esa-sceva/satcom-chunk-collection huggingface dataset:
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 4 Results and Discussion
### Evaluating the Proposed Receiver with LEO Satellite Communication  
In this section, we use data provided by Eutelsat [[https://www.eutelsat.com/en/satellites/leo-fleet.html](https://www.eutelsat.com/en/satellites/leo-fleet.html)]([https://www.eutelsat.com/en/satellites/leo-fleet.html](https://www.eutelsat.com/en/satellites/leo-fleet.html)), accessed on 2 June 2021, a company specializing in the deployment of LEO satellites.  
In order to evaluate the performance of our receiver, we first present, in Figure 7, the variation of the DR and Doppler shift over time obtained from an Eutelsat nano-satellite with a typical altitude of 550 km and given the carrier frequency of the LoRa signals 868 MHz.  
Figure 6: Comparison of robustness of CSS and DCSS technologies to DR over AWGN channel with \(SF=12\), \(B=B_{ref}\) and \(N_{pay}=51\) bytes.  
It can be seen that, in the worst case, the DR can reach 280 Hz/s. We note here that, along the packet duration, the DR can be modeled as a linear shift variable in time. Whereas the Doppler shift can achieve 19 kHz. This significant Doppler shift, related to the satellite motion, combined with local oscillator instability, leads to huge CFO values. Therefore, our proposal, which allows decoding LoRa-like signals whatever the frequency offset, would be a very promising solution for ultra narrow band (UNB) communications with LEO satellite using chirped signals.  
As depicted in Table 2, our receiver has to deal with a significant CFO value (i.e., \(\Delta f_{max}>\frac{B_{ref}}{4}\)) and the fastest Doppler variation in LEO satellite communication.  
We tested our algorithm with the (almost) worst case scenario, in terms of the value of DR in LEO communications with altitude ranging between 300 and 700 km, such as the majority of CubeSats [32]. Moreover, the values of DRs and Doppler shifts depend on the carrier frequency, and since there were not a lot of works that implemented LEO satellites communications using the the 868 MHz carrier frequency, we did not find sufficient results in this context in the literature.
## 4 Performance Evaluation
### Network Lifetime  
In Figure 10 (a), we simulated the network lifetime of each scheme according to the number of sinks and sources with an initial energy of 0.1 J. As shown in this figure, as the number of sinks and sources increases, network lifetime of all schemes decreases. Even though SLS has the highest  
Figure 8: Remaining energy power of the sensor nodes for data communication.  
Figure 9: Average data success ratio.  
network lifetime of all analyzed schemes, SLS has the lowest data success ratio as shown before. TTDD has the worst network lifetime because TTDD reselects a disseminate node for each source node whenever an event is detected. SDLS still has a high network lifetime even when the number of sinks and source nodes is 50.  
Figure 10 (b) depicts the network lifetime according to the simulation time from 0 to 200 seconds. Sink speed is 10 m/s and the number of sinks and source nodes is 15, with an initial energy of 1J. Since SDLS distributes data over the entire network by considering residual energy, the network lifetime could be significantly prolonged compared to the other protocols. The network lifetime for both SDLS (LSR) and SDLS (LSR + EDA) is longer than other protocols.
## 4 Head Node Checkpointing
### Energy Consumption Model
#### 4.3.2 The Minimum Number of Backup Nodes  
There is a trade-off between reliability and energy consumption. As the number of backup nodes increases, reliability increases, but the energy consumption of checkpointing also increases. As a result, the lifetime of network decreases. Therefore, we find the minimum number of backup nodes that satisfies the expected level of reliability. We apply the Markov model to determine the minimum number of backup nodes when the expected reliability is specified by a user or an application designer.  
In [21], there is a special case of the birth-death process that involves the use of continuous-time Markov model. Figure 7 shows the state diagram of our model, where the state refers the number of failure nodes.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Notation** & \multicolumn{1}{c}{**Description**} \\ \hline \(N\) & The number of sensor nodes \\ \(n\) & The number of backup nodes \\ \(\lambda\) & Failure rate of each node \\ \(\mu\) & Repair rate of backup nodes \\ \(\rho\) & \(\lambda/\mu\) \\ \(E_{tx}\) & The amount of energy to data transmission between two nodes \\ \(S\) & The transmission power (dBm) \\ \(d\) & Distance between two nodes \\ \(H\) & The depth of water \\ \(I\) & The intensity of acoustic signal \\ \(T_{tx}\) & Transmission time \\ \(K\) & Packet size \\ \(P_{k}\) & The failure probability of head node at the k\({}_{\text{th}}\) data transmission \\ \(E_{pre}\) & The energy consumption of previous scheme (without checkpointing) \\ \(E_{ckpt}\) & Energy consumption of the proposed scheme (with checkpointing) \\ \(I_{ckpt}\) & Checkpointing interval \\ \(D_{pre}\) & Recovery latency of previous scheme (without checkpointing) \\ \(D_{ckpt}\) & Recovery latency of the proposed scheme (with checkpointing) \\ \(\delta\) & The guard time \\ \(d_{max}\) & Distance from the farthest sensor node to the head node \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of notations used in this paper.  
