esa-sceva/satcom-chunk-collection huggingface dataset:
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 4 AntOR Protocol Family
This is the most efficient multi-core system, where the flow of processes can be scheduled to run on another processor, thus gaining speed through parallel or distributed processing. Programming with threads carries less overhead than expanding a new process, because the system does not initialize a new environment and virtual memory space for that process. Parallel programming technologies, such as Message Passing Interface (MPI) and Parallel Virtual Machine (PVM), are used in a distributed computing environment, while the threads are limited to a single computer system. All threads within a process share the same address space. For the implementation of this routing algorithm to be faster, we use the POSIX thread library. This parallel technique involves launching a thread for each neighbor that initiates any of the following processes: route setup, local route repair and link failure notification. As its name suggests, PAntOR-MI [26] is a variant of PAntOR designed for devices that contain more than one interface, i.e., for small and portable devices with more than one antenna or wireless network interface (PocketPC, mobile phones of last generation, and so on). PAntOR-MI parallelizes the sending ants broadcast through the interfaces using threads. Due to the difficulty of finding specialized hardware, PAntOR-MI only has been applied to the route setup process using two interfaces. Table 5 shows the main features of the AntOR protocols following a sequential and parallel approach. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol/** & **Approximation** & \begin{tabular}{c} **Route Obsolete** \\ **Management** \\ \end{tabular} & \begin{tabular}{c} **Control Packet** \\ **Buffer** \\ \end{tabular} & **Disjoint** & **Other Characteristics** \\ \hline AntOR & - & NO & NO & Node/link & \begin{tabular}{c} Pheromone separation. \\ Distance metric. \\ \end{tabular} \\ \hline AntOR-DLR & - & NO & NO & link & - \\ \hline AntOR-DNR & - & NO & NO & Node & - \\ \hline AntOR-RDLR & Sequential & NO & NO & Link & \begin{tabular}{c} Flexibility sent of \\ agents through \\ disjoint routes. \\ \end{tabular} \\ \hline AntOR-UDLR & Sequential & NO & NO & Link & \begin{tabular}{c} Link failure unicast \\ Management. \\ \end{tabular} \\ \hline AntOR-v2 & Sequential & YES & YES & Link & \begin{tabular}{c} No virtual pheromone. \\ New route exploration. \\ \end{tabular} \\ \hline HACOR & Sequential & YES & YES & Link & \
\begin{tabular}{c} No virtual pheromone. \\ Route exploration \\ based on S-ACO. \\ New neutralization process. \\ \end{tabular} \\ \hline P-AntOR & Parallel & NO & NO & Node & Based on thread. \\ \hline PAntOR-MI & Parallel & NO & NO & Node &
\begin{tabular}{c} Based on thread. \\ Multi-interface. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Characteristics of protocols based on AntOR.
# 5G-MEC Testbeds for V2X Applications
## 4 Usage Complexity of the Testbeds  
The complexity of using the 5G-MEC V2X testbeds depends on the level of complexity required to set up, configure, and conduct the experiments. Below, we discuss some common factors that can influence the complexity of using the testbed. **Hardware components**: The complexity of using a testbed can be influenced by factors such as the complexity of its hardware components and the number of required features. For the 5G-MEC V2X testbed, the hardware components mainly consist of servers and computing units that are used to deploy the 5G-MEC and V2X application servers. In some cases, open source tools can be used to deploy the 5G-MEC system, and telecommunication companies can provide the necessary infrastructure. **Software components**: The complexity of using software elements in a testbed is influenced by several factors, such as the development of the libraries, APIs, and SDKs used for deployment. These factors can significantly impact the usage of testbeds. For the 5G-MEC deployment, both open source and proprietary/licensed SDKs can be used. Additionally, simulators can be employed to implement V2X applications and scenarios. **Configuration**: The complexity of using testbeds can also be affected by the amount of configuration needed to set up the hardware components, including servers and computing units, according to the requirements of the experiments, and the SDKs to ensure that the testbed works correctly. In the case of open source tools, the deployment's complexity can be influenced by the availability of user documentation and the user's expertise. **User interface**: A user-friendly interface is essential for managing the resources of the 5G-MEC or 5G-MEC V2X open source testbed. Several testbeds are available and accessible for conducting research and running experiments. **User documentation**: The project deliverable that provides the hardware infrastructure used for the required use cases and the GitHub README files used to set up the projects/required testbed repositories are examples of user documentation that can significantly impact the testbed replication or usage complexity. **User experience**: The user experience is rated based on the complexity of all components and the technicalities of the testbed. In general, the 5G-MEC testbed also requires user expertise. The usage complexity of a testbed can directly impact its accessibility and usability for researchers. Reducing the complexity of the testbed can increase the efficiency and effectiveness of further experiments/research. The usage complexities of the available 5G-MEC, 5G-V2X, and 5G-MEC-V2X testbeds are shown in Table 4, Table 5, and Table 6, respectively, along with the aforementioned common factors.
