esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Testbed Demonstration Setup
### Mobile Edge Node Demo: Connectivity on the Move
Internet Control Message Protocol (ICMP) messages were used for validation and compatibility testing between the UE and the central node. The ICMP messages were encapsulated in IP packets within the GPRS Tunneling Protocol (GTP) and converted by the satellite modems into a physical signal (BPSK modulation with coderate 1/2). All tests include functionality evaluation for the considered architecture. As a result it can be stated that the performance and load management for this specific demonstration were limited by capacity. This demonstration shows that a RAN can be directly deployed with a satellite backhaul to connect to a 3 GPP Release 15 core network. The satellite backhaul can handle large RTT compared to terrestrial radio backhauls (The average RTT over half an hour was \(584\,\mathrm{ms}\)). Moreover, the demonstration shows that an edge node can handle two different backhauls at the same time. In the case of a blocking of the SOTM link, a backup solution for the UE traffic is provided by offloading the traffic over local MNO networks via two LTE modems. This demonstration shows that even a small satellite bandwidth is sufficient to provide a satellite backhaul for mMTC sensor applications.
# Reversed Lempel-Ziv Factorization with Suffix Trees +
## 3 Reversed LZ Factorization
### Factorization Algorithm
#### 3.2.3 Time Complexity  
First, let us agree on that we never compute the suffix number of a leaf since this is a costly operation for CST (cf. Table 3). Although we need the suffix numbers at multiple occasions, we can infer them if each player maintains a counter for the suffix number of the currently visited leaf. A counter is initialized with \(1\) (resp. \(2n-1\)) and becomes incremented (resp. decremented) by one when moving to the succeeding (resp. preceding) leaf in suffix number order. This works since both players traverse the leaves linearly in the order of the suffix numbers (either in ascending or descending order).  
Player 2 visits \(n\) leaves, and visits only unvisited nodes during a leaf-to-root traversal. Hence, Player 2's actions take \(\mathcal{O}(n)\) overall time.  
Player 1 also visits \(n\) leaves. Since Player 1 has no business with the non-phrase leaves, we only need to analyze the time spent by Player 1 for a phrase leaf corresponding to a factor \(F\): If \(F\) is fresh, then the root-to-leaf traversal ends prematurely at the root, and hence we can determine in constant time whether \(F\) is fresh or not. If \(F\) is referencing, we descend from the root to the lowest ancestor \(w\) marked in \(B_{\mathrm{V}}\), and compute \(\operatorname{str\_depth}(w)\) to determine the suffix number of the next phrase leaf (cf. Line 15 of Algorithm 1). Since \(\operatorname{depth}(w)\leq\operatorname{str\_depth}(w)\), we visit at most \(|F|+1\) nodes before reaching \(w\). Computing \(\operatorname{str\_depth}(w)\) takes \(\mathcal{O}(1/\epsilon)\) time for the SST, and \(\mathcal{O}(|F|)\) time for the CST. This seems costly, but we compute \(\operatorname{str\_depth}(w)\) for each factor only once. Since the sum of all factor lengths is \(n\), we spend \(\mathcal{O}(n+z/\epsilon)\) time or \(\mathcal{O}(n)\) time for computing all factor lengths when using the SST or the CST, respectively. We finally obtain the time bounds stated in Theorem 1 for computing the factorization.
## 5 Simulation Results and Analysis
### Convergence Analysis  
In the training phase, we trained 100 different maps that were scraped from flight trajectory data around the Paris Charles de Gaulle Airport in November 2017 from [[https://www.flightradar24.com/](https://www.flightradar24.com/)]([https://www.flightradar24.com/](https://www.flightradar24.com/)) (accessed on 5 October 2021). We extracted the location information of 100 nodes from each map as the initial locations. Then, we compared the convergence rates of TQNGPSR, QNGPSR, and DLGR-2 DQ. We used the packet-delivery ratio as a determinant of convergence rate.  
