esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application to Quantum Computing Devices: Mach-Zehnder Interferometer
Then, a tunable potential mesa generated by top gates further separates the two quantum rails, so that the traveling electron accumulates a different phase according to its path [34] as shown in Figure 4a. The two electron beams are then recollected at the second beam splitter to produce the electron interference which is the second step in a quantum device for the (unitary) manipulation of our initial quantum logical information. The final step is the measuring of the observable associated with the final qubit \(a^{\prime}|0\rangle+b^{\prime}|1\rangle\) by the detectors \(D1\) and \(D2\) in Figure 4. As in the case of the RTD, typically the electrical current (which is proportional to the transmission probability from source \(i=1\) to detector \(j=1,2\)) is used to indirectly identify the final qubit [35]. The magnetically dependent transmission probability \(T_{21}(B)\) for an electron in the input channel S1 to be detected at D2 can be analytically computed by means of a simplified 1D model based on the scattering matrix approach, as in Ref. [34] and reads:  
\[T_{21}(B)=2T(1-T)(1+\cos(\Phi)), \tag{13}\]  
Figure 4: **(a)** 3D view of the potential landscape felt by the electrons in the MZI at bulk filling factor 2 in the Integer Quantum Hall regime, in presence of a perpendicular magnetic field [34]. Electrons are injected in the first edge channel by the source \(S_{1}\), and collected at the end of the device by the drain \(D_{1}(D_{2})\). The paths of the electrons in the interferometer is defined by the red (blue) line for the first (second) Landau level. **(b)** Schematic diagram and functioning of the MZI. Electron injection with non-interacting and non-overlapping wavepackets is shown in gray, while the region encapsulated by the cyan color dashed line box defines the region where the unitary evolution of the quantum states is preserved. where \(T\) is the transmission coefficient of the single beam splitter and \(\Phi\) is the total phase difference accumulated in the MZI:  
\[\Phi=\frac{qB\mathcal{A}}{\hbar}+\varphi. \tag{14}\]  
Aharonov-Bohm oscillations in the transmission amplitude [36] are then driven by a variation of the magnetic field \(B\) or the loop area \(\mathcal{A}\), affecting the phase \(\Phi\). This platform can be used to implement other electron interferometry schemes for single electrons, e.g., Fabry-Perot geometries [27], or two interacting electrons [31; 37; 38]. Let us notice that the consideration of more qubits in the unmodified quantum device will just require occupying different Landau levels, but the basic understanding of how the uncertainty in the measurement can be controlled will not be modified by the presence of more qubits.
## 5 Conclusions  
An analytical two-dimensional (2D) dynamic tunnel-soil-tunnel interaction model was presented for the linear out-of-plane response of the seismic interaction between twin tunnels in the urban area. The closed-form analytical solution for tunnel response with consideration of the influence of tunnel relative stiffness was obtained by the wave function expansion method. The effects of tunnel relative stiffness on tunnel displacement response were also investigated in this paper based on the analytical solution. The simplicity of this analytical model made it possible to explain the main features of tunnel-soil-tunnel interaction with consideration of the influence of tunnel relative stiffness. The conclusions and findings are as follows.  
1. Tunnel relative stiffness has a great influence on tunnel displacement response. When tunnel relative stiffness is small, tunnel displacement amplitude can reach about 3.3 times larger than that of the single rigid tunnel. A tunnel cannot be treated as a rigid body even for a tunnel relative stiffness that is many times (\(\beta_{0}/\beta=100\)) larger than that of the surrounding soil.
2. The tunnel-soil-tunnel interaction depends not only on the spacing distances between the tunnels but also on the frequency of the incident wave and the incident angle. Especially for higher frequency waves with short wavelength, when the wave is incident obliquely, the tunnel on the incoming wave side has the obvious shielding effect on the tunnel at the outgoing wave side.
3. The strength of the interaction between the tunnels is highly related to the tunnel spacing distance. The smaller the spacing distance between tunnels, the stronger the interaction between them. When the distance between tunnels reaches \(s/a=20\), the interaction between tunnels can be ignored. It is worth pointing out that the seismic design of underground tunnels should consider the interaction between tunnels when the tunnel distance is small.  
**Author Contributions:** Conceptualization: [PERSON] and Z.Z.; mathematical derivation: [PERSON] and L.J.; computer program: [PERSON] and L.J.; validation: X.L.; data curation: X.L.; writing (original draft preparation, review, and editing): L.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study is supported by the National Natural Science Foundation of China under Grants 51808290, U2039208, and U1839202, which are gratefully acknowledged.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare that the work is original research that has not been published previously and is not under consideration for publication elsewhere. No conflicts of interest exist in the submission of this paper, and the paper is approved by all authors for publication.
# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
###### Abstract  
This paper focuses on proposing a new access barring scheme for internet of things (IoT) devices in long term evolution advanced (LTE/LTE-A) and 5G networks. Massive number of IoT devices communicating simultaneously is one of the hallmarks of the future communication networks such as 5G and beyond. The problem of congestion also comes with this massive communication for which access barring is one of the solutions. So, it is required that sophisticated access barring techniques are designed such that the congestion is avoided and these devices get served in less time. Legacy access barring schemes like access class barring (ACB) and extended access barring (EAB) suffer from high energy consumption and high access delay respectively. However, our proposed scheme provides less energy consumption than ACB while giving less access delay than EAB. The proposed scheme maximizes the success probability while reducing the number of collisions at the same time. The scheme is based on an approximation of the number of IoT devices based on details available to the eNodeB of the number of idle, successful and collided preambles. Extensive Matlab simulations are performed to validate our claims and analysis.  
access class barring; extended access barring; IoT; total service time; 5G communications
## 3 Mathematical Investigations
### Periodic Solutions  
Then, we discuss the solvability conditions and introduce detuning parameters \((\sigma_{1})\)&\((\sigma_{2})\) to achieve:  
\[\left.\begin{array}{c}\omega=\frac{1}{2}\omega_{1}+\varepsilon\sigma_{1},\\ \omega_{2}=\omega_{1}+\varepsilon\sigma_{2}.\end{array}\right\} \tag{18}\]  
Including Equation (18) the equation is divided into secular and small terms by separating (16) and (17). To gather the solvability conditions, the process involves:  
\[\left(-2i\omega_{1}DA-\alpha_{1}i\omega_{1}A-3\beta A^{2}\overline{A}-10\mu A ^{3}\overline{A}^{2}\right)+\frac{f}{2i}e^{2i\sigma_{1}\tau_{1}}+\lambda_{1}Be^ {i\sigma_{2}\tau_{1}}=0, \tag{19}\]  
\[-2i\omega_{2}DB-i\alpha_{2}\omega_{2}B+\lambda_{2}Ae^{-i\sigma_{2}\tau_{1}}=0. \tag{20}\]  
To scrutinize the key of (19) and (20) exchanging \(A\) and \(B\) by the polar form as follows:  
\[A(T_{1})=\frac{1}{2}a_{1}(T_{1})\;e^{i\theta_{1}(T_{1})},DA(T_{1})=\frac{1}{2 }\left(\dot{a}_{1}(T_{1})\;+ia_{1}(T_{1})\dot{\theta}_{1}(T_{1})\right)e^{i \theta_{1}(T_{1})}, \tag{21}\]  
\[B(T_{1})=\frac{1}{2}a_{2}(T_{1})\;e^{i\theta_{2}(T_{1})},DB(T_{1})=\frac{1}{2 }\left(\dot{a}_{2}(T_{1})\;+ia_{2}(T_{1})\dot{\theta}_{2}(T_{1})\right)e^{i \theta_{2}(T_{1})}. \tag{22}\]  
where \(a_{1}\) and \(a_{2}\) are the steady state phases and amplitudes of the motion of the system and controller, respectively, and the phases of motion are signified by \(\phi_{1}\)&\(\phi_{2}\). Inserting (21) and (22) into (19) and (20), upon calculation, the resulting equations governing amplitude and phase modulation are as follows:  
\[\dot{a}_{1}=-\frac{1}{2}\alpha_{1}a_{1}-\frac{f}{2\omega_{1}}\cos\phi_{1}+ \frac{\lambda_{1}}{2\omega_{1}}a_{2}\sin\phi_{2}, \tag{23}\]  
\[a_{1}\dot{\theta}_{1}=\frac{3}{8\omega_{1}}{\beta a_{1}}^{3}+\frac{5}{16\omega _{1}}{\mu a_{1}}^{5}-\frac{f}{2\omega_{1}}\sin\phi_{1}-\frac{\lambda_{1}}{2 \omega_{1}}a_{2}\cos\phi_{2}, \tag{24}\]  
\[\dot{a}_{2}=-\frac{1}{2}a_{2}a_{2}-\frac{\lambda_{2}}{2\omega_{2}}a_{1}\sin \phi_{2}, \tag{25}\]  
\[a_{2}\dot{\theta}_{2}=-\frac{\lambda_{2}}{2\omega_{2}}a_{1}\cos\phi_{2}. \tag{26}\]  
where \(\phi_{1}=2\sigma_{1}T_{1}-\theta_{1}\) & \(\phi_{2}=\sigma_{2}T_{1}+\theta_{2}-\theta_{1}\). To return to the major system parameters, consider the following equations:  
