esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.16 Path Planning III  
In [84], the authors developed a decentralized local route planning technique for a multiagent operating in dynamic environments. The method aims to improve the effectiveness of local navigation systems, specifically in terms of success rates, path lengths, and the management of dynamic barriers. This work integrates the local obstacle avoidance features of the DWA with the DRL. This methodology allows robots to acquire behaviors from their experiences, adjust to changing barriers, and engage with other robots in real time. This improves the overall efficacy of multiagent in intricate environments, augmenting their capacity to learn from experiences and adapt to dynamic environments.  
The authors in [84] used the PPO RL algorithm to train a decentralized local planner for each robot in a multirobot system. The DWA was used to create the allowable velocity space, allowing agents to choose optimal velocities within that space. The model addresses multirobot navigation challenges, acquiring knowledge from environmental observations and cost function metrics. The model generated smooth, stable trajectories through iterative experimentation, aiming to reduce collisions and conflicts in dynamic environments. An ROS played a key role in the execution and evaluation of the plan, the control of the interconnection between robotic components, and the evaluation of the proposed technique in both simulated and real-world environments. For more information on the hardware and software used, please see Table 4.  
The above-mentioned technique is successful in managing dynamic impediments and interactions, as demonstrated in the work. This work contributes to the field of multirobot route planning, addressing the growing need for efficient and adaptive multirobot route  
Figure 15: Architecture of off-policy RL framework. For more information, please refer to Table 4.  
planning systems in dynamic and unpredictable environments. Testing this technique in a variety of complex environments and robot platforms, such as those found in the transportation and logistics industries, will lead to new insights and enhance robotics applications.
# A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network
## 3 LSTM-RRM Method
### Process of RRM  
In LSTM-based RRM, the priority for allocating the resource to the desired UE is identified by analyzing the request queue. Therefore, a UE with high priority is considered as high priority through the network. Before performing the RRM, a set of queues is used to determine which UE the BS receives more request queues from. In this way, the first priority is given to the UE which transmits more queues. The matrixes used in the LSTM are previous values of bandwidth, power, and data rate. Moreover, the hyperparameter tuning of LSTM is performed by using GSO. In RRM, two different resources are allocated based on data rate factors, such as bandwidth and power. The LSTM-based RRM delivers high resources to the UE which has higher requirements in the data transmission. This helps to reduce the amount of request queues transmitted through the network, and it reduces the traffic in the 5G environment. Figure 3 shows the block diagram for the LSTM-based RRM.  
This RRM scheme receives the input from two modules (namely context acquisition and profile management) and delivers the output using the LSTM. Here, the RRM is interfaced with the 5G network. The input modules and LSTM used for resource management are described as follows:  
* Context Acquisition:  
Initially, the context acquisition is used to gather the information of the network components and the UE. Each element of the 5G network uses the monitoring procedure to discover the information. Here, the monitoring procedure provides the information for each component, a certain time period, request queues, and QoS levels. This context information is used to address the problems of the UE that exist in the 5G network.
* Profile management:  
The capacities of the segment terminals and elements are provided by the profile management. Moreover, this profile management component also provides the information about the preferences (queues), behavior, constraints, and requirements of the UE. Specifically, this component describes the configurations of the operating parameters, which will be verified for the network elements and terminals. This information is required to perform an appropriate resource management for the UE using LSTM.  
Figure 3: LSTM-based RRM.  
* LSTM-based RRM: The main objective of the LSTM is to exploit all the network resources for obtaining the high amount of bit rates with maximum possible QoS level. Here, the LSTM is used to discover the optimal resource management for serving the UEs with higher QoS level. The detailed description about an effective RRM using LSTM is given in the following section. A clear description about the LSTM-based RRM is given in the Section 3.4.
