esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations and Nomenclature  
\begin{tabular}{l l} Symbol & Explanation & SI Unit \\ ML & Machine learning \\ ANFIS & Adaptive neuro-fuzzy inference system \\ ANN & Artificial neural networks \\ RCSA & Real-coded simulated annealing \\ CA & Cultural algorithm \\ SFLA & Shuffled frog leaping algorithm \\ R & Correlation coefficient \\ RMSE & Root mean squared error \\ MAE & Mean absolute error \\ StD & Standard deviation \\ Q\({}_{\text{i}}\) (i = 0:100) & Quantile value at ith point \\ L & Beam length & M \\ d\({}_{0}\) & Beam end-opening distance & Mm \\ D & Diameter of circular openings & Mm \\ d & Inter-opening distance & Mm \\ H & Height of I-section & Mm \\ \(\text{t}_{\text{web}}\) & Web thickness & Mm \\ \(\text{w}_{\text{flange}}\) & Flange width & Mm \\ \(\text{t}_{\text{flange}}\) & Flange thickness & Mm \\ q & Uniformly distributed load & N/m \\ q\({}_{\text{c}}\) & Critical buckling load & N/m \\ \(m\) & Scaling parameter \\ \(n\) & Scaling parameter \\ \end{tabular}
# Design and Development of a Management System for Energy Microgrids Using Linear Programming
## 4 Discussion  
According to the characteristics and results of the methodology presented, it can be concluded that it adapts to changes in the energy demand of the microgrid and also to changes in the availability of generation resources (air and solar radiation), making intensive use of renewable generation sources while possible and covering the gap from the use of non-renewable sources and energy storage in the battery system. This use conforms to the proposed strategies and, in this way, manages to satisfy the maximum possible demand from an installed fixed generation capacity.  
For its part, the battery system and its management, which is presented as a contribution of this research, is especially relevant in seeking to achieve better coverage of demand. In this case, there are characteristics of the battery that are highly relevant to adequately meet the energy demand of the microgrid. Likewise, the use of a penalty cost for unattended demand points to a better microgrid management process, establishing a turning point for said penalty once it reaches the cost of the most expensive generation source.  
That said, it is worth mentioning that, in many cases, the management model seeks to use the stored energy as soon as possible with respect to its production period; however, this generates periods in which the unserved demand reaches significant levels with respect to the overall demand. This situation can be addressed in future research, developing strategies that allow for the prioritization of demand at certain hours of the day and that also considers multiple types of demand, such that the energy use available in each period of time is prioritized for specific users or customers such as hospital infrastructure, food warehouses, etc.  
On the other hand, the analysis of the sources of uncertainty that can affect the operation of the microgrid is proposed as future contributions, in such a way that the reliability of the management of generation sources is increased, taking into account different possibilities or scenarios.  
Figure 5: Impact of the penalty to unmet demand.
## 2 Testbed Setup and Measurement Methodology
### Experimental Setup for Available Bandwidth Estimation
#### 2.2.3 Test Scenarios  
To analyze the accuracy of AvB estimation results in different network configurations, experiments were conducted on three different scenarios: 300 Mbps, 1 Gbps, and 10 Gbps. The first scenario is the point of interest due to the limitation of a Linux sleep accuracy to 40 \(\upmu\)s, which also corresponds to a minimal achieved inter-packet interval and, therefore, to 300 Mbps data rate. Data rates of 1 and 10 Gbps were chosen as upper limits for the corresponding Ethernet standards. In each of the experimental scenarios, the accuracy and performance of used AvB tool measurement were tested in a consecution with _HighPerTimer_ and standard timing methods. Substitution of different timing methods for each AvB estimation tools was done without source code re-compilation. For this, the _LD_PRELOAD_ environment variable which preloaded specified timing libraries before others were used. The scenarios are the following:  
1. Between VMs and a remote server with an end-to-end path limited to 300 Mbps capacity, using _Yaz_ for AvB estimation. Cross traffic data rate in this scenario was varied from 0 to 250 Mbps with 50-Mbps increments and was generated by _ipperf_[24] tool.
