esa-sceva/satcom-chunk-collection huggingface dataset:
# A Transfer Reinforcement Learning Approach for Capacity Sharing in Beyond 5G Networks
## 3 Transfer Reinforcement Learning Approach
### States, Actions, and Reward in Source and Target Tasks  
The particularized definitions of state, action, and reward in the source and target tasks for an agent of the DQN-MARL capacity sharing solution are presented in the following. The presented definitions correspond to the agent associated with tenant \(k\) at time step \(t\), but for simplicity the notation, the dependency on \(k\) and \(t\) is omitted.  
* State: The state in the source task is defined as \(s^{(S)}=\{s^{N},s^{SAL}\}\), where \(s^{N}=\{s_{n}\,|\,n=1\ldots N\}\) includes the state components of the tenant in the \(N\) cells, denoted as vector \(s_{n}\) for the \(n\)-th cell, and \(s^{SAL}\) includes the state components that reflect the SLA of the tenant. In turn, the state in the target task is defined as \(s^{(T)}=\{s^{N},s^{\Delta N},s^{SAL}\}\), where \(s^{\Delta N}=\{s_{n}\,|\,n=N+1\ldots N^{\prime}\}\) includes the state of the newly deployed cells.
* Action: One action in the source task includes \(N\) components \(a_{n}\), \(n=1\ldots N\), each one associated with one cell. \(a_{n}\) tunes the capacity share \(\sigma_{k,n}(t)\) to be applied in the following time step in the \(n\)-th cell and can take three different values \(a_{n}\in\{\Delta,\,0,\,-\Delta\}\), corresponding to increasing the capacity share by a step of \(\Delta\), maintaining it, or decreasing it by a step of \(\Delta\), respectively. As a result, the action space \(\mathcal{A}^{(S)}\) in the source task has \(3^{N}\) possible actions, and the \(i\)-th action \(a^{(S)}(i)\in\mathcal{A}^{(S)}\) is denoted as \(a^{(S)}(i)=\{a_{n}(i)\,|\,n=1\ldots N\}\), for \(i=1,\ldots,\,3^{N}\). Correspondingly, the action space of the target task \(\mathcal{A}^{(T)}\) has \(3^{N}\) possible actions, and the \(d\)-th action is denoted as \(a^{(T)}(d)=\{a_{n}(d)\,|\,n=1\ldots N^{\prime}\}\), for \(d=1,\ldots,\,3^{N^{\prime}}\). An action in the target task can be decomposed into two parts: \(a^{(T)}(d)=\{a^{N}(d),a^{\Delta N}(d)\}\), where \(a^{N}(d)\in\mathcal{A}^{(S)}\) includes the components of the initial \(N\) cells (so it is one of the actions of the source task), and \(a^{\Delta N}(d)=\{a_{n}(d)\,|\,n=N+1\ldots N^{\prime}\}\) includes the components for the newly deployed cells.
* Reward: The reward in both the source and target tasks assesses at the system level how good or bad the action applied to the different cells in the RAN infrastructure was, promoting the learning of optimal actions during the training process. These optimal actions are those that allow satisfying the tenant's SLAs with less assigned capacity and penalizing those that lead to not fulfilling the SLA or to assigning more capacity than needed to a tenant in a cell (i.e., overprovisioning). Therefore, a common definition of the reward, denoted as \(r\), is considered for both the source and target tasks. However, in the source task, the reward will be based on the actions made over \(N\) cells, while in the target task, it will be based on \(N^{\prime}\) cells.
## 5 Conclusions  
Peel cracking and ostiole-end splitting (collectively termed cracking) downgrade fig fruit quality and thus limit its marketability. In this study, the possibility of mitigating cracking at harvest by preharvest foliar salicylic acid (SA) sprays was addressed. SA application (2 mM) increased the percentage of fruit not showing cracking (sound) by 1.4-2.6-fold, and of marketable fruit (sound and slightly cracked) by 2-fold. SA application (2 mM) was associated with increased flesh TSS and TA, as well as with decreased flesh pH and peel stripe red coloration. SA application (2 mM) also decreased TAN and c-3-rut contents. However, the cracking prevention achieved by SA application is much more important for the marketability of fruit. Moreover, the red stripe coloration develops completely at tree ripe and overripe stage, whereas it is limited at the commercial stage [31] when figs have to be harvested [1]. The prevention of cracking facilitates harvesting and postharvest management of figs. Furthermore, it provides the opportunity for a harvest delay, reducing the need for frequent harvesting.  