* Learning: The learning component of the LSTM has the information about the context acquisition and profile management, where the learning rate of the LSTM obtained from GSO is 0.9. This information helps the LSTM-based RRM scheme to identify and solve the issues.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 2 Requirements of Loop-Free Rerouting  
Routers follow destination routing to forward the packet in IP networks. Although an IP packet carries source and destination addresses, routers only examine the destination address to obtain the next-hop node for packet forwarding. After the insertion of fake nodes, the paths generated by a Fibbing network must be loop-free. More specifically, the collection of the links used to forward packets to any destination node forms an acyclic graph in a Fibbing network. COYOTE cleverly takes ECMP rules to enhance routing flexibility in Fibbing networks. As a result, given an acyclic graph to a destination node and the flow splitting ratios, a set of fake nodes can be generated to realize the desired paths.  
Although COYOTE provides an implementation for routing paths assignment, the working paths are not guaranteed to be survivable after a network failure. This section presents the requirements for a Fibbing network running LFA so the traffic carried by the working paths can be rerouted to bypass a failed node or a failed link.  
Because a single node and a single link failure are the most common failure events, we focus on handling these two types of failure. As we have mentioned, in a Fibbing network, the collection of the links used by the working paths toward a destination node must be an acyclic graph. We use the acyclic graph shown in Figure 2 as an example to illustrate the paths used for the destination node D. In this example, if node A is the starting node of a flow, this flow follows A\(\rightarrow\)Y\(\rightarrow\)D to deliver traffic to the destination node D. It is also possible that there are multiple paths between a source and a destination node pair. In this example, X uses four paths to deliver traffic to D.
# Named Data Networking Based Disaster Response Support System over Edge Computing Infrastructure +
## 3 System Design
### NDN Emergency Communication Design  
During the disaster response phase, emergency responders are normally split into several groups. Each group includes several members that have the same role, and do the same mission. Moreover, each responder may have multiple roles. Hence, we design an emergency communication namespace that allows each responder to participate in multiple group message rooms based on their roles. Figure 5 shows how messages are delivered inside the NDN network using our designed namespaces.  
The distributed NDN message delivery mechanism is based on ChronoSync [28]. Each group has two kinds of interest packets: group interest and user message interest. They are named following the role of responders in the same group and their format is given in Table 1. The group interest is used for fetching the sequence number of the message from other users. The user message interest is used for fetching the content of the message. At a balanced state, when there is no new message from any responder inside the group, everyone will send a group interest with the same sync number. When one responder establishes a new message, his device will reply with a data packet containing the new message sequence number to other responder devices. Then, they will send the message interest with that received sequence number to fetch the new message. After that, every responder device in the group will send the new sync interest with the sync number increased by one, and the balance state is achieved again.  
With this namespace design, we can provide flexible and location-independent emergency communication for responders. Figure 6 shows how we applied our namespace design for message delivery in a Seoul disaster scenario. Four responders participate in three messaging channels in this scenario. \"/disaster01/soe  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Interest Kind** & **Format** \\ \hline Group & responderRole/syncNumber \\ User Message & responderRole/userID/messageSequenceNumber \\ \hline \hline \end{tabular}
\end{table}
Table 1: Emergency group communication namespace.  
Figure 5: NDN emergency group message delivery: (**a**) Balance state; (**b**) New message state.  
channel is for all emergency responders (including hospital, fire and police responders) who is doing their mission in Dongjak. \"/disaster01/seoul/hospital\" channel is for all hospital responders in Seoul. \"/disaster01/seoul/general\" channel is the general channel for everyone. Each responder can join multiple channels based on their roles. When responders connect to a border edge node and join a channel, route to responder devices will be automatically advertised to the NDN network by the edge node. Hence, no matter where the responders are, they can send and receive messages from their channels using the group name prefixes.
## 2 Methodologies
### Classification with the Support Vector Machine  
The SVM is a machine learning algorithm based on the structural risk minimization principle [29]. SVM has achieved great performance in many pattern recognition based advanced technologies, such as the human-machine interface based on muscle activity classification [30], the brain-computer interface based on mental state classification [31] and the engine control system based on fault diagnosis [32]. The principle of SVM is introduced in brief.  