2. Between VMs and a remote server connected over 1 Gbps path. For the experiment, a patched version of _Yaz_ was used. To achieve specified data rate, this version uses a smaller inter-packet spacing than the original one. The reduced inter-packet spacing resulted in the AvB tool using inaccurate sleep time for inter-packet interval setting. As mentioned above, the concept of the algorithm is based on the iterative adjustments of inter-packet time interval until the maximal data rate is achieved, which also corresponds to the available bandwidth of the path. The accuracy of AvB tool was predicted to suffer from the algorithm using cross traffic set as 500 Mbps in this case.
3. Experiments were performed between a VM and host; and between two hosts to compare the virtualization impact on estimation accuracy. Servers were connected over 10 Gbps path with injected 20 ms RTT and 1 Gbps cross traffic. Measurements were performed using _Kite2_ tool.
# Performance Improvement for PMSM Driven by DTC Based on Discrete Duty Ratio Determination Method
## 5 Experimental Analysis
### Dynamic Performance  
To validate the fast dynamic response of the proposed FS-DDTC, the responses of torque and flux in the PMSM driven by the two control are tested when the torque and the flux linkage are set as 0.4 N\(\cdot\)m and 0.3 Wb, respectively. In these tests, a step change to 100 rpm is applied on the speed reference while the PMSM is in static state, as shown in Figure 9.  
To validate the fast dynamic response of the proposed FS-DDTC, the speed responses of the PMSM driven by the two control strategies are also tested when the torque is set as 0.5 N\(\cdot\)m. In these tests, a step change from 200 to 400 rpm is applied on the speed reference, as shown in Figure 10.  
Figure 10: The speed trajectory from 200 rpm to 400 rpm when using: **(a)** FS-DTC; **(b)** FS-DDTC.  
Figure 9: The torque and flux trajectory with the speed changes from 0 rpm to 100 rpm when using: **(a)** FS-DTC; **(b)** FS-DDTC.  
It can be seen that the torque and the flux linkage can adjust to the settled state within the similar time, the settling time of the torque using the two different control strategies are 0.032 and 0.03 s. And the ripple of the speed is 27 rpm when using FS-DTC, while the speed ripple of the PMSM is about 28 rpm with the using of FS-DTC. Moreover, the settling time of the rotor speed using two different control strategies are 0.015 and 0.014 s.  
Therefore, the main advantage of FS-DTC, i.e., the fast dynamic response, can be maintained in the proposed FS-DDTC. The experimental results show that dynamic response owns higher priority than ripples in dynamic-state condition, hence, the accurate duty ratio determination method in FS-DTC can be abandoned.
# Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
## 4 Evaluation
### Comparison with the FTUE Framework  
As mentioned earlier, FTUE [8] is a reputation-based trust framework for federated testbeds that uses numerical QoS and QoE KPIs and also provides a credibility mechanism. The FTUE framework assumed four types of experimenters with respect to the difference between the monitoring data and the user opinion, and the experimenter's credibility is updated accordingly. The reputation score per service per testbed is the aggregation of user opinions, weighted by the credibility and the confidence of the user for his/her evaluation. The FTUE framework does not take into account any SLA information.  
We compare our proposed reputation system with the FTUE framework following the experimental settings of [8]. Eighty experimenters are truthful, and twenty experimenters are malicious in disguise, who reserve several testbeds and give biased evaluations only for one specific testbed. The nodes of one or both wireless testbeds are reserved by the users to conduct ten experiments and submit the respective ratings.  
Figures 5a,b demonstrate the reputation score of the two approaches for the NETMODE and NITOS testbeds, respectively. For both testbeds, the reputation score computed by the FTUE framework is 10% lower than the fuzzy VIKOR approach. Three major remarks can be made regarding this result. First, the numerical evaluation of the FTUE framework is based on a five-star scale for evaluation, which provides coarse rating compared to the fine-grained numerical and fuzzy values of the proposed approach. Secondly, the FTUE credibility value depends heavily on the \(Mu\) parameter, which is testbed specific. For the results of Figure 5, we follow the experimental setup of the set in [8], and we set \(Mu=0.75\). For this value, we produce the best reputation score for both testbeds. Finally, the credibility mechanism of the compared reputation systems have two important differences that play a key role in their performance. First, our proposed mechanism leverages SLA data to quantify  
Figure 4: The effect of the \(\alpha\) parameter on the modified fuzzy VIKOR method.  
the user's requirements and check if they are actually satisfied by comparison with the monitoring data. FTUE did not exploit any SLA data. Secondly, FTUE's credibility mechanism is based on four specific types of experimenter behavior, which are quantified by the difference between user's opinion and monitoring values. On the contrary, the proposed reputation system does not assume any specific categorization of user's behavior like the FTUE framework. In Lines 4-10 of Algorithm 1, four cases, CASE1-CASE4, and the necessary relative errors are defined in order to measure exactly the deviation of the user's opinion from the SLA and monitoring value without assuming any specific behavior and covering any possible scenario.