Figure 7: The state diagram.  
If the failure rate of each node (including the head node) is \(\lambda\) and the repair rate is \(\mu\), the expressions for steady-state probabilities are obtained via Equations (3) and (4):  
\[\pi_{k}=\ \pi_{0}\ \prod_{i=0}^{k-1}\frac{\lambda(n-i)}{\mu},0\ \leq k\ \leq n \tag{3}\]  
\[\pi_{0}=\ \frac{1}{\sum_{k=0}^{n}\rho^{k}\ \frac{n!}{(n-k)!}} \tag{4}\]  
When each node has its own repair facility, such as a watchdog timer with repair rate \(\mu\), the availability of an individual component is obtained via Equation (5) and the steady-state availability is computed via Equation (6):  
\[A_{\mathrm{indiv}}=\ \frac{1}{1+\frac{\lambda}{\mu}}=\ \frac{1}{1+\rho} \tag{5}\]  
\[A_{\mathrm{steady}}=\ 1-\pi_{n}=\ 1-\frac{\rho^{n}n!}{\sum_{k=0}^{n}\rho^{k} \frac{n!}{(n-k)!}} \tag{6}\]  
When \(A_{\mathrm{steady}}\) is equal to the expected reliability, \(\mu\) is equal to the frequency of the watchdog timer. If the failure rate of each node, (\(\lambda\)), is given, we can define the minimum number of backup nodes (\(n\)) through Equation (6).
## 2 Materials and Methods
### Data
#### 2.2.2 Rain Gauge Data  
The daily scale rain gauge data from the China Meteorological Administration (CMA) were collected from the National Meteorological Science Data Center of China ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)) (accessed on 26 August 2022) to calibrate and validate the downscaled precipitation in this paper. The Chinese standard precipitation gauge (CSPG) was used to observe precipitation at 08:00 and 20:00 Beijing time every day. The precipitation data observed by meteorological stations will be released regularly with a lag of approximately three months for data compilation and quality control [91]. Due to the high altitude, complex terrain and inconvenient transportation in the Three-River Headwaters region, the available weather stations are sparse in and around the area. Although observed precipitation data at CMA weather stations are reliable, there are only 29 rain gauges in the area of 360,000 km\({}^{2}\). Therefore, creating a gridded precipitation dataset for this region by simply interpolating the station observations to the grid points is not accurate [91]. As a result, it is necessary to combine station data with remote sensing datasets to obtain higher-quality precipitation data. In this study, the precipitation data collected at 20 stations and 9 stations were utilized to calibrate and validate the performance of downscaled precipitation, correspondingly. The spatial distribution of stations used in this study area is shown in Figure 1, and the basic information of selected stations is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station ID** & **Longitude (\({}^{\circ}\)E)** & **Latitude (\({}^{\circ}\)N)** & **Elevation (m)** &
\begin{tabular}{c} **Average Annual** \\ **Precipitation (mm/Year)** \\ \end{tabular} \\ \hline
52754 & 100.13 & 37.33 & 3301.5 & 427.8 \\
52833 & 98.48 & 36.92 & 2950.0 & 199.6 \\
52866 & 101.77 & 36.62 & 2261.2 & 420.3 \\
52836 & 98.10 & 36.30 & 3191
## 3 Results and Discussion
### Homopolymer PHB Production from Acetic Acid
#### 3.1.2 Evaluation of Two-Phase and Three-Phase Fed-Batch Culture
To the best of our knowledge, so far, only one report has investigated PHB production from pure culture using acetic acid as the sole feedstock [27]. The aim of this study was to analyse the metabolic flux for PHB production in _C. necator_ using various carbon sources, rather than to optimise the fermentation process. A biomass and PHB concentration of 3 g/L and 1 g/L were obtained in this study. Most reports, however, have focused on the use of acetic acid as the carbon source for PHA production by mixed cultures, whereby the effects of several parameters, such as acetate, ammonia concentration, phosphorus concentration, pH, and feed regime, on the storage capacity of the mixedculture were investigated [28; 29; 30; 31; 32; 33; 34]. Data on PHA content is available; however, biomass and PHA concentration are mostly lacking, making the comparison of results obtained in the present study with these reports unfeasible. In addition, not all reports analyse the monomeric composition of the produced polymer. Furthermore, several studies are available in which a mix of carboxylates including acetic acid is used as carbon source by mostly pure cultures [4] (see Section 3.2) and in some cases by mixed cultures [34; 35]. Such fermentations produce PHA polymers with a considerably high diversity of different HA monomers, containing monomers other than 3 HB [35].