\[\dot{a}_{1}=-\frac{1}{2}\alpha_{1}a_{1}-\frac{f}{2\omega_{1}}\cos\phi_{1}+ \frac{\lambda_{1}}{2\omega_{1}}a_{2}\sin\phi_{2}, \tag{27}\]  
\[a_{1}\dot{\phi}_{1}=2\sigma_{1}a_{1}-\frac{3}{8\omega_{1}}{\beta a_{1}}^{3}- \frac{5}{16\omega_{1}}{\mu a_{1}}^{5}+\frac{f}{2\omega_{1}}\sin\phi_{1}+\frac {\lambda_{1}a_{2}}{2\omega_{1}}\cos\phi_{2}, \tag{28}\]  
\[\dot{a}_{2}=-\frac{1}{2}\alpha_{2}a_{2}-\frac{\lambda_{2}}{2\omega_{2}}a_{1} \sin\phi_{2}, \tag{29}\]\[a_{2}\dot{\phi}_{2}=(\sigma_{2}-2\sigma_{1})a_{2}+a_{2}\dot{\phi}_{1}-\frac{ \lambda_{2}}{2\omega_{2}}a_{1}\cos\phi_{2}. \tag{30}\]  
The control rule's performance will be assessed by calculating the equilibrium solutions of (27), (28), (29) and (30) and assessing their stability as a function of the parameters \(\sigma_{1},\sigma_{2},\eta,\beta,\gamma,\lambda_{1},\lambda_{2}\) and \(f\).
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
## 4 Background
### Q-Learning  
In Reinforcement Learning (RL), the problem is modeled by a Markov Decision Process (MDP). A MDP is described by a tuple \((S,A,P_{a},R_{a})\) where  
* \(S\) is a set of states called the state space,
* \(A\) is a set of actions called the action space,
* \(P_{a}(s,s^{\prime})\) is the probability that taking the action \(a\in A\) in state \(s\in S\) leads to state \(s^{\prime}\in S\),
* and \(R_{a}(s,s^{\prime})\) gives the immediate reward received after transitioning from state \(s\) to state \(s^{\prime}\), due to action \(a\).  
Q-learning is a model-free RL algorithm where the goal is to learn a policy telling an agent what action to take in a given state. In this model, there is a Q-value for every particular state-action combination, and those Q-values are stored in a Q-table. A higher Q-value represents the higher chance of getting a greater reward.  
We denote the Q-value of getting an action \(a\) in state \(s\) for policy \(\pi\) as \(Q^{\pi}(s,a)\). Q-values are initialized to an arbitrary value, and in an iterative process, the agent takes different actions and updates the Q-values in the Q-table. At each time step \(t\), by performing the action \(a\) in state \(s\), its Q-values are updated according to the following Bellman equation:  
\[Q_{t+1}(s,a)=(1-\alpha)Q_{t}(s,a)+\alpha\Big{[}R(s,a)+\lambda\max_{a^{\prime }\in A}Q_{t}(s^{\prime},a^{\prime})\Big{]}, \tag{7}\]  
where \(\alpha\) is the learning rate (\(0<\alpha\leq 1\)), \(\lambda\) is the discount factor (\(0\leq\lambda\leq 1\)) that shows the importance of future reward, and \(R\) is the immediate reward of this transition.  
Because of the exploration/exploitation trade-off, an \(\epsilon\)-greedy model is used during training and action selection. Instead of always choosing the best learned Q-value action (exploitation), a random action is selected with \(\epsilon\) probability (exploration). This behavior leads in making random decisions sometimes instead of always selecting the best learned Q-value action. Therefore, for choosing action \(a\) at state \(s\) the following model is used:  
\[a=\left\{\begin{array}{ll}\text{random action $\in A$,}&\epsilon-probability\\ \text{argmax}_{a}(Q(s))&\text{otherwise}\end{array}\right. \tag{8}\]  
Eventually, the Q-values converge and the learned values can be used as an action-value function for the agent. However, to learn better and converge faster, Decaying Epsilon Greedy can be used, which tries to decrease the percentage dedicated for exploration over time. At the beginning, the epsilon value is high (close to 1) so that the model can explore and learn things. As it learns, epsilon should decay so that the model can exploit the higher Q-values that are obtained.  
The optimal policy is composed of the actions with the highest Q-value for each state, denoted by \(Q^{*}(s,a)\).