# Fast Deployment of a UWB-Based IPS for Emergency Response Operations
## 3 System Design
### Ranging Obtainment  
As stated in the IEEE 802.15.4 standard, the medium access in a UWB network can be scheduled, so that devices are provided with slots to send and receive messages at known time intervals. Synchronization is achieved by means of periodic beacons, which are sent by the anchor acting as _master_ (and, if needed, retransmitted by the anchors acting as _relays_), according to the value of the synchronization period (\(T_{s}\)). The time between two consecutive beacons makes up a superframe, as can be observed in Figure 3.  
The proposed superframe contains \(n_{\text{TDMA}}\) slots of \(t_{\text{TDMA}}\) duration. Within each TDMA slot, a single network tag is entitled by the _master_ anchor to send a UWB broadcast message during the so-called broadcast slot. Next, the tag will receive a set of consecutive responses during the subsequent \(k\) node slots, with \(k\) being the number of network anchors. Note that each node slot is allocated by the _master_ to a single anchor. In consequence, after each TDMA slot, the tag should have received \(k\) UWB frames, one per available anchor. Lastly, a tag can be allocated with more than one TDMA slot per superframe.  
The previously described UWB frame exchange allows us to extend the single-sided two-way ranging (SS-TWR) localization technique up to \(k\) anchors only using \(k+1\) messages instead of \(2\cdot k\) messages, leading to a notable energy reduction [60]. According to the SS-TWR description [61], the broadcast message sent by the tag in our scheme would correspond to an SS-TWR request, whereas each anchor response would correspond to an SS-TWR reply. Then, as the tag knows the start time of each node slot and its corresponding allocated anchor, it is able to compute \(k\) ToFs, which will in turn be used to estimate \(k\) distances (_rangings_).  
In case there is more than one tag to be tracked at the same time, the available \(n_{\text{TDMA}}\) slots must be distributed among the different tags. In fact, proper sizing of the main parameters of the superframe structure (namely, \(T_{s}\), \(n_{\text{TDMA}}\), \(t_{\text{TDMA}}\), and \(k\)) is crucial, as there exists an important trade-off between scalability and accuracy. For example, for a given position update period (\(T\)), the more tags are tracked, the fewer computed rangings will feed the multilateration algorithm, likely impacting the positioning accuracy.
