esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The TRAJ-Q-GPSR Algorithm
### Q-Learning Framework and Theory  
Q-learning, a model-free reinforcement learning method, enables agents to learn optimal policies through interaction with their environment, adapting to unknown conditions. Its low complexity and adaptive nature make it ideal for optimizing routing decisions on edge devices like UAVs, particularly given the dynamic and uncertain topology of UAV networks caused by node mobility.  
Q-learning is based on value iteration, and its theoretical foundation lies in the Markov Decision Process (MDP). An MDP provides a rigorous mathematical framework for describing how an agent learns the optimal policy in a stochastic environment, and is defined by the five-tuple \((S,A,P,R,\gamma)\). Here, the state space \(S\) represents all possible states of the system; in our study, the state includes the UAV node's current position, velocity, neighbor information, and queue status. The action space \(A\) encompasses all possible actions that the agent can take, corresponding to the selection of a next-hop forwarding node. The state transition probability \(P\) defines the probability that the system transitions to a new state \(s^{\prime}\) after the agent takes an action \(a\) in state \(s\); this transition is influenced by the UAV's motion model and changes in the network topology. The reward function \(R\) measures the immediate gain obtained by executing action \(a\) in state \(s\), thereby reflecting the quality of that action. The discount factor \(\gamma\in(0,1]\) is used to balance the importance of immediate and long-term rewards, with larger \(\gamma\) placing more emphasis on long-term returns and smaller \(\gamma\) on immediate gains.  
The fundamental principle of Q-learning relies on an iterative update mechanism between states, actions, and rewards. Its objective is to find the optimal action-selection strategy for any state \(s\) by continuously exploring, experimenting, and updating Q-values, thereby enhancing routing efficiency and network adaptability. Specifically, when an agent  
Figure 3: Theoretical framework of Traj-Q-GPSR.  
in state \(s\) chooses an action \(a\), it receives an immediate reward \(R(s,a)\) and transitions to a new state \(s^{\prime}\). The agent maintains a Q-value table \(Q(s,a)\) and updates it using the Bellman equation:  
\[Q(s,a)\gets Q(s,a)+\alpha\bigg{[}R(s,a)+\gamma\max_{a^{\prime}}Q(s^{\prime },a^{\prime})-Q(s,a)\bigg{]} \tag{21}\]  
Here, \(\alpha\) is the learning rate, determining the influence of newly acquired information on the existing Q-values, and \(\max_{a^{\prime}}Q(s^{\prime},a^{\prime})\) represents the maximum Q-value achievable from the new state \(s^{\prime}\), i.e., the maximum expected future reward. Through this update mechanism, the Q-values gradually converge to their optimal values, guiding the agent to make efficient decisions in a dynamic environment.
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 5 Fifth-Generation Performance with Multiservice  
In wireless mobile networks, the service area is divided into cells with many available channels. There are two types of call requests that share these channels: new calls and handover calls. New calls are initiated by mobile users in the current cell, whereas handover calls are initiated in other cells and handed over to the current cell. When a call arrives at a cell in which a channel is not available, the call is blocked or queued, depending on the call admission control algorithm. The probability of a new call being blocked is called the new call-blocking probability, and the probability of a handover call being blocked is called the handover call probability. The probability that a call is either blocked or terminated during the call life is called the dropping probability. These probabilities are referred to as QoS metrics. In the following subsections, the performance of a 5G cell is studied in a multiservice case based on the above-derived capacity bounds.
# Leaching Behavior of Cesium, Strontium, Cobalt, and Europium from Immobilized Cement Matrix
## 3 Results
### Leading of the Solidified Cement Specim
#### 3.1.2. Leaching Rate (LR)  
The LR is defined as the amount of a constituent of a solidified waste form that is leached during a given time interval [45]. The LRs of the nuclides from the cement specimen were calculated by the following equation:  
\[\text{LR}=\left(\frac{\sum a_{n}}{A_{0}}\right)\cdot\left(\frac{V}{S}\right) \cdot\left(\frac{1}{t}\right) \tag{3}\]  
where \(a_{n}\) is the quantity of a nuclide released from the specimen during leaching interval \(n\), \(A_{0}\) is the total quantity of a given nuclide in the specimen, \(V\) is the volume of the specimen (cm\({}^{3}\)), \(S\) is the geometric surface area of the specimen (cm\({}^{2}\)), and \(t\) is the leaching time from the beginning of the leaching test (day) [45].  