# Unified Analytic Melt-Shear Model in the Limit of Quantum Melting
## 2 Methods
### Cold Free Electron Gas  
For the electron subsystem alone, usually two parameters are introduced, the (dimensionless) density parameter, \(s\), and the average number density (number of particles per unit volume), \(n\), related to each other via  
\[n^{-1}\equiv\frac{4\pi}{3}(sa_{B})^{3};\ \ \ a_{B}=\frac{\hbar^{2}}{m_{e}e^{2}}=5.29\cdot 10^{-9}\ \mathrm{cm}\,\ \mathrm{is\ the\ electron\ Bohr\ radius}\]  
The average energy per electron, \(E(s)\), is the sum of the three terms,  
\[E(s)=\bar{E}_{f}(s)+\bar{E}_{x}(s)+\bar{E}_{c}(s),\]  
where  
\[E_{f}=\frac{3}{5}\,\frac{p_{f}^{2}}{2m_{e}}=\frac{2.21}{s^{2}}\,\mathrm{Ry}, \ \ \ p_{f}=\left(\frac{9\pi}{4}\right)^{1/3}\frac{\hbar}{sa_{B}},\ \ \ \mathrm{Ry}=\frac{m_{e}e^{4}}{2\hbar^{2}}.\]  
Here \(\bar{E}_{f}\) is the average zero-point kinetic energy, and \(p_{f}\) and \(E_{f}\equiv 5/3\ \bar{E}_{f}\) are the Fermi momentum and Fermi energy, respectively. \(\bar{E}_{x}\) is the well-known expression for the electron \"exchange energy\":  
\[\bar{E}_{x}(s)=-\frac{3}{4}\bigg{(}\frac{9}{4\pi^{2}}\bigg{)}^{1/3}\frac{e^{2} }{sa_{B}}=-\frac{0.916}{s}\,\mathrm{Ry},\]  
while all higher order terms are lumped into the \"correlation energy\" \(\bar{E}_{c}\), the calculation of which is model dependent.
# Improving IoT Data Security and Integrity Using Lightweight Blockchain Dynamic Table
## 5 Proposed Solution
### Multiple AES Encryption  
At the beginning of its work, the device needs to send its unique array to the cloud using multiple encryptions, which we will talk about in the next section. On the cloud side, the cloud will decrypt the block and regenerate the array based on the data it has access to and using the array it owns. The generated array must match the array sent by the IoT. When matching occurs, the new array will be stored as a new device identifier. In the event of inconsistency, the received data will be discarded. Using this method will make it impossible to guess the device ID, as it changes every time, which gives the system additional layers of protection against attacks. On the other hand, any modification to the data will lead to a wrong result in generating the matrix, and this is what makes the protection of data integrity among the points covered by the proposed work.  
Multiple data encryption is a method that has been proposed to reduce the amount of processing required in encryption while maintaining the level of security. The idea as shown in Figure 4 is to split data into multiple parts and encrypt each part with a different, small-sized key. In the proposed system, the data will be hashed into parts of 64 bytes and then four AES keys of the same size are generated, and then each piece of data is encrypted with a separate key. This method will also increase the data protection against Brute Force attacks, because the attacker will need to guess four keys instead of one. In addition, the result of the data after encryption will be easy to send without increasing the load on the network, because its size is smaller than the data encrypted with a long key.  
Figure 4: Multiple AES encryption architecture.  
There is a strong likelihood that adversaries will use packet sniffing as a technique to launch an assault on wireless transmission systems. A transceiver is used by the attacker to intercept packets that are sent between gateways and nodes or the other way around. The integrity of the network and the validity of the data being compromised by the prospect of the attackers introducing these recorded packets back into the system. Furthermore, a Man-In-The-Middle attack is also a threat that should be handled for any new security solution for IoT. However, a solution can be proposed that protects both the block and dynamic table. First, each block is encrypted with four AES keys; therefore, extracting the data and dynamic array from the transfer packet requires guessing the four keys first. Second, the array is changed with every sensed data and sent to the server, which verifies each block before being accepted. Any block that does not pass the block checking mechanism will be dropped.