# Towards Harmonious Coexistence in the Unlicensed Spectrum: Rational Cooperation of Operators
## 4 Discussion, Future Topics, and Conclusions
### Future Topics  
LBT-Advanced: According to average user throughput results in [14], when a single LTE operator uses the coexistence mechanism in the unlicensed spectrum, different LBT schemes affect the collision probability with WiFi networks. Consequently, resource allocation between WiFi and LTE networks in the co-channel highly depends on CCA time, CCA period, and channel occupancy time based on a specific LBT feature. To better coexist with WiFi networks, an LTE operator may propose an advanced and unified version of LBT to make WiFi networks consider the LTE system as WiFi itself. Hence, multiple LTE operators with LBT-Advanced (LBT-A) enable all users from multi-RAT systems to benefit from the reduced collision probability and improved spectral efficiency.  
Uplink LBT: LTE in the unlicensed spectrum currently focuses on downlink transmission because data traffic of downlink is generally much heavier than that of uplink. When the unlicensed spectrum is used for uplink transmission, user equipment (UE) will carry out an LBT procedure before transmitting over target uplink channels. In this situation, multi-path fading, shadowing, and the hidden node problem may cause serious degradation of miss detection and false alarm performance. In addition to reliable LBT, energy-efficient LBT methods are necessary especially for power-constrained UEs. To this end, the cooperative LTE mechanism is significantly beneficial to the mitigation of these issues. Since many cooperative sensing schemes have shown reliable and energy-efficient performance in cognitive radio, LTE compliant cooperation and the data fusion process are interesting research topics for uplink LBT (UL LBT) of enhanced LAA (Release 14, 15, and further versions) to protect incumbent systems.  
Modeling complicated behavior by game theory: The operator' dilemma is a typical non-cooperative game where operators only care about their own benefit and choose the best strategy to maximize their payoff function. One can extend the operator's dilemma to describe such a scenario where strategies such as LTE-itself and LTE with LBT are non-deterministic, e.g., a solution concept is the mixed strategy Nash equilibrium. In addition to the non-cooperative game, one can design a cooperative LBT game to analyze the strategic interaction between base stations for downlink LBT (DL LBT) as well as users for UL LBT. Hence, it is necessary for operators in the game to have an agreement on how to utilize and distribute unlicensed spectrum resources based on their own contributions. This bargaining game is useful to verify the enforceable requirements of CCAtime, CCA threshold, CCA consumption power, and expected occupancy time to maximize their utilities respectively.
# A Reinforcement Learning Routing Protocol for UAV Aided Public Safety Networks
## 1 Introduction
The graph traversal method is used to reduce the energy cost of the UAV to one third of the total energy. In [38], authors reviewed and discussed different UAV-Aided Wireless Sensor Networks (UAWSNs). The advantage of these networks is increased coverage and maximum energy consumption at the cost of variable paths and mobility issues resulting in coverage problems in these networks. In [39; 40; 41; 42; 43; 44; 45; 46], different UAV WSN-structured routing (flat, cluster-based, tree-based, and location-based) protocols are proposed. Authors in [47] used UAV communication to provide rescue operations in disaster-affected areas. UAVs are spread over the entire area to provide network coverage. Gateway UAVs are further used to deliver the information to the main network. In this work, the aim is to maximize the data rate while considering battery consumption. To address the problem of gateway UAV selection, authors in [48] proposed a gateway UAV selection algorithm named Battery-Aware Multi Arm Bandit (BA-MAB). They have also explored the use of machine learning. Two kinds of UAVs are present in this work: Access UAVs and gateway UAVs. The objective is to maximize the data rate while consuming minimum energy. In [49], authors used drones for surveillance and data collection in buildings. Sensors are used by the drones to navigate the buildings to identify and pinpoint the problems. Deep RL is applied along with curriculum learning and neural networks. Existing work on UAV and clustering mainly focuses on optimizing data collection in WSNs. However, as demonstrated from the above literature, PSN is another important use of UAVs and clustering simultaneously. The energy dynamics of on-scene devices in a disaster scenario is highly dynamic compared to other uses, which amplifies the complexity of ensuring end-to-end connectivity. Therefore, in this paper:  
* We first analyze the impact of different clustering schemes and a UAV presence on the performance of multihop routing in a disaster scenario. We then present a RL approach to ensure end-to-end connectivity and improve Energy Efficiency (EE) of WSNs. * We consider the mobility of UAVs around the disaster area. Multiple UAV trajectories are devised in order to improve the coverage of clusters in the disaster area while ensuring EE. This paper is organized as follows: In Section 2, the detailed system model is presented. Section 2.1 presents network throughput and delay, Section 2.2 presents the energy model, and the problem formulation is presented in Section 2.3. In Section 3, routing methods are discussed in detail with clustering, route discovery, routing, and control overhead in Sections 3.1-3.4 respectively. Section 4 provides a comparison of clustering schemes with respect to energy and throughput. Sections 4.1 and 4.2 discuss reinforcement-based routing and combined RL and UAVs trajectory optimization. In the end, Section 5 gives the conclusion and future directions.