Figure 5 shows the PDR of the learning curve of all the protocols on different maps with continuous iterations. As shown, the GPSR did not need to be trained, so it was a straight line. The nodes were stationary in the training phase for all the protocols. That was because they were hard to converge when the nodes were mobile. The PDR of our proposed routing protocol was best compared with other protocols, and the PDR of the DLGR-2 DQ fluctuated around 0.7. This was attributed to the link quality and total waiting time of packets in candidate forwarding nodes, which could significantly improve the packet-delivery ratio under a relatively heavy traffic load.
# Dynamic Pressure Analysis of Hemispherical Shell Vibrating in Unbounded Compressible Fluid
## 2 Control Equation and Solution
### Incompressible Fluid Solution  
As for a comparison, it is necessary to consider the model of a shell vibrating in the incompressible fluid. The control equation can be expressed as follows:  
\[m\frac{\partial^{2}U}{\partial^{2}t}+k\cdot\
abla^{4}U+\Delta p=0, \tag{27}\]  
\[\frac{\partial u}{\partial r}+\frac{2u}{r}=0, \tag{28}\]  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial r}+\frac{1}{\rho_{o} }\frac{\partial p}{\partial r}=0, \tag{29}\]Equation (27) is the structural control equation, which is the same as Equation (1). Equation (28) is the continuity equation with the constant density. Equation (29) is the equation for the conservation of momentum. The symbols and the boundary conditions of structure are the same as Equation (4), whilst the boundary of the fluid domain is as follows:  
\[u|_{r=r_{s}}=u_{s}(t),\left.u\right|_{r=R->\infty}=0,\left.p\right|_{r=R-> \infty}=p_{0} \tag{30}\]  
According to the continuity equation, it may be concluded as Equation (31):  
\[u(r,t)=\frac{r_{s}^{2}}{r^{2}}u_{s}(t), \tag{31}\]  
While according to the momentum equation, it can be written as:  
\[p(r)=-\rho_{0}\frac{\partial u_{s}}{\partial t}\frac{r_{s}^{2}}{r}-\frac{1}{2 }\frac{r_{s}^{4}}{r^{4}}u_{s}^{2}+C. \tag{32}\]  
Substituting the boundary condition of the far field to Equation (32), it can be found that:  
\[\Delta p=p-p_{0}=\rho_{0}\frac{r_{s}^{2}}{r}\frac{\partial u_{s}}{\partial t} +\frac{1}{2}\rho_{0}u_{s}^{2}\frac{r_{s}^{4}}{r^{4}}. \tag{33}\]  
From the solution of Equation (31), in the condition of incompressible fluid, it is found that the motion of boundary will transfer to the far field simultaneously, and the amplitude decreases with the rate of \(\frac{1}{r^{2}}\). According to Equation (33), it can be observed that the pressure is contributed from two parts [26]. The first term of pressure can be seen as added mass, and the second term is also convective acceleration, which is the same as Equation (5). The pressure under the shell is \(\Delta p=\rho_{0}r_{s}\frac{\partial u_{s}}{\partial t}+\frac{1}{2}\rho_{0}u _{s}^{2}\), which is very different to the solution of compressible fluid condition.