The LRs of the nuclides (Cs, Sr, Co, and Eu) as a function of elapsed time are shown in Figure 5. The LRs of the nuclides occurred in the order of Cs, Sr, Co, and Eu during the test. In all cases, the LR significantly decreased during the early stages of leaching (<5 days) but then gradually decreased over time. Among the four nuclides, Cs had the highest LR because of its low field strength, making it substantially soluble under highly alkaline conditions [18; 49; 50; 51]. [PERSON] [52] reported that the easy release of Cs from its cementitious waste form can be attributed to its high solubility in high pH (12.5-13.6) hydrated cement and the low sorption potential of Cs in cement. Thus, numerous studies to reduce the leaching of Cs from the cement matrix have been conducted. Using zeolite as a blending material in cement was found to cause a reduction in Cs leaching [13; 53; 54; 55]. Slag is also used for immobilizing Cs within matrices [6; 56]. Meanwhile, studies considering the binding capacity and bond strength of Cs were also conducted. [PERSON] [57] and [PERSON] [58] reported that the binding capacity and bonding strength of Cs ions in cementitious pastes are enhanced as the Ca/Si ratio in the paste decreases.  
Sr has higher leaching rates than Co and Eu. In the case of Co and Eu, the leaching rate is very low, suggesting that leaching of Co and Eu from the cement matrix rarely occurs. In a previous study, Co was potentially found to be immobilized by irreversible reactions with calcium silicates in the waste-form matrix [59].  
Eu has the lowest levels of leachability, which can be attributed to the low solubility of lanthanides in the high pH environment of hydrated cement [18]. Most lanthanides were found to be strongly retained on the calcium-silicate-hydrate (C-S-H) of cement. Also, it was reported that Eu adsorbed on the surface of the cement matrix was not precipitated in the solution despite its low solubility limit [60].  
Figure 5. Leaching rates of nuclides (Cs, Sr, Co, and Eu) from the cement matrix.
## 3 Mathematical Modeling
The proposed model can be described as follows:  
Indices:  
0: the starting point of the route (called the origin or the health center)  
\(i,j\): patients \((1,2,\ldots,n)\)  
\(k\): the caretaker \((1,2,\ldots,m)\)  
Parameters:  
\(d_{ij}\): the travel time from patient \(i\) to patient \(j\)  
\(p_{j}^{k}\): the service time of caretaker \(k\) at patient \(j\)  
\(e_{j}\): the earliest starting time (or availability time) of patient \(j\)  
\(l_{j}\): the latest start time of patient \(j\)  
\(q_{j}\): the demand of patient \(j\)  
\(Q\): the capacity of all vehicles  
\([r_{jk}]\): a compatible matching matrix \((n\times m)\) whose element \(r_{jk}=1\) means patient \(j\) can be treated by caretaker \(k\) otherwise \(r_{jk}=0\)  
\(M\): a large number  
Variables:  
\(x_{0j}^{k}\): equal to 1 if caretaker \(k\) travels from the origin to patient \(j\); otherwise, 0  
\(x_{ij}^{k}\): equal to 1 if caretaker \(k\) travels from node \(i\) to patient \(j\); otherwise, 0  
\(x_{j0}^{k}\): equal to 1 if patient \(j\) is the last patient visited by caretaker \(k\); otherwise, 0  
\(u_{j}\): variables for subtour elimination  
\(t_{j}\): the starting time of patient \(j\)  
\(c_{ij}\): the traveled cost between patient \(i\) and patient \(j\)
## 3 Results and Discussion
### Case Study for Southern Brazil--R1
Note that estimation products can identify the extreme rain that occurred in the region compared to the in situ rain dataset  