Given input feature vectors \(x=\{x_{i},i=1,2,...,N\}\) and the corresponding labels \(y=\{y_{i},i=1,2,...,N\}\), where \(x_{i}\in\mathbb{R}^{d\times 1}\), \(d\) is the dimensionality of the feature space, \(N\) is the dataset size and \(y=1\) or \(y=-1\) refer to the two categories (normal or ictal in this work) to be classified. We define a function \(f(x)\) taking the following form:  
\[f(x)=w^{T}x+b, \tag{10}\]  
where \(w\in\mathbb{R}^{d\times 1}\). SVM aims to find the optimal function \(f(x)\) to achieve \(f(x_{i})\geq 0\) for \(y_{i}=1\) and \(f(x_{i})<0\) for \(y_{i}=-1\) so that data of two classes in the high-dimensional feature space are separated by the optimal hyper-plane \(w^{T}x+b=0\). To obtain the optimal hyper-plane as the classification boundary with the maximal separating margin between the two classes [33], mathematically, the objective function of SVM takes the following form:  
\[\begin{split}&\underset{w}{min}\ \frac{1}{2}w^{T}w=\frac{1}{2}\|w\|^{2}\\ & s.t.\ w^{T}x_{i}+b\geq 1,\ \ if\ \ y_{i}=1\\ & s.t.\ w^{T}x_{i}+b\leq-1,\ \ if\ \ y_{i}=-1.\end{split} \tag{11}\]  
The constraints in Equation (11) can be rewritten in a compact form:  
\[\begin{split}& s.t.\ y_{i}(w^{T}x_{i}+b)\geq 1.\end{split} \tag{12}\]  
Because the training data may not be perfectly separated by the obtained hyper-plane, the slack variables \(\xi_{i}\) were employed to relax the constraints in Equation (12):  
\[\begin{split}& s.t.\ y_{i}(w^{T}x_{i}+b)\geq 1-\xi_{i}.\end{split} \tag{13}\]  
The objective function (11) can be further rewritten to the following form which constitutes the structural risk [29]:  
\[\underset{w,\xi}{min}\ \frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{N}\xi_{i}. \tag{14}\]  
The objective function (14) takes both the model complexity (the first term) and the training error (the second term) into consideration. The optimal solution of objective function (14) can be obtained through Lagrange multipliers [33].
## 3 Example Analysis
### Experimental Analysis and Verification
#### 3.3.2 Noise Reduction Visualization and Comparison Experiment  
After preprocessing the input data used in the experiments in this paper, the EMD method was performed on the typical residential electricity consumption data clustered into two categories to obtain eight IMF components. Each component of the IMF represents each frequency component in the original signal. The components are arranged in order from high frequency to low frequency. Modal decomposition is a preprocessing method for signal feature extraction, and each IMF component is used as the input of the subsequent analysis, which is often used to remove noise. Thetime-frequency decomposition diagrams and corresponding spectrum diagrams are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **MAPE** & **MAE** & **MSE** & **RMSE** & **SMAPE** \\ \hline ALL & 2.16\% & 46.36 & 3177 & 56.36 & 3.29 \\ K-Shape\_2 & 1.49\% & 23.77 & 2994 & 59.67 & 2.84 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cluster ablation experimental results.  
To verify the effectiveness of the modal decomposition, all components and the first five components were used as the input data, and experiments were carried out on the power grid residential electricity data set used in this project. The results are shown in Table 3, where EMD_all means using the whole fraction as the input, and EMD_5 means using the first five components as the input.  
By reducing the IMF component as the input, noise is reduced and the indicators, such as MAPE, are reduced. MAPE decreases from 3.01% to 2.50%, which is a decrease of 0.51%. MAE decreases from 24.32 to 20.46, which is a decrease of 3.86. This shows that the EMD method can remove noise and reduce experimental errors.