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 4 Implementation and Experiments
### Discussions
#### 4.5.3 Problems Related to a Large Deployment Area  
When the field of deployment is large, some of the Mesh Beacons will be many hops away from the Mesh Router. When the users are located around those remote Mesh Beacons, they may experience a longer response time owing to greater physical distance and higher packet loss rate. A practicable solution to this problem is to divide the large deployment area into several smaller subareas, and equip each subarea with its own Mesh Router. Mesh Routers, on the other hand,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Locations**} & \multicolumn{2}{c}{**Timestamp of the Locating Record**} \\  & **User 1** & **User 2** \\ \hline \(B\) & 17:14:18 & 17:14:18 \\ \(C\) & 17:14:37 & 17:14:36 \\ \(D\) & 17:14:56 & 17:14:58 \\ \(C\) & 17:15:24 & 17:15:27 \\ \(B\) & 17:15:39 & 17:15:40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Locating results of two simultaneous users recorded in the Cloud Database.  
can connect to the Application Server through Wi-Fi or ethernet interfaces. The idea is depicted in Figure 12. Therefore, the number of hops from any Mesh Beacons to its closest Mesh Router can be well-controlled. Besides, Mesh Beacons can be configured not to rebroadcast the messages from different subareas they belong to, so as to reduce the total number of broadcast messages in the deployment field.  
Last but not least, when the deployment area is large, it takes more Mesh Beacons to cover the area. With a greater number of Mesh Beacons in the network, the overhead incurred by the broadcast behavior of message relays is also increased. Specifically, although broadcasting avoids the need to create and maintain routes, it may be less efficient from the perspective of the total number of consumed messages to fulfill an end-to-end communication between two devices [37]. From our point of view, the overhead of the broadcast can be controlled if the Bluetooth beacon networks are well-designed. Just like what we have mentioned in the previous paragraph, if the rebroadcasts can be limited inside smaller subareas, the scalability of the Bluetooth beacon networks will not be an issue. In addition, the official Bluetooth mesh protocol also utilizes the managed-flood approach to implement the mesh network. This approach is based on message relays to rebroadcast messages, which is exactly what our Mesh Beacons do. As a matter of fact, our Mesh Beacons do more than acting as message relays--they play the role of reference points for micro-location services at the same time.
## 5 Operational Descriptions of the Future Distribution Networks with the Sundom Smart Grid
### Energy Management Use Cases
#### 5.1.2 Energy Management Use Cases of the Microgrid Phase
In the island operation mode, the MEMS behave like a retailer-aggregator, sending the price signal to the CHP unit's EMS and the customers' HEMS to be exploited. The MMS (MEMS) can send direct load control signals to the customers to secure the network's reliable operation. The DR for peak shaving by load shedding PUC was developed over the LV and MV distribution networks, and it includes the following UCs:  
* Economic DR via AS marketplaces: In the customer's premises, the HEMS displays the energy signals to the responsive consumers, prosumers, and partners to decide consumption and electricity generation and supply to the distribution grid (prosumers and partners). The HEMS (at the customer connection point) and the MEMS (at the POI of the microgrid) monitor the energy flows. The HEMS generates forecasts of the load based on the history data and the generation forecasts based on the weather forecasts. The MEMS optimizes the power to be consumed or generated within a microgrid area depending on the energy community's agreed AS programs, market prices, and the network's security. * Generation activation: The electricity generation of the energy community's CHP unit and the PS-BESS can be controlled to produce more electricity to flatten or reduce the distribution grid's peak demand (can be for the TSO's or the DSO's load shedding). * DR from the frequency responsive reserves and intermittent generation: The retailer-aggregator collects the demand and generation flexibility and agrees on using them within the microgrid with the energy community. The loads can be collected for the FCR-N reserves (0.1 MW, P\({}_{100\%}\) in 3 min [60]). In addition, an option is the FFR reserves (1 MW, 49.6 Hz in 1.0 s [66]) for the inverter-connected dynamic loads, that is, various BESS units, but also controllable loads having an enhanced controller as studied in [90], for example. In this study, they are called smart loads.
