esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup and Measured Results in the Salt Mine
### Node Deployment Analysis in the Mine
#### 3.2.2 Node Separation  
Figure 6e shows the theoretical and measured values for node separation using wizzi motes with monopole antennas. The theoretical separation was calculated as 148 m, whereas a measured separation of 110 m was achieved in the mine due to unpredicted multi-path and random human activities. For the multi-hop experiments, stationary nodes were placed with equal separations between them, both in tunnel and room-pillar mining. A non-LOS scenario was also tested, where the link went down at 70 m apart with average fluctuations of 5% in RSSI. In the non-NLOS experiments, the pillar width and the dominance of higher mode multi-path reflections had an impact on the separation range. A  
Figure 6: (**a**) RSSI for the tunnel, (**b**) RSSI for the room–pillar scenario, (**c**) RSSI for the node position matrix analysis, (**d**) propagation mode analysis inside the mine, (**e**) node separation range, and (**f**) RSSI for the LOS vs. non-LOS links.  
comparison between the LOS and non-LOS cases in an underground mine is depicted in Figure 6f.  
The results show a smooth increase in RSSI for tunnel mining and an unpredicted deviation for room-pillar mining. A mean deviation in RSSI of 9% was observed in the single-hop scenario, 4% in 2-hop scenario, and 14% in the 3-hop scenario between tunnel and room-pillar scenarios. The maximum calculated node separation was 148 m, with 12.6 dBm transmit power and \(-85\) dBm receiver sensitivity, which increased with an increase in transmit power and antenna gains. The maximum measured node separation with four different node positions was 110 m using the same configuration. The main reason for the decrease in separation range was found to be lossy mine walls and higher propagation mode dominance, but only 6% mean variation was noticed for the node matrix, which indicates that radio link performance is independent of node position. It was noticed that the RSSI can be influenced by various factors, such as human presence, metal object interference, and antenna orientation. These factors were kept constant in repeated experimental trials to ensure consistency in the results.
## 3 Results  
For every patient, the absorbed dose was calculated for the liver, spleen, left and right kidney with three methods using Planet(r) Dose with bi- and tri-exponential fitting, and the Gate Toolkit. Box-plots, as shown in Figure 4, were constructed for the absorbed dose calculated with every method for the OOIs. Table 2 presents the percentage differences between the calculation methods as calculated by:  
\[RD=\frac{\left|experimental\ value-reference\ value\right|}{reference\ value}.100\% \tag{4}\]  
Figure 4: Average absorbed dose calculated for liver, spleen and right and left kidney.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  
In the comparison between GATE and Planet(r), the absorbed doses from GATE were considered reference values and those calculated with Planet(r) were the experimental values. In the comparison between the biexponential and triexponential fitting, the biexponential was considered as reference.  
The average relative differences between GATE and Planet(r) for every organ are presented in Table 3.  
Bland-Altman plots, as shown in Figure 5, were also constructed for every organ of interest to visualize the absorbed dose differences between Planet(r) (bi-exp) and GATE. The red and blue lines indicate the means of the differences between GATE and the biexponential fitting and triexponential fitting, respectively. The dotted lines represent the 95% confidence intervals (CIs) of
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 2 MPC Implementation
### Robust MPC  
MPC architectures can be specifically designed to ensure robust performances for set bounded disturbances within the expected state constraints [44]. The following are some of the most commonly used strategies for ensuring robust control performance:  
* **Min-max:** The min/max MPC approach essentially converts a \"min\" optimization problem into \"min-max\" optimization by decreasing the worst-case objective functions and maximising them across all feasible points in the uncertainty set [45]. In this formulation, optimization is performed with respect to all possible disturbance evolutions. The min-max MPC has been proven to be the most effective for solving linear robust control applications. However, it is also relatively computationally expensive.
* **Constraint tightening:** In this approach, the state constraints are widened by a certain amount to ensure that a trajectory is discovered regardless of the disturbance evolution [46].
* **Tube:** The tube method employs a separate nominal system model and a feedback controller for converging the active state to the nominal state as quickly as possible [47]. This MPC collects all possible state deviations due to disturbances in a robust positively invariant (RPI) set, which are then used to determine the degree of separation of the states from the set of constraints.
* **Multi-stage:** The multi-stage approach accommodates different control decisions at every stage. It is non-conservative in nature due to the availability of measurement information at each time step in the forecast as well as the fact that it can be used to mitigate the effects of uncertainties. The inherent disadvantage of this strategy is that the complexity of the control problem increases as the number of uncertainties and the time between predictions increases [48; 49].
* **Tube-enhanced multi-stage:** This approach combines the advantages of tube-based and multi-stage MPC architectures to furnish more options for optimality versus simplicity trade-offs. This method has been found to be quite useful in system forecasting using various control and uncertainty principles [50; 51].  