# A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing
## 4 Autonomous Tugboat Element Technologies
### Decision-Making Technology
This method adds the use of Dubins curves to the reactive path planning method of the existing APF (Artificial Potential Field) method to plan feasible paths and calculate obstacle avoidance potential. Initially, a path is generated and followed, and if an obstacle is detected, the obstacle avoidance potential field is recalculated, and the path is regenerated along the optimized path. Although this study proposed a path planning method for a single robot rather than a swarm, it is an algorithm that can be applied to future ship berthing research, considering the complex environment of multiple ships, reefs, and sudden obstacles near the port. In the work by [35], a path planning problem that considers obstacles in two actual harbor environments is presented (as in Figure 14), in contrast to previous studies that  
Figure 12: Trajectories of USV swarm control conducted by [PERSON] et al. [32]. Figure 13: Overview of the concept of visibility graph in path planning problem [33]. performed path following control of the mothership according to the planned path after setting the departure and destination points. To optimize path planning in actual ports, obstacles such as berths, breakwaters, and buoys are represented as polygonal obstacles, and a collision avoidance algorithm is applied based on the ship domain that changes depending on the ship's speed to maintain a sufficient distance from the obstacles. At the same time, an optimization method for path planning, including the effect of wind disturbance and waypoints, is presented. The paper dealt with the optimization problem of path planning that takes into account the complex actual harbor environment of obstacles, external disturbances, and other factors in the berthing situation of a single ship. In addition, if the technique of tracking and controlling the path of the mothership using several tugboats is applied, the path planning optimization problem of the mothership cluster can be solved.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 7 LoRaWAN Improvements  
Sections 3.2 and 5.1 show that LoRaWAN coverage can go up to several kilometers. This involves that single gateway can cover many end devices. Researchers propose several LoRaWAN improvements to deal with network scalability and traffic reliability. This section details the studies  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Studied Security Aspects** & **Description** \\ \hline
[87] & \
\begin{tabular}{c} Data replay attacks for ABP \\ nodes, Eavesdropping, \\ Bit flipping attack. \\ \end{tabular} &
\begin{tabular}{c} Re-keying after every reset of counter overflow. \\ Replace the counter with a nonce for AES counter mode. \\ Add a MIC for application layer. \\ \end{tabular} \\ \hline
[88] & Key management issues. &
\begin{tabular}{c} Add proxy nodes to drive a reputation \\ system enhancing the LoRaWAN security \\ \end{tabular} \\ \hline
[90] & \
\begin{tabular}{c} Replay attack \\ and unique DevNonce \\ \end{tabular} &
\begin{tabular}{c} Use of sequential DevNonce. \\ \end{tabular} \\ \hline
[91] & Replay attack &
\begin{tabular}{c} In addition to DevNonce use RSSI \\ to determine the reply attacks. \\ \end{tabular} \\ \hline
[92] & ATAA join-procedure security &
\begin{tabular}{c} Use hybrid cryptosystem including \\ asynchronous cryptosystem. Here the join-request \\ packet is encrypted using AppKey. \\ \end{tabular} \\ \hline
[93] & Replay attack &
\begin{tabular}{c} Masking the join-request packet by a \\ unique token derived from the previous NetSKey. \\ \end{tabular} \\ \hline
[94] & Replay attack &
\begin{tabular}{c} Two types of join-request packets. The initial ones packets \\ are sent normally. The non-initial packet’s \\ MIC is generated using previous NetSKey. \\ \end{tabular} \\ \hline
[95] & Static Context of
\begin{tabular}{c} It involves session key update mechanism into LoRaWAN based on \\ Ephemeral Diffie-Hellman Over COSE (EDHOC) algorithm. It is shown \\ that the message size used in EDHOC case is around 40\% than in DTLS case. \\ \end{tabular} \\ \hline
[96] & Encryption &
\begin{tabular}{c} Secure Low Power Communication method (SeLPC) that reduces the \\ AES encryption cycles in the end node is proposed. The encryption \\ power is minimized up to encryption power up to 26.2\%. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: LoRaWAN security aspects research.  
related to ADR improvements for improved network scalability as well as different synchronization and traffic scheduling approaches.