Figure 4: (**a**) Accumulated rainfall recorded from 24 June 2014 to 28 June 2014 in the south region of Brazil (R1); (**b**) fraction of rain on days that had MCS record over the rain accumulated in five days; (**c**) number of MCS events recorded in five days; (**d**) fraction of the days that had MCS records; (**e**) analyzed region. Figure 3: Municipalities affected by extreme rains. (**a**) flooding in the municipality of Iran located north of Rio Grande do Sul state; (**b**) flooding in the municipality of Chapelö located west of Santa Catarina state. Photographs: [PERSON]/G1 and [PERSON]/RBS. (Figure 5m). Figure 5c,f,j suggest that the products GSMAP, 3B42g, and PERSIANNg overestimate the area where the event occurred because they have a larger number of pixels with rain values above 140 mm/5 days. 3B42g, GSMAP, TAPEER, PERSIANN, and CHIRPSg products showed better POD and MR scores (Figure 6a,b). The Threat Score (Figure 6d), also known as the Critical Success Index, considers hits, misses, and false alarms, assigning a perfect score when TS = 1. The products analyzed reached a maximum value of 0.36, corresponding to the same products that reached POD = 1. However, these products had a high rate of false alarms (up to 0.5) (Figure 6c). The FBI (Figure 6e) is an index that shows whether the products are overestimating (FBI \(\geq\) 1) or underestimating (FBI \(\leq\) 1). Products 3B42, CMORPH, and COSCH had metrics closer to a perfect score. The product that showed the best Proportion Correct (Figure 6f) was COSCHg, followed by CMORPHg, and 3B42. In summary, COSCHg, CMORPHg, and 3B42 performed better in the case of extreme rainfall in R1 analyzed, presenting a low FAR (0.11; 0.13; 0.2), a high POD (0.8; 0.7; 0.8), a high PC (0.89; 0.86; 0.86), and FBI nearby of 1.0 (0.9; 0.8; 1.0), respectively. These findings are consistent with results found by [PERSON] and [PERSON] [49]. These authors found large differences between observational and estimated data on days with extreme rainfall events. These results underscore the uncertainty faced by analysts when evaluating precipitation extremes in the region on a daily scale. Figure 5: A case of extreme precipitation occurred over the south region of Brazil (R1) from 24 June 2014 to 28 June 2014 estimated by different products (**a-m**).
## 3 Results
### Intelligent Vehicle Platoons Scenario Experiment
#### 3.2.2 Desired Headway of Intelligent Vehicles
On the other hand, a large following distance can cause intelligent vehicle formations to become bottlenecks on the road, disrupting the flow of nearby vehicles. As a result, both scenarios contribute to increased carbon emissions. Conversely, as the number of intelligent vehicles on the road increases, there is a higher likelihood of forming intelligent vehicle formations. Adhering to a shorter following distance enhances traffic efficiency, which in turn reduces carbon emissions along this traffic route.
# Effect of an Organic Rectorite on the Properties of a Waste Engine Oil-Modified Asphalt
## 3 Results and Discussion
### Storage Stability  
Figure 2 presents the test results of the 48-h softening point difference (SPD) for OREC + WEO composite-modified asphalt at different OREC content levels. From the graph, it is evident that the SPD value for WEO-modified asphalt is 2.8 \({}^{\circ}\)C. In comparison, the SPD value of OREC + WEO-modified asphalt decreases, and this reduction is particularly prominent at a 1% OREC dosage, with the SPD value experiencing a significant drop. Beyond the 1% dosage, the SPD decrease tends to stabilize. When the OREC content reaches 2%, the corresponding SPD value for the asphalt sample is 1.8 \({}^{\circ}\)C, which is 1.0 \({}^{\circ}\)C lower than the asphalt without OREC. These results demonstrate that the addition of OREC improves the storage stability of WEO-modified asphalt. Due to certain differences in solubility and density between WEO and asphalt, WEO-modified asphalt tends to exhibit some exudation issues at high temperatures. Previous reports have indicated that under high shear conditions, nanoscale layered silicates can form a well-dispersed structure in the asphalt matrix, hindering the free movement of all components in the asphalt [18; 19]. Therefore, in this study, the OREC used as a nanoscale layered silicate can impede the movement of WEO components in the asphalt matrix, thus enhancing the system's storage stability.  