## 5 Experiment and Comparison
### Comparative Analysis
#### 5.2.2 Efficiency of Throughput  
The parameter throughput defines successful transmission of data appropriately. The increase in throughput illustrates that the efficiency of the system is better. Hereby, the measured throughput is marked across the transaction number, and the results are depicted in Figure 11.  
According to the increase in transaction count, the throughput also increases in the AAC-IoT, where it is higher than the existing LBAC. The throughput increases because of the parallel authentication process that allows users to submit a request and receive a response. In contras,t the LBAC uses the HLF blockchain for access control policy, whereas the authentication process is not efficient; hence it allows illegitimate user requests that tend to minimize the throughput with the increase in transaction counts. In an average estimation, the throughput is about 82 Mbps and 189 Mbps for LBAC and AAC-IoT, respectively. There is a large difference between the proposed and existing works, and this is due to the performance of the authentication and access control mechanism. The proposed AAC-IoT achieves higher throughput compared to the previous LBAC method. On average, 50% of the throughput has been increased in the proposed work. The increase in throughput will improve with the increase in transaction number on a large scale.  
Figure 10: Performance of latency plot against rate of transaction.  
Figure 9: Performance of latency plot against transaction number.
# Integrating ISA and Part-of Domain Knowledge into Process Model Discovery
## 5 Knowledge-Based Abstraction Operations
### Main Issues
In our approach, we want to grant experts the possibility of choosing which one(s) of the conflicting abstractions have to be performed. As an example, in Figure 6C, we show the case where the abstraction is performed on the occurrences of the paths on the left and the right of the graph. Figure 5: (**A**) Example of a graph with intersecting paths, where the red box marks the path \(p=<\)\(A_{1}\to A_{2}\rightarrow\ldots\to A_{k}\)?, which will be substituted with a single node \(A\), as illustrated in (**B**), where the resulting model shown the re-addressing of all the arcs entering/exiting any node in \(P\) as arcs entering/exiting \(A\) and preserving the “relevant” intersecting path... \(I_{2}\to A_{2}\to O_{1}\ldots\). Finally, SIM grants domain experts the possibility to _selectively_ apply the merging operations on the process model. We want to also retain selectivity in the new version; thus, in the interactive algorithms, we provide users with the possibility of _selecting_ which occurrences of the retrieved paths a given abstraction have to be performed. Before moving to our abstraction algorithms, it is worth noticing that, aiming at generality, we have developed our approach considering different possibilities:  
* Interactive vs. automatic application of the abstraction;
* _Activity-based_ abstractions vs. _level-based_ abstractions. Regarding the first point, though we aim at supporting _interactive_ knowledge-based abstraction (Section 5.3), for the sake of generality, we also provide algorithms to perform abstraction in a fully _automatic_ way (Section 5.2). Regarding the second point, in _activity-based_ abstractions, the expert(s) selects an activity \(A\) in the KB, and the process model is automatically/interactively updated by substituting \(A\) to all the occurrences of the patterns constituting a subsumption/decomposition of \(A\) (in the KB). On the other hand, in _level-based_ abstractions, the expert(s) selects a level \(L\) in the KB, and the process model is automatically/interactively updated by considering each activity \(A\) at level \(L\) in the KB and by substituting \(A\) to all the occurrences of the patterns expressing its subsumptions/decompositions in the KB itself. The rationale behind this facility is the one of homogeneously converting a given input process model at a desired hierarchical abstraction level. For the sake of brevity, in the paper, we mainly focus on activity-based abstraction, giving hints about how the abstraction algorithms can be extended/modified to support level-based abstraction, as well.
## 4 Optimal Placement of PZT Actuators
### Discussion  
In this section, the placement of the actuators has been optimized to improve the control efficiency of the active reflector. For the single-objective optimization, the load consists of orthogonal Zernike polynomials and in-orbit thermal loads have been applied to the reflector, respectively. The optimal arrangement is derived to minimize the residual RMS error by the proposed method based on the GA. The control efficiency is improved by increasing the number of actuators, but it will reach to an approximate saturation condition  
Figure 16: Voltages of the actuators of the two optimization approaches.  