Conclusively, SA appears to be a low-cost and eco-friendly means of enhancing fig fruit quality, and thus marketability. However, more studies are needed to evaluate the flesh quality and storability of the treated fruit.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112311374/s1](https://www.mdpi.com/article/10.3390/app112311374/s1)]([https://www.mdpi.com/article/10.3390/app112311374/s1](https://www.mdpi.com/article/10.3390/app112311374/s1)), Table S1: Pairwise correlations among TSS, pH, TA, TAN, C-3-rut, _PAL_, _ANS_, _UFGT1_, _UFGT2_, \(L^{*}\), C\({}^{*}\), and \(h^{\circ}\) in all cracking categories (C0, C1 and C2) in 'Vasilika' figs treated with 0, 1, and 2 mM SA at preharvest stage, in year 1.  
Conceptualization, E.T.; methodology, M.K., G.T. and E.T.; software, A.D.K., M.K., D.F. and J.B.; validation, A.D.K., M.K. and J.B.; formal analysis, A.D.K., M.K., M.V.C. and J.B.; investigation, A.D.K., M.K. and G.T.; resources, M.V.C., M.K. and E.T.; data curation, A.D.K., M.K., M.V.C., D.F. and E.T.; writing--original draft preparation, A.D.K. and E.T.; writing--review and editing, D.F., M.K., A.D.K., J.B. and E.T.; data interpretation and valuable insights, J.B. and E.T.; supervision, E.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data available at present.  
The authors thank [PERSON] for statistical advice. Thanks are also due to [PERSON] and [PERSON] for the access to their field properties in the counties of Attiki and Lakonia, respectively, and free supply of fig fruits. All individuals included in this section have consented to the acknowledgement.  
The authors declare no conflict of interest.  
References
## 3 Performance Analysis
### Average Bit Error Rate  
The average BER expression of FSO communication systems with a binary phase shift keying modulation scheme can be calculated based on [19] [Equation (42)] and [11] [Equation (21)]:  
\[P_{e}=\frac{1}{2\sqrt{\pi}}\int_{0}^{\infty}\gamma^{-\frac{1}{2}}\exp(-\gamma )F_{\gamma,2x}(\gamma)d\gamma, \tag{22}\]  
where \(F_{\gamma,2x}(\gamma)\) is the CDF expression of the e2e SNR given in (15). Now, rewriting the exp function in (22) as \(\exp(-\gamma)=G_{0,1}^{1,0}\Bigg{(}\gamma\begin{vmatrix}-\\ 0\end{vmatrix}\Bigg{)}\) and using (15) in (22), we may write  