# Hybrid Adaptive Bias OFDM-Based IM/DD Visible Light Communication System
## 2 System Model
### Transmitter
Additionally, if the four signals are all non-negative, no bias is required to ensure the non-negativity of the four signals. From (8), if at least one signal in \(\{w_{n},w_{N/4-n},w_{N/4+n},w_{N/2-n},w_{N/2+n},w_{3N/4-n}\), \(w_{3N/4+n},w_{N-n}\}\) for \(n=1,\cdots,N/8-1\) is negative, the adaptive bias can be generated with the inverse polarity of the minimum of \(\{w_{n},w_{N/4-n},w_{N/4+n},w_{N/2-n},w_{N/2+n},w_{3N/4-n}\), \(w_{3N/4+n},w_{N-n}\}\). In addition, if the eight signals are all non-negative, there is no need to produce the adaptive bias. Furthermore, it can also be observed that adaptive bias is selected according to the different amplitude of the signal, which can decrease the power consumption, and still ensure the non-negativity of the signal. As a result, the superimposed time-domain signal of the proposed scheme is given by  
\[z_{n}=w_{n}+s_{n},\quad n=0,1,\cdots,N-1. \tag{9}\]  
Through the introduction of the adaptive bias, the non-negativity of the transmission signal is dynamically adjusted to realize the simultaneous transmission of different optical OFDM signals, while maintaining high spectrum utilization.
# Plasmonic Tweezers towards Biomolecular and Biomedical Applications
## 3 Major Designs
### Double Nanoholes  
Double nanoholes are the most used nanostructures for PTs [69]. Using a double nanohole with 15 nm gap, [PERSON]'s group have trapped single BSA protein (\(\sim\)3.4 nm) between the two tips in 2012 [70]. They also observed the unfolding process of a trapped protein by changing the PH value of the solution and changing the laser power. Besides the BSA protein, they have trapped and manipulated DNAs [71] and single egg white protein [72], and examined biotin\(-\)streptavidin, biotin\(-\)monovalent streptavidin, and acetylsalicylic acid\(-\)cyclooxygenase 2 interactions [73]. As shown in Figure 4, an experimental trapping scheme is demonstrated on the left side. On the right side, the top figure is an SEM image of a double nanohole and the bottom figure is the transmission signal. With a protein trapped, the transmission signal was increased due to the locally increased refractive index introduced by the presence of the protein. Two states of the protein were observed since the unfolded protein occupied more volume between the two tips. Using the transient time (\(\tau\)) of the upward steps in the transmission signal, the trap stiffness of these PTs can be calculated using Equation (12) [60]:  
\[\tau=\frac{\gamma}{k}, \tag{12}\]  
where \(k\) is the trap stiffness and \(\gamma\) is the Stokes drag coefficient. Modified double nanohole design-arrays of nanoholes connected along one direction were used in trapping multiple 30 nm polystyrene particles and sequence trapping events were observed with multiple upward steps in the transmission signal [45].  
Figure 3: (**a**) schematic of the nanoscale particle trapping and SH monitoring concept. Top view (top) and side view (bottom) of (**b**) the simulated near field and (**c**) SEM images of a fabricated BNA; (**d**) SH signal and (**f**) transmission signal with 10 mW trapping power and (**e**) SH signal and (**g**) transmission signal with 40 mW trapping power; adapted from [66] with permission @ 2018 Springer Nature.
# Investigations of the Energy Transfer
## 3 Results
### Ultrafast Dynamics in Allophycocyanin  
#### 3.2.1 Time-Resolved Fluorescence  
The fluorescence kinetics of APC excited at 580 nm was monitored over the spectral region extending from 640 to 710 nm, and the data are displayed in Figure 5a. Global analysis was applied to investigate the fluorescence kinetics of APC using a sequential model and two compartments. In order to obtain a satisfactory fitting, two spectro-kinetic components were needed. The values of the time components were 380 ps and 2 ns. The EAFS connected to the first compartment showed a maximum at 655 nm, while the one characteristic to the second compartment presented a maximum at 660 nm, which was similar to the steady-state emission spectrum of APC (Figure 5b). Additionally, the short EAFS presented a sudden decrease at emission wavelengths longer than 665 nm. The presence of the faster kinetic component showing a fluorescence maximum shifted to shorter wavelengths may indicate the emission from spectrally different chromophores that transfer their energy to the terminal emitter in APC right after the excitation. The long component obtained here represents the fluorescence lifetime of the final emitting chromophores in APC. Since the value of the short component is in the time range of our IRF, this kinetic component could be limited by the temporal resolution of our detection system and not represent the true decay time associated to the energy transfer among the chromophores in APC. To understand the origin of this component, we performed transient absorption experiments, as presented in the next section.  
Figure 5: (a) Time-correlated single photon counting (TCSPC) data showing the three-dimensional data carpet obtained from allophycocyanin (APC) when pumped at 580 nm. The white line corresponds to the steady-state fluorescence spectrum; (b) Evolution-associated fluorescence spectra as obtained from global analysis using a sequential model.