Figure 17: Pareto frontiers of different numbers of actuators.  
when reaching a certain number of actuators. For the multi-objective optimization, the RMS error and the energy consumption are considered as the optimizing objectives. The Pareto frontiers and corresponding solutions are derived by the proposed method. The proposed method can contain more and widely distributed non-dominated solutions for engineering purposes compared with the traditional method. The obtained non-dominated solutions can significantly reduce the energy consumed by slightly increasing residual the RMS error.
## 5 Conclusion
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Routing Algorithm** & **Schemes** \\ \hline  & & MDA [35], GHT [1], DLB [23], DIM [13], \\  & GPSR & D-GHT [43], LB-DCS [24], Q-NIGHT [25], \\ Point-to-Point & & SSA [22], Tug-of-War [27], RDCS [36] \\ Routing & Logical Stateless Routing (LSR) & KDDCS [42] \\  & CAR-POOLING & SDS [19] \\  & COMBING & Tug-of-War [27] \\  & Recursive Hierarchical Routing & SR-GHT [1] \\ \hline \multirow{4}{*}{Tree Based Hierarchical Routing} & GPSR & DIFS [30], DIM [13] \\  & PATH BASED TREE STRUCTURE & PathDCS [32] \\ \cline{1-1} \cline{5-10}  & HIFARCHICAL VORONOI GRAPH & \\ \cline{1-1} \cline{5-10}  & BASED ROUTING & HVGR [33] \\ \cline{1-1} \cline{5-10}  & VPCR & GEM [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of DCS Schemes Based on Routing Algorithm. There is also a strong need to have an efficient compatible routing algorithm specifically designed for DCS. Most DCS schemes utilize GPSR routing and research is occurring into developing routing protocols based on tree and hierarchical structures. An optimal approach for large WSN is yet to be found.
## 5 Simulation Results and Discussion
#### Advantages  
1. It is adaptable to the existing location detection systems and it is productive in terms of early detection.
2. Using animals as mobile biological sensors is preferable to using fixed sensors because it allows measurement to be taken at any point in a region.
3. It allows the possibility of obtaining information using fewer of sensors than the fixed systems.
4. It allows the possibility of monitoring areas unreachable by satellites and unmanned aircrafts, using sensor equipped animals moving freely in their habitat.  
Figure 10: Simulation results for HVD.  
5. The infrastructure of the system can provide alternative applications other than data collection process. It also enables the possibility of obtaining information about various types of animal.
6. It is easily adaptable to predesigned animal tracking systems.
7. It provides an important support to the prevention of poaching because the sensors allow the immediate detection of death of the animal.  
Although the advantages, disadvantages and the aforementioned problems may vary according to different conditions, the advantages are still noteworthy.
## 2 Materials and Methods
### MR Data Acquisition  
All the participants were scanned with 3D gradient echo images using an Achieva 3T MR system (Philips, Amsterdam, The Netherlands). The scan parameters were: resolution, \(0.4\times 0.4\times 2\) mm\({}^{3}\); 70 to 80 slices (to cover the whole brain); repetition time, 18.16 to 19.78 ms; echo time, 25.65 to 27.98 ms; matrix size, \(512\times 512\); flip angle, \(10^{\circ}\); and the number of receiver channels in the head coil, 8. The 3D gradient echo images were then used directly for processing.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 6 Evaluation  
**Implementation:** The hardware used to implement the ACE nodes were Nordic nRF52840 modules. Nordic provides its own implementation of the Bluetooth Mesh standard [16], which we used to implement ACE and conduct contrast experiments.  
**Configuration:** We let node 1 in the front of Figure 4 act as the source node, and node 13 in the back of Figure 4 act as the destination node. If not special specified, all nodes in the network implement the generic on/off model, and the source node sends on/off message 1000 times to the destination node at a rate of 2 pkts/s. We use as low as \(-12\) dBm transmission power, simulating a longer communication distance condition and making performance gaps between the two networks more significant in our limited experimental space.  
**Testbed:** We deploy a testbed with 13 nodes of the type nRF52840 in a 18 \(\times\) 6 = 108 m\({}^{2}\) empty office as depicted in Figure 4. The maximum distance between two nodes is close to 5 m, and the minimum distance is about 1.5 m. Although small, this setup represents both multi-hops and multi-neighbors scenarios, which can be generalized to a larger scene. neighbor **Metrics:** We focus on the following performance metrics:  
Figure 4: Testbed of 13 nodes in an empty office (18 m \(\times\) 6 m).  