# In-Band Full-Duplex Relaying for SWIPT-Enabled Cognitive Radio Networks
## 3 Proposed Solution for SRM Problem
### Tractable Formulation for SRM Problem  
We first introduce a new variable \(\beta\) with an additional constraint: \(\alpha+\beta\leq 1\). Accordingly, \((1-\alpha)\) in (12) and (13) can be equivalently replaced by \(\beta\). This provides a smoothing optimization instead of a strictly-splitting time slot in the TSR protocol. Then, we tackle the non-smooth functions in (12) and (13) by adding the following constraints:  
\[\gamma_{\texttt{ST},\texttt{R}} \geq\frac{1}{t_{\texttt{s}}}\text{ and }\gamma_{\texttt{R}, \texttt{SR}}\geq\frac{1}{t_{\texttt{s}}}, \tag{15a}\] \[\gamma_{\texttt{PT},\texttt{R}} \geq\frac{1}{t_{\texttt{p}}}\text{ and }\gamma_{\texttt{R}, \texttt{PR}}\geq\frac{1}{t_{\texttt{p}}}. \tag{15b}\]  
Therefore, \(R_{\texttt{s}}\) and \(R_{\texttt{p}}\) are respectively expressed as:  
\[R_{\texttt{s}} \geq\beta\ln\bigl{(}1+\frac{1}{t_{\texttt{s}}}\bigr{)}:=R_{ \texttt{s}}, \tag{16a}\] \[R_{\texttt{p}} \geq R_{\texttt{s}}+\beta\ln\bigl{(}1+\frac{1}{t_{\texttt{p}}} \bigr{)}:=R_{\texttt{p}}. \tag{16b}\]By using (16), we can equivalently rewrite (14) as:  
\[\underset{\alpha,\beta,\mathbf{t}}{\text{maximize}} \bar{R}_{\mathbf{a}}+\bar{R}_{\mathbf{p}},\] (17a) subject to \[0\leq\alpha\leq 1,\ 0\leq\beta\leq 1, \tag{17b}\] \[\alpha+\beta\leq 1,\] (17c) \[(\ref{eq:15}), \tag{17d}\]  
where \(\mathbf{t}\triangleq\big{[}t_{\mathbf{s}};\ t_{\mathbf{p}}\big{]}\). We note that Problem (17) is still non-convex due to the non-concave objective in (17a) and the non-convex constraints in (17d).
## 3 Results and Discussion
### Physicochemical Properties
#### 3.1.2 Density  
Density is one of the key indicators of NADESs, which can provide information about intermolecular interactions in eutectic solvents. As a rule, eutectic solvents have a higher density than water [20; 40]. Being a part of the structure of NADES, HBD, or HBA and having a lower density compared to NADES, water reduces the density and viscosity of solvents. The densities of the tested NADES as a function of added water content after dilution are presented in Figure 2.  
The density values ranged from 1.226 \(\pm\) 0.010 g/cm\({}^{3}\) (NADES1 at 0% water) to 1.510 \(\pm\) 0.050 g/cm\({}^{3}\) (NADES2 at 0% water) and decreased linearly with the increase in water content. The density of NADES2 following a function \(\rm y_{2}=-0.0051x_{2}+1.5062\), R\({}^{2}\) = 0.996 (\(\rm y_{i}\): density; \(\rm x_{i}\): water content percentage for relevant NADES). A similar linear relationship was also observed in NADES3 (\(\rm y_{3}=-0.0041x_{3}+1.3904\), R\({}^{2}\) = 0.99) and NADES1 (\(\rm y_{1}=-0.0022x_{1}+1.2213\), R\({}^{2}\) = 0.989). Using these equations, the density of NADES can be calculated for a known amount of dilution with water.  
Notably, the coefficient for water content (equation for NADES1) was lowest among other tested NADES. This indicates a weak impact of water on the density of NADES1 (ChCl:LA 1:3) (Figure 2). Similar trends for NADES containing ChCl:LA were observed previously. The density of NADES containing ChCl:LA (at the molar nations 1:1-1:2.5) diluted with water (up to 22 wt%) [41] and ChCl:LA 1:1 with water contents up to 50 wt% [42] has a linear dependence on the amount of added water and varies slightly [20]. We suppose that the increase in LA content in NADES resulted in a decrease in NADES density.  
Figure 1: The relationship curves between hydrogen bond strength and water percentage of the tested NADES at room temperature. NADES1—LA:ChCl (3:1); NADES2—LA: SL (1:3); NADES3—LA:GL:W (3:5:1).  