\[P_{e}=\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\frac{\Phi_{1}\phi_{2R}}{2 \sqrt{\pi}\mu_{0}^{\varrho_{1}}}\mathcal{I}_{5}+\sum_{k=1}^{\beta_{R}}\sum_{ l=1}^{\beta_{D}}\sum_{n=0}^{N}\frac{\Phi_{2}\phi_{2R}}{2\sqrt{\pi}\mu_{0}^{ \varrho_{2}}}\mathcal{I}_{6}+\sum_{k=1}^{\beta_{R}}\sum_{l=1}^{\beta_{D}}\sum_ {n=0}^{N}\frac{\Phi_{3}\phi_{2R}}{2\sqrt{\pi}\mu_{0}^{\varrho_{3}}}\mathcal{I }_{7}+\sum_{k=1}^{\beta_{R}}\Big{(}1-F_{\gamma_{D}^{\prime}}(1)\Big{)}\frac{ \phi_{2R}}{2\sqrt{\pi}}\mathcal{I}_{8}, \tag{23}\]  
where  
\[\mathcal{I}_{5} = \int_{0}^{\infty}\gamma^{-\frac{1}{2}}G_{0,1}^{1,0}\Bigg{(}\gamma \begin{vmatrix}-\\ 0\end{vmatrix}\Bigg{)}G_{4,8}^{7,1}\Bigg{(}\frac{\psi_{1R}^{2}\gamma}{2^{4} \mu_{R}}\begin{vmatrix}1,\kappa_{1R},1+\varrho_{1}\\ \varrho_{1},\kappa_{2R},0\end{vmatrix}\Bigg{)}d\gamma;\] \[\mathcal{I}_{6} = \int_{0}^{\infty}\gamma^{-\frac{1}{2}}G_{0,1}^{1,0}\Bigg{(}\gamma \begin{vmatrix}-\\ 0\end{vmatrix}\Bigg{)}G_{4,8}^{7,1}\Bigg{(}\frac{\psi_{1R}^{2}\gamma}{2^{4} \mu_{R}}\begin{vmatrix}1,\kappa_{1R},1+\varrho_{2}\\ \varrho_{2},\kappa_{2R},0\end{vmatrix}\Bigg{)}d\gamma;\] \[\mathcal{I}_{7} = \int_{0}^{\infty}\gamma^{-\frac{1}{2}}G_{0,1}^{1,0}\Bigg{(}\gamma \begin{vmatrix}-\\ 0\end{vmatrix}\Bigg{)}G_{4,8}^{7,1}\Bigg{(}\frac{\psi_{1R}^{2}\gamma}{2^{4} \mu_{R}}\begin{vmatrix}1,\kappa_{1R},1+\varrho_{3}\\ \varrho_{3},\kappa_{2R},0\end{vmatrix}\Bigg{)}d\gamma;\] \[\mathcal{I}_{8} = \int_{0}^{\infty}\gamma^{-\frac{1}{2}}G_{0,1}^{1,0}\Bigg{(}\gamma \begin{vmatrix}-\\ 0\end{vmatrix}\Bigg{)}G_{3,7}^{6,1}\Bigg{(}\frac{\psi_{1R}^{2}\gamma}{2^{4} \mu_{R}}\begin{vmatrix}1,\kappa_{1R}\\ \kappa_{2R},0\end{vmatrix}\Bigg{)}d\gamma. \tag{24}\]  
The integrals in (24) are solved with the help of [40] [Equation (07.34.21.0011.01)] as \[\mathcal{I}_{5} = G_{5,8}^{7,2}\Bigg{(}\frac{\psi_{1R}^{2}}{2^{4}\mu_{R}}\bigg{|} \frac{1,\frac{1}{2},\kappa_{1R},1+\varrho_{1}}{\varrho_{1},\kappa_{2R},0}\Bigg{)};\] \[\mathcal{I}_{6} = G_{5,8}^{7,2}\Bigg{(}\frac{\psi_{1R}^{2}}{2^{4}\mu_{R}}\bigg{|} \frac{1,\frac{1}{2},\kappa_{1R},1+\varrho_{2}}{\varrho_{2},\kappa_{2R},0}\Bigg{)};\] \[\mathcal{I}_{7} = G_{5,8}^{7,2}\Bigg{(}\frac{\psi_{1R}^{2}}{2^{4}\mu_{R}}\bigg{|} \frac{1,\frac{1}{2},\kappa_{1R},1+\varrho_{3}}{\varrho_{3},\kappa_{2R},0} \Bigg{)};\] \[\mathcal{I}_{8} = G_{4,7}^{6,2}\Bigg{(}\frac{\psi_{1R}^{2}}{2^{4}\mu_{R}}\bigg{|} \begin{matrix}1,\frac{1}{2},\kappa_{1R}\\ \kappa_{2R},0\end{matrix}\Bigg{)}. \tag{25}\]  
The average BER expression is, thus, derived by plugging (25) into (23).