## 4 Simulation Results and Discussions
### Effects of the Coarse- and Fine-Scale Grid Ratios  
We use the case of Model A with \(\sigma=1.0,\lambda_{x}=\lambda_{z}=4\) m but different coarse-scale grids to illustrate the effects of the coarse- and fine-scale grid ratios on the inverse modeling. The inversely estimated saturated hydraulic conductivity fields under different coarse-scale grid are illustrated in Figure 6, and values of NRMSE are also indicated in the figure. As can be seen from the figure, all cases show good simulation results, and as the coarse-scale grid gets closer to the fine-scale grid, the simulated values match the true values more and more closely. We also show in Figure 7 the temporal and spatial distribution of soil water potential, corresponding to modeled and true saturated hydraulic conductivity fields. Overall, the estimated hydraulic conductivity is able to simulate the spatial and temporal variability of the soil water potential very well in the coarse- and fine-scale setup scenarios, as also shown by the very low NRMSE values marked in the figures.  
Figure 5: The change of \(K_{\mathrm{s}}\) with the iteration of the Markov chains.  
Figure 6: Comparison between the simulated and reference value of \(K_{\mathrm{s}}\) field (effects of the coarse- and fine-scale grid ratios).  
Figure 7: Comparison of reference and simulated pressure head values (effects of the coarse- and fine-scale grid ratios). (a) 8 \(\times\) 8. (b) 16 \(\times\) 16. (c) 32 \(\times\) 32. (d) 64 \(\times\) 64. (e) 128 \(\times\) 128.  
Table 1 further gives the time spent on the inversion and the values of the evaluation metrics for different coarse-scale grids. As can be seen, when a fine-scale grid is determined as 128 \(\times\) 128, the greater the difference between the coarse and fine-scales, the lower the cost of the observation and the less the inverse simulation consumes, but also the lower the accuracy of the reconstruction of the fine-scale saturated hydraulic conductivity. Therefore, we need to make a trade-off between accuracy and cost.  
Through the above analyses, in the research situation of this paper, the coarse grid taking the size of 32 \(\times\) 32 to 64 \(\times\) 64 are more appropriate choices, which can guarantee a certain computational accuracy at the same time, the computational efficiency will be increased to 2 to 6 times of the original, which is why the other calculations of this paper set the size of its coarse grid to 32 \(\times\) 32. Due to limitations on the length of the paper, the results related to model B and C are not represented graphically, but list metrics in Table 2 only.
# The SXFEL Upgrade: From Test Facility to User Facility
## 2 Main Parts of the SXFEL User Facility
### Switchyard  
As is described above, there are two undulator lines with different FEL parameters and different operation modes available in SXFEL-UF. For feeding the two undulator lines with one linac, a beam switchyard is installed between the linac and the undulator section. A fast kicker which is programmable for bunch-by-bunch separation of the 50 Hz electron beam is used for directing the beam to the two undulator lines, respectively. When the kicker is off, the beam goes straightly to the SASE undulator line; while when the kicker is on, the beam is kicked horizontally and heads to the seeding undulator line through a dog-leg. A schematic of the switchyard is shown in Figure 4.  
The switchyard should perform a beam deflection with the least perturbation as possible to the beam quality from the linac, particularly for the seeded FEL. To realize this, the beam switchyard line is designed to be a dual-DBA dog-leg structure, which consists of two symmetrical double-bend-achromat (DBA) and a matching section in between [37]. The kicker acts as the first bending magnet of the entrance DBA and the successive deflection is finished by three dipole magnets. The total deflection angle of the dog-leg is about 6\({}^{\circ}\) and the total length is about 39 m.  
The optics of the beam switchyard are designed to be mirror symmetrical. For minimizing the CSR induced emittance growth, the beam size at the bending magnets is  
Figure 4: Schematic layout of the beam switchyard of SXFEL-UF.  
Figure 3: Longitudinal phase space evolution of the electron beam at the exit of the (**a**) injector, (**b**) BC1, (**c**) main linac, and (**d**) transport line.  
optimized to be very small for reducing the CSR kick. Meanwhile, the matching section between two DBAs is optimized to be a \(\pi\) phase shifter for cancelling the CSR kick in the bending magnets, i.e., the \"optics balance\" method [38]. For suppressing the micro-bunching instability, a micro-bend is inserted in the middle of each DBA. A small reverse deflection angle to the DBA provides a compensation to the R\({}_{56}\) of the DBA and thus a locally isochronous condition is realized. The lattice functions of the beam switchyard are shown in Figure 5 (left).  