Figure 5: Explicit (pre-computed) MPC [43].
## 1 Introduction  
A huge number of small devices such as the internet of things (IoT) will be served in the coming 6-th generation (6G) wireless networks and consequently more wireless resources and improved latency handling capability of the systems are required. The non-orthogonal multiple access (NOMA) transmission technologies [1] draw research attention in fulfilling these requirements and many research works have been resulted so far. The power domain NOMA [2, 3, 4] is attractive since it does not need additional spectral resources or a specific multiple access scheme in accommodating the NOMA principle, where multiple message signals are superposed in a single spectral resource. Since the signals destined to different users are allocated onto separate orthogonal resources in the orthogonal multiple access (OMA), the received signals of the OMA schemes are almost free of the inter-user interference (IUI). This is not true in the power domain NOMA scheme, and the successive interference cancellation (SIC) is a common way to handle the IUI. To maximize the benefit of the NOMA systems, resource allocation acts in a crucial role [5, 6, 7, 8], which incurs additional complexity however. The NOMA principle can be applied to millimeter wave band communications as well [9, 10, 11].
## 6 Conclusions  
This study explores the influence of the mechanical property of the lining subjected to inner surface pressure along the radial direction, and the rheological behavior of the rock mass on the mechanical response of the rock-lining interface under hydrostatic pressure. The main conclusions are summarized as follows:  
(1) When both the inhomogeneous coefficient (\(\delta\)) and the inner surface pressure (\(q(\tau)\)) of the lining in the radial direction are equal to zero, the proposed analytical solution is the same as the existing analytical solution.  
(2) The influences of the change of the negative value of the radially inhomogeneous coefficient of the lining (\(\delta\)) on the radial deformation on the rock-lining interface (\(u_{\rm i}(\tau)\)) and the normalized supporting pressure provided by the lining (\(Q(\tau)/P_{0}\)) are significantly larger than that of the positive value. These parameters decrease with the increase of the radially inner surface pressures of the lining (\(q(\tau)\)).  
(3) The radial deformation on the rock-lining interface (\(u_{\rm i}(\tau)\)) and the normalized supporting pressure provided by the lining (\(Q(\tau)/P_{0}\)) decrease with the increase of the relaxation time of the Maxwell model. The time required for an apparent stabilization of their values increases with the increase of the retardation time of the Kelvin model, but their ultimate stable values are constant.  
Figure 11: Radial deformation on the rock–lining interface (\(u_{\rm i}(\tau)\)) and the normalized supporting pressure provided by the lining (\(Q(\tau)/P_{0}\)) for different relaxation times of Maxwell model (\(t_{\rm m}\)).  
Figure 12: Radial deformation on the rock–lining interface (\(u_{\rm i}(\tau)\)) and the normalized supporting pressure provided by the lining (\(Q(\tau)/P_{0}\)) for different retardation times of the Kelvin model (\(t_{\rm k}\)).  
(4) The functional gradient lining can be applied to the structural design of tunnel engineering in further work.  
Methodology, J.D.; Software, J.D.; Validation, J.D.; Resources, X.Z.; Data curation, X.Z.; Writing--original draft, J.D.; Supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of CCCC Highway Engineering Co., Ltd., China under Grant KJYF-2021-B-20.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
We thank the anonymous reviewers and editors for their constructive comments and suggestions to improve the quality of this article.  
The authors declare that this study received funding from the Key Project of CCCC Highway Engineering Co., Ltd., China. The funder had the following involvement with the study: Analytical Model of Mechanical Responses of Circular Tunnels Considering Rheological Behavior of Surrounding Rock and Functionally Graded Lining.
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 2 Related Works  
In recent years, researchers have proposed several lightweight authentication schemes for the IoT to ensure the security and privacy of IoT. In [15], [PERSON] et al. introduced several security challenges along with necessary security requirements for IoD environments and designed a user authentication scheme for IoD. [PERSON] et al. [16] presented an enhanced authentication scheme for IoD. However, none of the above authentication schemes are secure against physical capture attacks. Because secret information required for authentication is stored in the drone's physical memory, an adversary can easily extract this critical information and perform various attacks.  
Asymmetric encryption solves the above problem very well; for example, [PERSON] et al. [17] proposed an elliptical curve cryptography (ECC)-based certificate authentication scheme for IoD. [PERSON] et al. [18] presented an ECC-based secure and lightweight authentication scheme for IoD. [PERSON] et al. [19] presented an ECC-based authentication method for UAV communication. [PERSON] et al. [20] proposed an ECC-based authentication protocol to provide mutual authentication between UAV and base station devices. [PERSON] et al. [21] devised an authentication scheme for IoD which employs a hash function and authenticated encryption with associative data (AEAD). All of the above schemes use elliptic curve encryption, but this requires a high computational and communication overhead margin for resource-constrained edge devices such as drones.  