# Routing Schemes in FANETs: A Survey
## 6 Open Issues
### Improving Network Performance by Using Artificial Intelligence  
Artificial intelligence (AI), such as deep learning and reinforcement learning, allows a system to autonomously learn from its operating environment and past experience in order to improve its performance as time goes by. AI plays a vital role in optimizing communication among flying nodes [139; 149]. For instance, reinforcement learning enables UAVs to learn from their respective operating environment and to adjust their position and movement (i.e., speed and direction) accordingly. Learning enables UAVs to self-organize and to select optimal routes among themselves. Furthermore, AI can be embedded in ground stations to enhance FANETs, such as to detect the presence of UAVs, to monitor their movement and behavior as time goes by, to create channel model and utilization map, and to distribute traffic load among routes in the multi-UAV swarm scenario. Further investigation could be pursued to explore and exploit the AI domain to improve routing performance in FANETs.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### RSSI Based Solutions
#### 4.3.6 Energy and Mobility Aware Routing (EMA-RPL)  
We note that RPL does not provide suitable performances for time-constrained real-time healthcare applications where data is captured by attaching sensor nodes to a mobile patient and faces issues of data loss and disconnections of mobile nodes. The authors [66] proposed EMA-RPL consisting of three phases i.e., the'mobility detection phase', the'reaction and prediction phase', and finally the 'notification phase'. In the detection phase, an Associated Mode (AN) that is a static node computes the average RSSI value from the data information obtained from Mobile Node (MN). MN is assumed to be away from the coverage area by AN if the RSSI value is less than the predefined threshold, hence, the mobility is detected. In the handover process, the AN keeps probing MN to send data packets till RSSI reaches a predetermined threshold value. Following, in the second phase, a new AN prediction is done based on the RSSI parameter. This phase is performed by the current AN before the MN disconnects to provide continuous MN connectivity.  
In the final 'notification phase', path routes update is done. Current AN informs the new node that it is a predicted AN, following, the predicted AN sends a unicast DIO message to MN to make an attachment. Parameters such as PP, _rank_, and default route are updated by the MN and two DAO messages are sent to the current and predicted AN for backward path route updates. Therefore, MN establishes a new attachment to the predicted AN before disconnecting from its previous AN avoiding data losses and disconnections in the network.  
EMA-RPL has shown a handover delay of 156.25 ms as compared to 1639.06 ms by mRPL and 45,737 s by basic RPL. A PDR of approximately 100% is achieved by EMA-RPL as compared to 27% by basic RPL and 70% by mRPL.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 3 Problem Statement and Design Considerations
### Federated Learning  
Contrary to the centralized machine learning approach, FL assumes a distributed approach that allows for training AI models across decentralized devices without transferring data to a central server. FL is especially useful in IoT applications that involve private user data since it does not require the data to be sent to a central server, addressing privacy and security concerns. This paper aims to explore the possibility of leveraging federated learning to decrease the amount of data that need to be transmitted while maintaining a comparable performance. The overall architecture of FL is shown in Figure 3.  
Figure 2: Illustration of a typical machine learning model where all data are transmitted from IoT sensors to a central server for model development and testing.  
In federated learning, instead of the end devices transmitting all their data to the central server, the central server sends the global model (initially untrained) to the participating end devices. The end device trains the received model based on its locally collected data. This training is carried out for a certain number of _epochs_ similar to the training in the case of machine learning, but it is performed on the edge and based only on the data collected by the end device itself. Once the local training is complete, the model weights, also known as model parameters, are sent to the central server. These local model weights are numerical vectors that do not provide any information about the type or kind of data that was used for training. At no point do the local data leave the end device. Once the central server receives the model parameters from all participating end devices, it performs a federated averaging on all the received model parameters and outputs an improved global model. This entire cycle of sending the global model, local training, sending local updates, and obtaining an updated global model by federated averaging is termed as a _communication round_. Federated learning is performed for a certain number of communication rounds until the desired performance is achieved. Once the training is complete, the global model is sent to all end devices that are fed with test samples, and its performance is evaluated based on the predictions that it makes. In federated learning, under no circumstances do the local data leave the edge device; hence, the data privacy is preserved at all times.