* _Packet Delivery Ratio (PDR):_ The PDR is the portion of packets sent at the application layer, which make it to their final destination, possibly over multiple hops.
* _End-to-end Latency:_ The end-to-end latency is measured between the initial application's intention to send a packet and its reception at the final destination.
* _Route Setup Time:_ The route setup time refers to the time when routing table entries are last updated by any of the nodes in the network.
* _Route Recovery Time:_ The route recovery time refers to the time when the moved node receives the mesh packet sent to itself again.
## 4 Experiments and Results
### Generated Synthetic Defects  
We assume that the defects generated by experts are comprised of four types: hole, stitching, stain, and misprinting. By considering the characteristics of each type, we generate synthetic defects and apply them to the non-defected image. In the case of hole and stitching defects, we draw achromatic ellipses and lines, and apply them to non-defected images by alpha compositing with low transparency or Poisson image editing. Similarly, we drew colored shapes for stain and misprinting. These are applied to non-defected images by alpha compositing with high transparency. All of the generated defects are randomly located and scaled to random size. Also, we used three data augmentation methods for synthetic defects: flip, transformation, color augmentation. Figure 3 shows examples of the generated synthetic defects.
# Power System State Estimation Approach Considering Transmission Line Temperature
## 3 Augmented Jacobian Etc-Se Approach  
### Augmented Jacobian Etc-Se  
The AJ-ETC-SE approach calculates line temperature and voltage phasors simultaneously. The state vector is consistent with \(\mathbf{x}_{aug}\), and the augmented measurement vector \(\mathbf{z}_{aug}\) is expressed as,  
\[\mathbf{z}_{aug}=\begin{bmatrix}u_{i}&P_{i}&Q_{i}&P_{ij}&Q_{ij}&0\end{bmatrix}^{T} \tag{13}\]  
The HBE is taken as pseudo measurements into the measurement vector like zero-injection measurements. Additionally, the measurement equations of the AJ-ETC-SE approach can be written as,  
\[\mathbf{z}_{aug}=\mathbf{h}(\mathbf{x}_{aug})+\mathbf{v}_{z} \tag{14}\]  
where \(\mathbf{v}_{z}\) is the vector of measurement errors.  
Using Taylor expansion at the initial value, \(\mathbf{h}(\mathbf{x}_{aug})\) can be expressed as,  
\[\mathbf{h}(\mathbf{x}_{aug})=\mathbf{h}(\mathbf{x}_{aug,0})+\mathbf{H}(\mathbf{x}_{aug,0})\Delta\bm {x}_{aug} \tag{15}\]  
where \(\Delta\mathbf{x}_{aug}\) and \(\mathbf{H}(\mathbf{x}_{aug,0})\) are the correction vector and Jacobian matrix, respectively.  
In order to minimize the residuals between \(\mathbf{z}_{aug}\) and \(\mathbf{h}(\mathbf{x}_{aug})\), the objective function is established by WLS and expressed as,  
\[J(\mathbf{x}_{aug})=[\mathbf{z}_{aug}-\mathbf{h}(\mathbf{x}_{aug})]^{T}\mathbf{R}^{-1}[\mathbf{z}_{ aug}-\mathbf{h}(\mathbf{x}_{aug})] \tag{16}\]  
where \(\mathbf{R}\) is the weighting matrix.  
By substituting (15) into (16), the correction vector in the \(k\)th step \(\Delta\mathbf{x}_{aug,k}\) can be written as,  
\[\Delta\mathbf{x}_{aug,k}=[\mathbf{H}^{T}(\mathbf{x}_{aug,k})\mathbf{R}^{-1}\mathbf{H}(\mathbf{x}_{ aug,k})]^{-1}\mathbf{H}^{T}(\mathbf{x}_{aug,k})\mathbf{R}^{-1}\Delta\mathbf{z}_{aug,k} \tag{17}\]  
Thus, the state vector in the \(k\)th step \(\mathbf{x}_{aug,k}\) can be calculated as,  
\[\mathbf{x}_{aug,k+1}=\mathbf{x}_{aug,k}+\Delta\mathbf{x}_{aug,k} \tag{18}\]