## 2 Materials and Methods  
### Isolation of Actinomycetes and Screening for Their Antibacterial Activity  
The soil samples were collected from vegetable fields in Hanoi, Vietnam, in February 2020. Soils were sampled with a sterilized spoon, placed in zip-lock plastic bags, and transported aseptically to the laboratory for additional processing. Isolation of the _actinomycetes_ was performed by the serial dilution method on the International _Streptomyces_ Project (ISP) 2, supplemented with nalidixic acid (25 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) and nystatin (50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) to avoid unwanted microorganisms. All plates were incubated at 30 \(\mathrm{\SIUnitSymbolMicro C}\) for 8-10 days. Then, the _Streptomyces_-like colonies that emerged were sub-cultured on ISP2 medium to purify and kept at 30 \(\mathrm{\SIUnitSymbolMicro C}\) for 4 days. Suspected colonies were characterized morphologically and then preserved in 30% glycerol at \(-80\)\(\mathrm{\SIUnitSymbolMicro C}\).  
All isolates were fermented in a 1000 mL flask containing 300 mL YIM38 medium by pipetting 30 mL of seed culture, and then they were incubated on a rotary shaker incubator (180 rpm) at 30 \(\mathrm{\SIUnitSymbolMicro C}\) for 4 days. Mycelia were removed from the culture broth by centrifugation at 8000 rpm for 10 min, and the cell-free supernatant was collected. For extracting secondary metabolites, 2:1 \(v/v\) ethyl acetate was added to the cell-free supernatant and shaken vigorously for 2 h to obtain the upper organic phase of ethyl acetate. All samples were evaporated to dryness using a rotary vacuum evaporator to completely eliminate the extracting solvent, which was subsequently dissolved in dimethyl sulfoxide (DMSO) in order to use for the agar well-diffusion method [19]. Gram-positive _Staphylococcus aureus_ ATCC 29213, _Salmonella enterica_ serovar Typhimurium ATCC 14028, and _Mycobacterium_ supernatants MC\({}^{2}\) 155, and Gram-negative _Escherichia coli_ ATCC 11105 and _Pseudomonas aeruginosa_ ATCC 9027 were used as test bacteria and spread on Luria-Bertani (LB) agar (HiMedia, Mumbai, India) plates. About 50 \(\mathrm{\SIUnitSymbolMicro L}\) of each extract (100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) was put into 6 mm wells, and the plates were then examined for the presence of a distinct zone of inhibition after 18-24 h at 37 \(\mathrm{\SIUnitSymbolMicro C}\).
# Calculation of Guided Wave Dispersion Characteristics Using a Three-Transducer Measurement System
## 6 Experimental Demonstration
### Experimental Results  
The results presented in Figures 14 and 15 have been extracted from signals at two different spacings: 5 cm and 10 cm. The velocities extracted from the experimental signals correlate quite well with the theoretical velocities. However, there are slight variations around the theoretical velocities. These variations can be caused by a number of factors, such as the aforementioned interpolation and overlapping, but also by the manipulation of the transducers, which in this case were handled manually. Small variations in the correct spacing distance between transducers lead to errors in the calculation of the velocities. The velocity-distance relationship is linear so the relative error in the calculated velocity due to misplacement is the same as the relative distance error. An error of 1 mm in an intended 5 cm spacing distance will cause a 2% error in the velocity or an error of 110 ms\({}^{-1}\) in the S\({}_{0}\) mode (\(\approx\) 5400 m/s), for example. On the other hand, with a 1 mm error transducer placement for a 10 cm spacing, the error will be of 1% of the velocity. Therefore, large errors can occur where small propagation distances are used. A\({}_{0}\) and SH\({}_{0}\) exhibit less absolute error variation than S\({}_{0}\) due to their slower velocities, the generated errors being proportional to the velocity.  
In this paper, two propagation distances have been chosen: 5 and 10 cm. From the S\({}_{0}\) mode in Figure 15, it can be seen that the calculated velocities at 10 cm are more accurate, and the variance is also smaller. This is for two reasons: the first is the reduction in the error caused by an incorrect transducer placement, and the second is the implicit increase of the propagation time, which reduces the error in determining that time. Other factors that can introduce errors in the velocity calculation are the variations of the boundary conditions of the transducer, such as the applied pressure over the transducer at each receiving location, as well as the difference of transfer function of each transducer. This problem was overcome utilizing the same transducer to acquire the signals at all locations. Similarly, a change in temperature between measurements will cause velocity changes leading to erroneous values. This can be avoided by acquiring results in quick succession to minimize the impact of the temperature in the results. Using three transducers, one transmitter and two receivers would avoid the issues relating to moving the transducers.  