# Satellite IoT Edge Intelligent Computing: A Research on Architecture
## 4 Results and Discussions
### Satellite IoT Connectivity and Coverage Performance
\begin{table}
\begin{tabular}{c c c} \hline
**Connection Pair** & **Connection Duration/Day** & **Connection Distance/km** \\ \hline Sat1205-to-Sat1105 & 24 h & 1446.81–4327.11  \\ Sat1205-to-Sat1204 & 24 h & 4439.16 \\ Sat1205-to-Sat1206 & 24 h & 4439.16 \\ Sat1205-to-Sat1305 & 24 h & 1446.81–4327.11  \\ \hline \end{tabular}
\end{table} 
Table 1: Connection duration and connection distance between satellite and satellite. Figure 7: The coverage characteristics of the entire satellite IoT sensor. We can see that the network topology of the satellite IoT is a periodic stable motion. By designing a reasonable satellite constellation, the connection and coverage performance of the satellite IoT can be improved to some extent.
## 1 Introduction
In [39], such results are shown for carbon sputtering in Ar/N\({}_{2}\) mixtures in DCMS and HiPIMS. In [43], a similar approach is used to analyze Ti and WTi sputtering in nitrogen in a DC magnetron plasma. A closer look at the correlation between optical and ion mass spectrometric data in a pulsed magnetron sputtering of aluminum oxide is presented in [35]. However, the data there are shown in dependence on the discharge voltage, and no analysis of the hysteresis curves is demonstrated. Apart from the experiments, modeling studies equally contribute to understanding and quantifying the hysteresis effects observed in reactive magnetron sputtering, both DC and pulsed [51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61]. The models can be used to predict the position of the working point and the characteristics of hysteresis loops. Calculations, however, imply possessing a comprehensive set of coefficients (for sputtering, chemisorption, sticking, implantation,etc.) for a given selection of materials, which as a rule is not readily available and might take a separate study to determine. Investigation of the positive and negative ions' fluxes gives an insight in experimentally identifying the transition zones, and this can also help us determine the qualifying parameters which influence the sputtering process and in turn affect the quality of the fabricated coatings. The aim of this work is to characterize the reactive DCMS of aluminum oxide in Ar/O\({}_{2}\) mixtures by bringing together the diagnostic results of current and voltage measurements, deposition rate dynamics, ion mass spectrometry, and optical emission spectroscopy. The novelty of this work therefore lies in the systematic and comprehensive look at different indicators of the metallic-to-oxide sputtering mode transition, which has not been reported before. Revealed trends of constituent plasma species are thereafter compared with the modeling results to yield the parameters of the hysteresis loop applicable for the power regulation mode of magnetron sputtering.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 5 Future Trends
### Decentralized Energy Management  
Another major application trend that is gradually gaining traction is an effective decentralized energy management system. Currently, approaches are being designed towards fault detection/diagnosis [57; 61; 62]. However, it is envisaged that future applications will consider other system resilience and efficiency aspects. Since the DT opens up real-time energy visualization for stakeholders, transparent and secure energy transactions can occur. This may be in the form of blockchain technology in peer-to-peer markets or even towards powering private utilities such as communication towers (5G/6G) using energy generated from smaller installations nearby [138; 139; 140].  
Furthermore, DTs will enable proficient control of smaller energy systems dispersed over long remote distances by creating a unified energy management portal for easy management.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 6 Power Transfer
### Laser Transmission  
Laser power is attenuated when transmitted over large distances while propagating through a medium, and the attenuation coefficient \(\alpha\) is modeled as Equation (9) [40].  
\[\alpha=\frac{\sigma}{\kappa}{\left(\frac{\lambda}{\chi}\right)^{-\rho}} \tag{9}\]  
Figure 16: (**a**) Variation of data rate \(B_{r}\) in bps over distance for 10 hops (each sensor placed at a distance of 100 m). The simulation is performed with RF communication (500 MHz and 2 GHz) with (10% and 25%) bandwidth and optical communication (200 THz) with (1%) bandwidth, (**b**) Variation of BER over \(E_{b}/N_{0}\) for different number of hops.  
where, \(\lambda\) is the wavelength of the laser, \(\sigma\) and \(\chi\) are two constants, \(\kappa\) is the visibility, \(\rho\) is the size distribution of the scattering particles which depend on the visibility. The laser transmission efficiency is then defined as Equation (10).  
\[\eta_{T}=e^{-ad} \tag{10}\]  
where \(d\) is the distance.