To meet the resource-constrained requirements of drones, the use of PUFs in authentication schemes for IoD has become a popular research direction. [PERSON] et al. [22] proposed a PUF-based authentication scheme. [PERSON] et al. [23] designed a similar scheme. [PERSON] et al. [24] proposed a lightweight mutual authentication scheme based on PUF, which includes drone-GS authentication and drone-drone authentication. None of the papers mentioned above protect challenge-response pairs. [PERSON] et al. [25] proposed a lightweight authentication protocol for UAVs using PUFs and chaotic systems to protect the communication between drones and the GS. However, each drone's PUF challenge-response pair is stored in its memory.  
Almost all of these PUF-based papers assume that the PUF is stable, i.e., inputting the same challenge always produces the same response; otherwise, the hashing operation on the response becomes meaningless. Any PUF needs to be recovered before it is used, and the information used to recover the PUF is necessarily recorded in non-volatile memory, which can give away information about the PUF to attackers. Table 1 summarizes some of the important related works.
# Reinforcement Learning Based Resource Management for Network Slicing
## 4 Resource Trading System Using a Markov Decision Process  
In this section, we outline the design of our dynamic resource trading system that aims at maximizing the profit of a slice tenant using an MDP. The MDP is defined by a 4-tuple as \(M=\langle S,A,V,P\rangle\), where \(S\), \(A\), \(V\), and \(P\) denote the state space, action space, reward function, and transition probability, respectively. In any state \(s_{t}\in S\) at time slot \(t\), one of the available actions \(a_{t}\in A\) can be selected. After the selected action has been taken, the state \(s_{t}\) may change to the next state \(s_{t+1}\) with a state transition probability of \(P(s_{t+1}|s_{t},a_{t})\). \(V(s_{t},a_{t})\) represents an immediate reward that can be obtained by taking action \(a_{t}\) in state \(s_{t}\).
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 4 Simulation Studies
### Influence of the Number of UAVs on the Task Allocation Scheme  
During inspection tasks, the more UAVs there are, the less time is required to complete the tasks. However, in practical inspection tasks, not only time but also costs need to be considered. Based on market prices, the rental cost of a UAV is \(\Psi\)150 yuan, the cost of replacing a battery for a UAV is \(\Psi\)70 yuan, and the cost per second of each UAV flight is \(\Psi\)0.1 yuan. By varying the number of UAVs, ten independent optimization runs were conducted, and the average operation time and costs are summarized in Table 6.  
Based on the data in Table 6, it can be observed that when the number of UAVs is 5, the inspection cost is the minimum. On this basis, increasing the number of UAVs will lead to higher rental costs, which have a greater impact on the total cost, resulting in an increase in overall costs. When the number of UAVs decreases, the cost does not decrease because each UAV requires battery replacement, incurring additional expenses. Therefore, the cost of completing the inspection task can be minimized when the number of UAVs is kept as low as possible, just enough to complete the inspection with the original battery.
# Reinforcement Learning Based Relay Selection for Underwater Acoustic Cooperative Networks
## 1 Introduction
### Related Work
In [30; 31; 32], the UWA channel is simulated by integrated empirical formula and statistic model, and the relay selection criteria is similar to wireless sensor networks, mainly by CSI. This paper will systemically analyze how the specific phenomena of UWA affect relay selection criteria in Section 1.2, will design an effective state and reward expression for UWA RL based relay selection with the consideration of UWA propagation delay in Section 3.1. In the above RL based UWA cooperation systems [28; 29; 32; 31; 32], the learning related parameters are static. We consider that the learning process is a dynamic process to explore the possible actions for maximizing the reward. Accordingly, in the early stage of learning, the intelligent agent of RL should explore the possible random actions with high possibility. With the number of iterations increasing, the learning will turn to convergence gradually, then the agent should explore random action with low possibility and exploit the previous learning results for relay selection. Herein, we employ simulated annealing (SA) algorithm to adapt the exploration rate and greedy factor dynamically for better balance between exploration and exploitation. The SA algorithm is one of best-known metaheuristic methods for addressing global optimization problems [33]. The SA algorithm is powerful to be applied in diverse areas, and still attracts much attention in recent research, such as multi-objective optimization problem [34] and federated learning problem [35]. The SA algorithm simulates the cooling down process of solid annealing whose temperature decreases from a high value. With the decrease of temperature, the global optimal solution is explored randomly in the solution space according to the Metropolis criterion to avoid falling into local optimum. For the routing problem, SA is combined into Q-routing algorithm which has self-adaptive learning rate for dynamic exploration [36; 37]. We utilize SA algorithm to dynamically adjust the exploration rate and dynamic greedy factor for RL through the temperature decline rate. The proposed algorithm can better balance the exploration and exploitation process of reinforcement learning, and achieve better long-term performance as the agent can learn the best action in both exploration and exploitation stage.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 1 Introduction