## 5 Topology and Overload Detection  
INFI-90 DCS network architecture incorporates both ring and bus topologies. The ring topology is used to connect the PCU, EWS, and OIS, primarily for generator control operations. In contrast, the bus topology interconnects the control-way, I/O modules, and field devices via the field bus. In this study, we focus on evaluating the performance of the bus topology using two key communication protocols: Carrier Sense Multiple Access with Collision Detection (CSMA/CD) and the Token Bus protocol. These protocols are considered favorable due to their enhanced compatibility with Internet-based communication systems compared to traditional field bus protocols. Based on the observed performance characteristics, we propose a novel overload detection algorithm that leverages the advantages of the optimal communication protocol identified in this evaluation.
## 3 Results and Discussion
### Sorption Studies of EF and Reactivity Test on Alternator, Ignition Coil, and Starter Motor  
The EF concentration on the alternator, ignition coil, and starter motor in the fumigation chamber remained constant during the 4 h fumigation period. For the alternator, ignition coil, and starter motor, EF applied a dose target at the LCt\({}_{99}\) value, and the sorption of EF was approximately 5-10% at the end of fumigation (Figure 3). This sorption rate was extremely low compared with 50% in previous studies for phytosanitary purposes with plants [31; 32]. This phenomenon might be related to the hydrolysis properties of EF, as EF is readily hydrolyzed to formic acid and ethanol in water or humid conditions. Therefore, this low sorption rate was suitable because machinery components do not have much water.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Tem. (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**1**LCt\({}_{50}\)} & \multicolumn{2}{c}{**LCt\({}_{99}\)**} \\  & **(95\% CL \({}^{2}\), g h/m\({}^{3}\))** & **(95\% CL, g h/m\({}^{3}\))** & **Slope \(\pm\) SE** & _df_ & **X\({}^{2}\)** \\ \hline
23 & 15.88 & 31.51 & & \\  & (15.09–16.69) & (28.20–36.77) & 7.82 \(\pm\) 0.7 & 16 & 8.93 \\ \hline \end{tabular} \({}^{1}\)LCt: Lethal concentration \(\times\) time; \({}^{2}\) CL: confidence level.  
\end{table}
Table 1: Efficacy of ethyl formate fumigation for 4 h against _Halymorphus_ adults.  
Figure 2: Photographs of commercial trials for disinfesting the hitchhiking insect pest _Halymorphus_ using ethyl formate fumigation on imported agricultural machinery. (**a**) A polyvinyl chloride (PVC)arpoulin fumigation chamber. (**b**) Secondhand imported products infested with _H. halys_.