The \"Technical Specifications for Highway Asphalt Pavement Construction\" (JTG F40-2004 [40]) stipulates that the SPD value for SBS polymer-modified asphalt should not exceed 2.5 \({}^{\circ}\)C to ensure good storage stability of the modified asphalt. From Figure 2, it can be observed that when the OREC content exceeds 0.5%, the corresponding SPD value for OREC + WEO composite-modified asphalt is below 2.5 \({}^{\circ}\)C. Based on these technical requirements, this study selected OREC content ranging from 1% to 2% for further research on the composite-modified asphalt.  
Figure 1: FT-IR spectra of WEO, OREC, base asphalt, WEO-modified asphalt, and 2% OREC+ 6% WEO composite-modified asphalt.
# Ultrashort Vortex Pulses with Controlled Spectral Gouy Rotation
## 2 Theoretical Background and Numerical Simulation  
### Spectral Gouy Rotation of Bessel-Gauss Beams  
Spectral rotation properties of OAM pulses result from the coaction of wavefront rotation and Gouy effect. The wavefront rotation in polychromatic singular beams is determined by the spectral distribution function, either because of the wavelength-dependence of the diffraction angle (for spiral gratings) or because of the spectral dependent mismatch of the phase shift of polychromatic beams (for spiral phase plates). The Gouy phase shift [45; 46; 47] is a characteristic phase delay of a beam at passing a focal region which amounts to \(\pi\) for Gaussian beams. The phenomenon can be explained by a geometrical path length difference [48] or as a consequence of the uncertainty relation in transversal confinement [49]. To obtain a continuing rotation of spectral eyes around the phase singularities, two necessary preconditions have to be fulfilled. At first, the spectral bandwidth should enable for a measurable separation of blue- and red-shifted parts. At near-infrared wavelengths, spectral eyes were shown to be reliably detectable at bandwidths in the range of 10 nm [37]. Secondly, the wavelength-dependent Gouy rotation has to be accumulated with increasing propagation distance. This condition is matched if the focus is replaced by continuously superimposing conical partial beams, as it is the case for nondiffracting Bessel or Bessel-Gauss beams [49]. By solving the transverse Helmholtz equation, it can be shown that the wavelength-dependent extra phase shift \(\Delta\psi_{B}\) of a Bessel beam compared to a reference plane wave accumulates with the distance \(z\)[50]:  
\[\Delta\psi_{B}(\lambda)\ =\ (k-\beta)z \tag{1}\]  
Here, \(k\) and \(\beta\) are the amount of the wave vector and the propagation constant, respectively. In contrast to Hermite-Gaussian and Laguerre-Gaussian beams, the Gouy phase shift is independent on the order of the Bessel beam. The spatial confinement of the Bessel beam can be described by a parameter.  
\[\alpha\ =\ \sqrt{k^{2}-\beta^{2}} \tag{2}\]  
so that, with Equations (1) and (2), the propagation dependent spectral Gouy phase shift can also be written in terms of the confinement factor  
\[\Delta\psi_{B}(\lambda)\ =\ \frac{\alpha^{2}}{k+\sqrt{k^{2}-\alpha^{2}}}z \tag{3}\]  
For ultrashort pulses with extremely broad spectra and high intensity, specific spatio-temporal chirp phenomena should modify the Gouy phase shift because of dispersion and nonlinear optical distortions. Under the conditions our experiments, i.e., working with Bessel-Gauss-type vortex beams of low pulse energies and few-nm spectral widths, such additional effects were mostly negligible.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
###### Abstract  
Multipath transport protocols are aimed at increasing the throughput of data flows as well as maintaining fairness between users, which are both crucial factors to maximize user satisfaction. In this paper, a mixed (non)linear programming (MINLP) solution is developed which provides an optimum solution to allocate link capacities in a network to a number of given traffic demands considering both the maximization of link utilization as well as fairness between transport layer data flows or subflows. The solutions of the MINLP formulation are evaluated w. r. t. their throughput and fairness using well-known metrics from the literature. It is shown that network flow fairness based capacity allocation achieves better fairness results than the bottleneck-based methods in most cases while yielding the same capacity allocation performance.  