A start-to-end tracking from the linac end throughout the beam switchyard is performed by the code Elegant[36]. The results show a well-preserved beam quality along the switchyard section. With the designed optics, the emittance growth is less than 10%, shown in Figure 5 (right). Negligible longitudinal phase space distortion and microbunching growth are observed with the isochronous configuration, as shown in Figure 6.
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
## 6 Conclusions  
A pragmatic method is proposed to derive physically consistent FCover values from the GLASS LAI product and other ancillary information. The quality and accuracy of the generated FCover  
Figure 7: Scatterplots of (**a**) TRAGL and (**b**) GEOV1 FCover products _versus_ mean values of the high resolution FCover maps. The R-squared, RMSE, and bias values are also shown. N is the number of matched data pairs for each case.  
product (TRAGL) were evaluated by comparison with GEOV1 FCover product and directly validated against ground-based FCover estimates.  
The TRAGL FCover product is spatially and temporally complete. Comparison with GEOV1 FCover product showed that both FCover products were generally consistent in their spatial patterns. However, there were relatively large discrepancies in the relative magnitude of the FCover products over equatorial rainforests, broadleaf crops in east central United States and needleleaf forests in Europe and Siberia. TRAGL FCover product had continuous trajectories. The temporal profiles of TRAGL and GEOV1 FCover products showed consistent seasonal variations. Direct validation with ground-based FCover estimates showed that TRAGL FCover product provided the better accuracy against the mean values of high-resolution FCover maps compared with the GEOV1 FCover product. The GEOV1 FCover product shows a slight overestimation of the ground-based FCover estimates, particularly for high FCover values.  
The proposed method was used to calculate FCover values based on the GLASS LAI data derived from MODIS reflectance data. In the near future, we will extend this method to calculate FCover values based on the GLASS LAI data derived from AVHRR reflectance data and perform more extensive validation and analysis of TRAGL FCover values.  
This work was financially supported through Grant No. 2013 AA121201 under the \"State Program for High-Tech Research and Development (863 program)\", the Chinese 973 Program under Grant No. 2013 CB733403 and the National Natural Science Foundation of China under Grant Nos. 41171264 and 41331173.  
[PERSON] and [PERSON] conceived the study; [PERSON], [PERSON] and [PERSON] performed the data analysis; [PERSON] wrote the paper. [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors read and approved the manuscript.  
The authors declare no conflict of interest.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 6 Conclusions  
In this paper we propose a new distributed scheduling method for underwater acoustic networks that supports multiple handshakes and concurrent transmissions in one transmission cycle for one-hop clusters. The proposed method is based on the two-way handshake mechanism; a deterministic scheduling algorithm was developed to calculate the sending time table for each source node. The proposed deterministic scheduling algorithm optimizes the sending sequence and time of the source nodes jointly by dynamic programming to ensure packets sent by a source node do not conflict with other packets at the corresponding destination node, while the whole data transmission time is as short as possible. Using the deterministic scheduling algorithm can also effectively reduce the scheduling overhead. Simulation results showed that the proposed method outperforms UWALOHA, UW-CSMA/CA, slot SFAMA, and ROPA protocols in normalized throughput, packet delivery rate, average end to end delay, and average energy consumption under different network loads and sizes.  
Conceptualization and methodology, J.Z.; software and validation, H.L.; formal analysis and data curation, H.L., J.Z. and Y.X.; writing--original draft preparation, Y.X. and J.Z.; supervision, J.Z.  
This research was funded by the National Natural Science Foundation of China, grant number 61372083, 61871191 and 61871192, and the Science and Technology Planning Project of Guangdong Province, grant number 2016A020222003.  
The authors declare no conflict of interest.