## 2 Materials and Methods
### Problem Statement and Method
#### 2.3.2 Proposed Method
We assume the image resolution of the original camera system is \(m\times n\) which is known and then the ideal principal point \(x_{0\_ideal}\) can be given by  
\[x_{0\_ideal}=\left(\frac{m}{2},\frac{n}{2}\right) \tag{14}\]  
After giving the ideal principal point, the unique solution of principal point \(x_{0}\) is obtained by  
\[x_{0}=\text{argmin}\big{(}\big{|}x_{0j}-x_{0\_ideal}\big{|}\big{)} \tag{15}\]  
Note that \(x_{0}\) has up to four points and the point closest to the ideal principal point is the unique principal point. When \(\big{|}x_{0j}-x_{0\_ideal}\big{|}\) is the minimum, we let the unique solution be \(x_{0}=x_{0j}\) and focal length \(f=f_{j}\). Now the intrinsic parameter estimation is finished and finally the transformation between \(\tau\) and \(\eta\) is written as  
\[X_{i}^{\eta}=R_{\tau\_J}\cdot X_{i}^{\tau}+T_{\tau\_J} \tag{16}\]  
(7) Extrinsic parameter estimation. From the definition of the original camera frame \(S_{C}\) and the new world frame \(\tau\) as shown in Figure 4, \(\tau\) can be transformed into \(S_{C}\) using  
\[S_{c}=\tau+T_{\tau\_c} \tag{17}\]  
Here  
\[T_{\tau\_c}=\left[ \begin{array}{c}x_{0\eta}\\ y_{0\eta}\\ -f\end{array} \right] \tag{18}\]  
In the following, we will focus on the transformation between \(S_{C}\) and \(S_{W}\). Our method involves four frames \(S_{w},\eta,\tau,S_{c}\), and the transformations between \((S_{w},\eta)\), \((\eta,\tau)\), \((\tau,S_{c})\) have been obtained, as shown in Figure 5.  
From these transformations, the original world frame \(S_{W}\) can finally be transformed into the original camera frame \(S_{C}\) using  
\[ \begin{array}{c}S_{c}=R_{w\_c}\cdot S_{w}+T_{w\_c}\\ R_{w\_c}=R_{\tau\_J}^{-1}\cdot N\\ T_{w\_c}=T_{\tau\_c}-R_{\tau\_J}^{-1}\cdot\big{(}N\cdot O_{c}-T_{\tau\_J} \big{)}\end{array}  \tag{19}\]  
Extrinsic parameter estimation is finally finished.
## 3 Results and Discussion
### Effect of Contact Time on ASNase Immobilization onto MWCNTs  
To attain the maximum \(IY\) and \(RRA\) values, we assessed the influence of immobilization contact time. The adsorption of 0.086 mg/mL of ASNase onto 2 mg of MWCNTs/f-MWCNTs was evaluated for three different contact times (30, 60 and 120 min) and at pH 8 (best immobilization pH for most materials). The results of the effect of the immobilization time on the \(RRA\) are displayed in Figure 3, where it is possible to observe that when using pristine MWCNTs as immobilization support the \(RRA\) values increase until an immobilization time of 60 min for all the materials studied, except for MWCNT-10, where the best \(RRA\) was found for a contact time of 30 min. Nevertheless, when the adsorption is performed onto f-MWCNTs, 30 min seems to be enough to attain the highest \(RRA\) values of immobilized ASNase, showing that it is not necessary to leave the enzyme in contact with the functionalized support for more time, reducing the overall processing time and costs consequently. It is worth noting that, for all cases, the \(RRA\) increased after MWCNTs  
Figure 2: Effect of pH and diameter of multi-walled carbon nanotubes (MWCNTs) on the relative recovered activity (\(RRA\)). Immobilization of 0.086 mg/mL of L-asparaginase (ASNase) onto 2 mg of pristine multi-walled carbon nanotubes (MWCNTs) and functionalized MWCNTs (f-MWCNTs) for 60 min of contact time. Error bars correspond to the standard deviation between replicates.  
functionalization, indicating the importance of this step for the efficiency of the biocatalytic process, promoting larger surface areas, as was already verified. It must be noted that the best results have been achieved with the f-MWCNT-1020 reaching almost 90% of _RRA_ at the end of 30 min. These results open prospects for modifying the biocompatibility of MWCNTs by varying their size and surface by chemical modification.  
Once again, very high _IY_ values (above 96%) were attained for all the studied samples with a contact time of only 30 min, reaching values of 100% after 120 min of immobilization time also practically for almost all the MWCNTs (Figure S2 in the Supporting Information), meaning that the enzyme is totally adsorbed.  
Due to the high _RRA_ and _IY_ yields attained, it seems that in general, the enzyme-to-MWCNT mass ratio used is close to the maximum enzyme adsorption capacity of most of the f-MWCNTs used; however, this ratio was optimized as described below.
## 3 OLSR-Based Protocols  
In this section, we present the standard OLSR protocols and the modified OLSR protocols. Further, we explain the fundamentals of the routing process, and the algorithms used to make a routing decision. Finally, we make a comparison between the four routing protocols in terms of their main feature, routing technique, and route selection mechanism.