multipath transport; fairness; linear programming +
Footnote †: journal:
## 2 Hybrid Real-Time Scheduler in the MIROS
### Performance Evaluation
#### 2.6.1 Code Size of the Different OS Schedulers  
_Code size of the event-driven schedulers:_ The code sizes of the different event-driven schedulers are shown in Figure 5. In the _TinyOS_, only one FIFO scheduling queue is used for the event scheduling, and the scheduler code size is small. In the _Contiki_, two level scheduling mechanisms are adopted: the FIFO scheduling queue for the asynchronous events and the polling mechanism for the high-priority events. As a result, the scheduler code size is larger than that in the TinyOS. In the _SOS_, three priority-based scheduling queues are used. Moreover, the indirect access and the module management mechanisms are implemented [5]. Consequently, the SOS scheduling structure is more complicated and more code memory is consumed. In the _MIROS_, the simple flag-polling mechanism is applied and the code size is small. In _mantisOS_, no event-driven scheduler is implemented.  
_Code size of the multithreaded schedulers:_ The code size of the different multithreaded schedulers is shown in Figure 5. The _mantisOS_ scheduler consumes more code memory than the others. This is because mantisOS is a pure multithreaded OS, and all the tasks in this OS are executed by threads. As a result, the thread number is large. In order to manage these threads efficiently, the multilevel-queue (five ready queues and one sleep queue) scheduling mechanism is implemented, and this increases the  
Figure 4: iLive node. ILive is equipped with the AVR ATmega1281 microcontroller (128 KB FLASH, 8 KB RAM), one temperature sensor, one light sensor, one air sensor, one humidity sensor, three soil moisture decagon sensors [16] and four soil moisture watermark sensors [17].  
complexity of the mantisOS scheduling architecture. In the _MIROS_, only the RT tasks need to be scheduled by the multithreaded scheduler, thus the thread number is small and the multithreaded scheduling structure is simple (Section 2.4). In consequence, the code memory consumption is not high. In the _TOST Thread_[13], the code size of the multithreaded scheduler is large; this is because a flexible boundary between the user code and the kernel code is implemented in TOST Thread. With this approach, the TinyOS core can keep unchanged, thread-safe and non-invasive. However, the code size of the TOST Thread becomes larger.
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
## 4 WSN Deployment
### Proposed WSN Deployment  
In this section, an optimized WSN deployment is proposed considering the morphology and topology of the real underground utility tunnel system. In general, the network topology gains a great importance for an adequate deployment and infrastructure design in this type of complex underground environments, since their particular morphology (for instance, tunnels in the presented case) could limit the use of the star topology (the typical one for LoRaWAN). From the previous results, it can be concluded that LoRaWAN technology presents a much higher range in terms of distance and better robustness with human presence due to its better sensitivity. Besides, it provides a high tolerance for interference, which is highly relevant in a confined scenario with the presence of tubes, grids and metal trays. However, considering the wireless system technologies' capabilities and constraints, and the real utility tunnel system conditions in terms of connections, intersections and infrastructure location possibilities, there are several key factors that lead to the election of ZigBee technology to be deployed within the considered scenario. The first LoRaWAN limitation is the reduced transfer capacity (up to 242 bytes), but the most critical one is the network topology and its deployment in underground conditions. While the mesh topology provided by ZigBee gives the option of deploying one single gateway, the star topology of LoRaWAN leads to the deployment of several gateways due to the morphology of the tunnels. Furthermore, taking into account that the considered tunnels lack of communication systems infrastructure and coverage of external systems (such as the mobile network), the deployment of gateways presents serious difficulties, since they need external communication links in order to send the data of the underground tunnel network to the cloud. Therefore, the possibility of deploying a unique gateway is very advantageous.  
For the proposed WSN deployment, a Digi International's XBee Zigbee Gateway was selected, allowing GPRS communications. The gateway was designed to be installed in the center of the tunnel system for network performance optimization and located under a sewer, with the aim of facilitating the  
Figure 16: Bidimensional plane at a 3-m height representation of the RF power level distribution after applying the 20-dB margin due to the human presence for (a) ZigBee 868 MHz and (b) LoRaWAN.  
wireless communication with the mobile network. An efficient node of deployment was implemented with specific nodes located in key corners and intersections, considering the particular morphology of the tunnels and the maximum distance between the nodes obtained in the previous subsection analysis. Digi's XBee SX 868 RF Modules were employed for the sensor nodes. A schematic view of the WSN deployment is depicted in Figure 17 representing the location of the ZigBee nodes and the gateway over the real utility tunnel system map.