## 4 Discussion
### Social Perception of the Role of Technology for Traffic and Mobility Improvement  
As described before, self-reported knowledge of ITS systems was rather scarce among Dominicans, as well as other related concepts, such as'smart mobility' and'smart cities'.  
Figure 3: Most commonly used apps during urban trips (absolute frequency).  
Previous studies agree on the prevalence of this panorama across several Latin American countries, despite the important role of Information and Communication Technologies (ICTs) in the growth of these territories [54].  
For instance, research conducted in Colombia exposes the growing misinformation and number of misbeliefs surrounding the aforementioned concepts [55], even though government lines of action revolve around digitization and technological innovation [56]. Similar conclusions can be drawn from the implementation of some ITS systems in Brazil, where the role of users and institutions in the specific technological adaptation to the problems of each region is emphasized [57; 58].  
In contrast, supportive technological systems for mobility already count on a considerably high (and still increasing) acceptance in countries from other regions, principally Europe and Asia. In these contexts, a strong satisfaction with these systems is usually reported, as a good part of the population reports perceiving everyday mobility-related and quality of life improvements as a result of these advances [59]. Despite the evident contextual and income-related differences, it can be hypothesized that increasing community involvement, participation, and individual responsiveness for the correct functioning of ITS systems might increase their acceptance, valuation, and future use [60].  
In this regard, differences in the degree of knowledge of ITS systems have been stated as closely linked to the digital (income-based) gap between territories [61]. While in developed countries, the majority of people have access to electronic devices, digital media and the internet, low and middle-income countries (LMICs) still tend to report a very low digitization rate. According to the data, only between 6-7 out of 10 Dominicans have an internet connection [62], placing them very far from high-income countries (HICs), where this index usually reaches 90% of people [63]. Notwithstanding, and more as a potentially positive scenario, the Digital Agenda 2030 for the Dominican Republic has been recently approved with the aim of promoting a digital, open, and participatory society in which connectivity, cybersecurity, and digital accessibility of the population could be improved in several fields, including transport and mobility [27; 64].
# The Effect of Exposure to an Electromagnetic Field on Entomopathogenic Fungi
###### Abstract  
Background: The rapid development of mobile communication has caused an increase in electromagnetic field (EMF) emissions in the environment. However, there is a lack of research on the impact of EMFs on microorganisms. Thus, the aim of the study was the determine the effect of exposure to 900 and 1800 MHz electromagnetic fields on the entomopathogenic fungi (EPFs) _Banveria bussiana, Cordycys fumosroscan_, and _Metarhizium anisopliae_. Methods: The entomopathogenic fungi developed under exposure to an EMF for seven days. After the termination of exposure, the linear colony growth, sporulation, germination, and pathogenicity of the EPFs were investigated. Results: The effect of EMFs on _B. bussiana, C. fumosrosca_, and _M. anisopliae_ depended on the EMF frequency and the tested fungus species. Exposure to the 900 MHz frequency stimulated the growth of the mycelium and the pathogenicity of the entomopathogenic fungi, whereas the 1800 MHz electromagnetic field inhibited sporulation and spore germination. Conclusions: The exposure to the 900 MHz frequency stimulated the development of the mycelium of all tested species and the pathogenicity of _C. fumosrosca_. The sporulation and germ tube length of the entomopathogenic fungi were stimulated by the 900 MHz frequency. The 1800 MHz electromagnetic field inhibited the sporulation and spore germination of _B. bussiana_.  
electromagnetic field; 900 MHz; entomopathogenic fungi; stimulation; inhibition Article