### Research Progress of Thulium-Doped Fiber (TDF) Photodarkening  
Since 1988, [PERSON] et al. [8] found the phenomenon of PD in Tm-doped quartz fiber, the effect of PD has attracted wide attention. Because there is a certain correlation between PD and laser mode, how to suppress the PD and excitation mode disturbance of high-power fiber laser has become an important topic. When the TDF is exposed to strong blue light (475 nm), the visible and near-infrared transmission spectrum of the fiber is significantly reduced, and it is easy for the Ge\({}^{4+}\) ions in the fiber to produce two-photon absorption close to the pump light. In 1993, [PERSON] et al. [9] compared Tm-doped silica fiber and Tm-doped germanosilicate fiber; it was found that thulium ion itself affects the generation of PD. In the same year, [PERSON] et al. [10] found that Tm-doped silica fiber appeared PD when excited by a near-infrared laser (1064 nm). He attributed this phenomenon to ultraviolet and visible light produced by photon avalanche upconversion. In 1995, [PERSON] et al. [11] found the PD effect introduced by near-infrared laser in Tm-doped fluorinated fiber. The typical phenomenon is that a strong loss is found in the visible light area after the laser radiation of 1140 nm. It is believed that the generation of the color center is the cause of strong loss. In 1995, [PERSON] et al. [4] reported the PD-induced absorption of four kinds of Tm-doped ZBLAN fibers under a 1.12 \(\upmu\)m laser. The results show that the relationship between the darkening rate and the pump intensity is quartic and strongly depends on the concentration of Tm\({}^{3+}\) ions. The PD-induced absorption was mainly found in the ultraviolet-visible part of the spectrum, and there are some parts of the absorption spectra extending slightly longer than 1 \(\upmu\)m in the near-infrared. In 1997, [PERSON] et al. [12]observed the photobleaching (PB) of Tm-doped ZBLAN fiber after short-time irradiation under visible light. The bleaching process can be described by the stretched-exponential function of time. Incomplete and slow color center recovery was observed in previously bleached fibers, indicating at least two types of defects exist. In 2010, [PERSON] et al. [13] analyzed the reason for blue light produced by 790 nm pumped TDF and the influence of fiber components. The loss of power of TDF laser in thousands of working hours was less than 1% through component optimization. At the same time, increasing the Tm\({}^{3+}\) doping concentration can effectively reduce the upconversion efficiency and the PD of the optical fiber, and it was found that the PD rate was directly proportional to the number of concentrated ions in energy level \({}^{3}\)H\({}_{4}\). In 2020, [PERSON] et al. [14] demonstrated the PD phenomenon in TDF core-pumped by a 1080 nm laser and the bleaching effect of deuterium (D\({}_{2}\)) loading on photodarkened TDF.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
## 3 Main Experimental Results and Discussion  
### Noise Power Spectral Density Analysis at a Specific Time  
During one week, many measurements were performed at 10 a.m. in the five sites described in the previous sections of the manuscript. For each site, the aim is to measure the noise power spectrum in two specific locations: in the transformer substation, and at the smart electricity meter (i.e., at the customer side). The noise PSD is carried out during a sweep time equal to 412 ms. This duration is very close to the OFDM frame time in the PRIME and G3-PLC protocols.  
Figure 8 gives the evolution of the noise PSD in the transformer substation. Regarding the customer side, the experimental results are summed up in Figure 9.  
Firstly, all sites have the same trend line in terms of background noise level. For instance, regarding the site named \"Urban dense 1\", this level decreases from 80 dB uV at 50 kHz to 40 dB uV at 500 kHz. It corresponds to a reduction of more than 40 dB. It is important to note that the rural site exhibits the lowest values of noise.  
Secondly, for each site, the narrowband noise can be characterized both on the transformer and the customer sides. Interferences due to the radio broadcasting with the PLC transmission are the source of this narrowband noise Therefore, many peaks can be measured; each one corresponds to a frequency of a station (e.g., Europe 1 with a frequency equal to 183 kHz, Radio Monte-Carlo (RMC) with a frequency of 216 kHz, or Radio Tele Luxembourg (RTL) with a frequency of 234 kHz).  
Thirdly, frequency impulsive harmonics, such as at the frequency equal to 100 kHz, may originate from the switching of electrical devices that are connected to the same PLC network (e.g., TVs and phone chargers). These harmonics have a strong impact at the customer side. At the transformer side, their impact is much more moderate.  
Finally, in comparison with the transformer side, the noise level at the customer side is low, because the number of loads connected to the customer's electrical installation is not important at 10 a.m. In that case, the transformer plays a key role. It is important to note that this one is connected to all installations within a site. However, noise measured in the transformer substation is the sum of noise generated by all domestics loads connected to the same branch.  
Figure 7: Transfer function measurement setup.