The literature data evidence the influence of composition and molar ratio of HBA and HBD on the density of the eutectic solvents. [PERSON] et al. (2019) compared the density of different molar ratios of ChCl and DES based on citric acid (CA) [37]. It was reported that the density of the eutectic mixture decreased with increasing ChCl relative to CA. The study [43] showed that increasing density either by replacing HBD or increasing the molar amount of HBD promotes an association between HBD molecules. The addition of water up to 40% decreases the density of NADES. According to the results [44], the polar components of eutectic mixtures interact with water, and an increase in the percentage of water leads to a gradual weakening of the strength of hydrogen bonds. For solvents containing ChCl, the NADES nanostructure remains stable up to 42 wt% water due to solvophobic water sequestration into nanostructured domains around choline cations. However, increasing water to 51% leads to the destruction of the DES nanostructure, and the solvent is converted into an aqueous solution of the DES components [45].
## 4 Results and Discussion  
### Effects of Air Speed on Heat Exchanger Pressure Drops  
The effect of airflow speeds on the pressure drops of heat exchangers is depicted in Figure 6. Three trends can be observed. Firstly, the pressure drop increases almost linearly as the airspeed increases, irrespective of surface coating conditions. This is expected due to larger flow frictional resistances at higher speeds. Secondly, a larger fin pitch results in a smaller pressure drop, also irrespective of surface coating conditions; also expected because of smaller geometric constraints on the airflow at larger fin pitches. Lastly, by compared to the uncoated cases, hydrophobic surface coating leads to larger pressure drops; whereas, hydrophilic surface coating leads to smaller pressure drops. Specifically, compared to the uncoated cases, the pressure drops of hydrophilic coated cases can be reduced by about 20-24%, 36-43%, and 43-50% for the fin pitches of 1.5 mm, 2 mm, and 2.5 mm, respectively. This kind of trend was also observed by [PERSON] et al. (2019). The pressure drop of the hydrophilic coated surface of pitch 1.5 mm (having the largest fin density of all the cases investigated as listed in Table 1) is still smaller than all of the uncoated cases, indicating the effectiveness of hydrophilic surface coating in reducing the pressure drop of fin-and-tube exchangers by modifying the surface morphology as illustrated in Figure 1. In practical applications, reducing the pressure drop is essential to save fan energy. Even  
Figure 5: Experimental flowchart.  
though a lower pressure drop can be obtained by a larger fin pitch, enlarging the fin pitch is not desirable because it will reduce heat transfer due to a smaller FPI. Namely, it would be more practical to lower the pressure drop by surface coating than by enlarging the fin pitch. It should also be cautioned that the hydrophobic surface coating of 1.5 mm in fin pitch results in a considerably larger pressure drop by about 55% to 75% and should be avoided if environmental contamination could lead to hydrophobic surface modifications from the originally uncoated surfaces.
## 3 Results
### Cavity of Length \(L=\lambda/4\)
#### 3.1.4 Several Source Frequencies  
A study similar to Sections 3.1.2 and 3.1.3 is carried out by keeping \(p_{s}\) constant at \(p_{s}=20\) kPa and \(v_{v}\) constant at \(v_{v}=3.2725\times 10^{-5}\) for different source frequencies. Figure 4 represents the maximum amplitude of the 2 nd, 3 rd, and 4 th harmonics vs. ratio \(f_{0}/f\) for \(f=200\) kHz, \(f=300\) kHz, and \(f=400\) kHz. The lowest \(f\) value generates the higher harmonic amplitudes. The difference observed in the harmonic generation with the source frequency is more accentuated for the 4 th and 3 rd harmonics, although the latter to a lesser extent. The growth with \(f_{0}/f\) is similar to the previous sections, when \(f_{0}/f\) increases from 3 to 20 the harmonic amplitudes grow quickly, whereas for values of \(f_{0}/f\) from 20 to 65 their growth is slower and ends up to be constant.  
To summarize, in this resonator, larger source amplitudes, smaller source frequencies, more amount of air and, for the same void fraction, more smaller bubbles, are more convenient and efficient for the generation of more intense harmonics.  
