geometric and traffic parameters on the speed-flow relationship has a wide range of variability. Particularly, the significant impact of longitude parameters on the speed-flow relationship was established to rely on a coefficient value in the model equation (Table 5 and Figures 6-8). For instance, the urban road without median, one lane, and low side friction displayed the coefficient of access driveway density of 0.26. This clearly indicated that when the urban road has an access density (AccessD) value of one per 1 km, the travel speed can reduce by a factor of 0.26 times. Conversely, for the same access density (AccessD) condition and urban road with a median, two lanes, and a high side-friction condition, the travel speed can reduce by a factor of 0.52. Based on the results, it is claimed that the multilinear equation of the proposed speed-flow-geometric relationship models can help the urban roads authority to estimate exactly the numerical quantity of average travel speed (ATS) depending on any numerical quantity of geometric variable in the field. Furthermore, this kind of model may allow the engineers and planners to predetermine the range value of speed and flow in an urban road segment based on certain exact values of surrounding geometric and traffic features. Despite the number of models developed in this study being limited for the available cross-section parameter (CSP) conditions of the urban roads segment in the Johor roads network, these models confirmed the benefits for developing the multilinear models on other urban roads network to fulfill these unavailable cross-section parameters (CSP).
## 3 Platform for Real-Life Testing of Communication Protocols
### Monitor Messaging
#### 3.3.3 The PMT-Based Messaging Method  
The monitoring system's baseline implementation uses only PMT data structures built in GNU radio and is versatile. The PMT type requires no structure or schema like JSON, and the messages are self-contained. For this method, the Payload ID field is not required.  
The PMT type has a significant drawback in that it adds overhead to the message size and negatively impacts the performance of serialization/deserialization of the messages. This occurs because it is necessary to send all of the message field names, and for each value, additional information is needed to indicate the value type. Additionally, the traversal of the data structure is performed recursively, which negatively affects performance for highly nested messages.  
In the current implementation of the testing platform, a flat dictionary (i.e., pmt:dict), which contains both header information and payload data, is used. When building the message, the timestamp and payload are added, and the probe-related fields are easily inserted in the dictionary before serialization at the probe (see Figure 11). To make things more convenient, a syntactic sugared message builder API was implemented as a variadic function, which can take any number of (field, value) pairs as parameters, as shown in the example presented in Figure 12.