Figure 13: Schematics of the shear transducer. (**a**) Undeformed, (**b**) deformed.  
Figure 14: Phase velocity and group velocity created from experimental signals for 5 cm propagation distance (black circles) and theoretical values (grey lines).  
Figure 15: Phase velocity and group velocity created from experimental signals for 10 cm propagation distance (black circles) and theoretical values (grey lines).
## 3 Proposed Architecture
### Instantiating of Network Slices in SDN Transport Networks
Thus, the NCS must have a complete view of the network to map the orders and distribute them across domains. The realization should include the expansion/selection of Forwarding Policies, Routing Policies, VPN policies and Underlay transport preference. To maintain the data coherence between the control layers, the network-slice-id used must be directly mapped to the transport-instance-id at the VPN-Node level. * **Network Slice Controller as an stand-alone controller:*
* When the Network Slice Controller is a stand-alone controller module, the NSC's should perform the same two tasks described before:
* _Map_: Process the customer request. The customer request can be sent using the [draft-liu-teas-transport-network-slice-yang-01]. This draft allows the topology mapping of the Slice request. * _Realize_: Create necessary network requests. The slice's realization will be translated into one LXNM Network request. As the NCS has a topological view of the network, the realization can include the customer's traffic engineering transport preferences and policies. * **Network Slice Controller at the domain controller level:*
* The Network Slice Controller can at the same level as the network domain controllers. The SDTN controller should handle the slices request, and the realization can be done by the NSC directly communicating with the Network Elements. The SDTN controller should create unified network views, including each transport domain and the network slices. * _Map_: The SDTN would Process the customer request. The customer request can be sent using the [draft-liu-teas-transport-network-slice-yang-01]. This draft allows the topology mapping of the Slice request. * _Realize_: The realization can be done by the NCS controller applying the service logic to create policies directly on the Network elements. The SDTN should handle the shared resources management between domains. * **Network Slice Controller as part of the domain controller:*
* When the Network Slice Controller is part of the domain controller, the OSS/BSS systems process the Slices requests and introduce the network abstraction layer. At the network level, the same device data model would be used in the NBI and SBI of the SDN controller. The direct translation would reduce the service logic implemented at the SDN controller level, grouping the mapping and translation into a single task. :
* _Map & Realize_:The mapping and realization can be done by the Domain controller applying the service logic to create policies directly on the Network elements.
## 3 System Model
### V2V Scenario Model for Straight Roads  
As shown in Figure 1, buildings and trees are the main scatterers distributed along the sides of the road. The horizontal distance between the signal-transmitting vehicle (\(T_{X}\)) and the receiving vehicle (\(R_{X}\)) is D, and the straight-line distance is \(D_{0}\). The distance between the scatterer S and the transmitter is hr1, and the distance between the \(R_{X}\) and the \(T_{X}\) is hr2. Both the transmitting and receiving vehicles use a uniform single antenna. The angle of departure (AOD) of the signal is \(\alpha_{T_{1}}\), the angle of arrival (AOA) is \(\alpha_{R_{1}}\), the angle of the direct component at the transmitter is \(\alpha_{T_{0}}\), and the angle of the direct component at the receiver is \(\alpha_{R_{0}}\). The speed of the transmitting vehicle is \(V_{T_{x}}\), and the speed of the receiving vehicle is \(V_{R_{x}}\), with their respective directions of travel being \(\beta_{T}\) and \(\beta_{R}\). The scatterers on both sides of the road affect the strength and propagation path of the signal received by the receiving vehicle. The received signal includes not only the LOS component but also a large number of NLOS components that arrive after being reflected by the scatterers. This paper considers only single scatterer reflections. The parameters of the channel model are summarized in Table 1.  
Figure 1: V2V communication in a typical straight road scenario, including scatterers on both sides of the road, as well as LOS and NLOS components.