Figure 3: Maximum amplitude of \(2f\) (**a**), \(3f\) (**b**), \(4f\) (**c**) in the cavity of length \(L=\lambda/4\) vs. \(f_{0}/f\), for three different void fractions \(v_{v}\) (blue line), \(2v_{v}\) (green line), and \(3v_{v}\) (red line).
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 10 Proposed Solution: Background, Implementation, and Simulation Results
### Q-Learning  
Q-Learning (QL) [54] operates by creating a table of q-values using a \(Q(s,a)\) function that estimates the rewards for each state/action pair. \(Q(s,a)\) is the q-value of action \(a\) when the system state is \(s\). The q-value indicates the desirability of taking action \(a\) when in state \(s\). The higher the q-value, the more desirable it is.  
The values estimated by this function are updated with each iteration, according to Equation (2) [49]:\[Q(S_{t},A_{t})\gets Q(S_{t},A_{t})+\alpha[R_{t+1}+\gamma\max_{a}Q(S_{t+1},a)-Q( S_{t},A_{t})], \tag{2}\]  
where \(R_{t+1}\) is the reward from action \(A_{t}\) when the state is \(S_{t}\); \(a\) is the learning rate, which determines the impact of new information in the update of the q-values; and \(\gamma\) is a discount factor, which determines the impact of future rewards. Its value ranges from 0 to 1, and values close to zero indicate that future rewards are not important. In this case, immediate rewards have a greater impact in the learning process.  
Therefore, the algorithm's goal is to find the optimum \(Q(s,a)\) table, which gives an optimum policy \(\pi^{*}\). This policy corresponds to choosing action \(a\), given a state \(s\), as defined in Equation (3):  
\[\pi^{*}(s)=arg\max_{a}Q(s,a). \tag{3}\]  
The author of [55] demonstrates that, given enough samples, the QL algorithm converges to a function directly derived from the Bellman equations, which is the classic solution of a Markov Decision Process (MDP). The key to that convergence is a Markovian Process named Action-Replay Process (ARP). It is possible to use the weak law of large numbers to prove that the policy \(\pi\) gets closer to the optimum policy \(\pi^{*}\) for a growing number of samples.
# Developing Quantum Trusted Platform Module (QTPM) to Advance IoT Security
## 4 Quantum Entropy as a Service (QEaaS)
### Distributed Entropy Sourcing for Enhanced Privacy  
When a single server provides all the entropy to an IoT device, it could potentially track or infer sensitive data about the device's operations, such as cryptographic keys.  
Figure 3: QEaaS framework for IoT security.  
By requesting entropy from multiple sources, no single entity has complete visibility into the randomness used by the IoT device. Even if one server is compromised or malicious, the combined entropy from multiple sources can still maintain the desired randomness and unpredictability. The idea is to ensure that the final random value used by the IoT device is unpredictable unless all servers are compromised.  
The IoT device pre-configures a set of entropy sources (QEaaS servers) to request entropy from, while these servers could be operated by different entities or organizations to reduce the risk of collusion or a single point of failure. The IoT device sends entropy requests to multiple servers, ideally in parallel, to minimize latency. These requests should be structured to ensure that each server only provides part of the total entropy needed. Once the entropy is received from all servers, the IoT device combines the different entropy inputs using a secure combination method, such as bitwise XOR operation or hashing. The entropy combination operation ensures that if even one of the entropy sources is truly random and unpredictable, the final result will also be random and secure. The IoT device uses the combined entropy for cryptographic operations, such as key generation. Since the final entropy is derived from multiple sources, it is highly secure and ensures privacy, as no single server can infer the actual randomness used. One potential issue of the proposed method is the added latency from requesting entropy from multiple servers. To mitigate this, we can consider caching entropy locally at IoT devices for short-term use, pre-fetching entropy during periods of low activity, and utilizing parallel requests to minimize waiting times. Implementing the QEaaS platform will be pursued as part of our future work.