esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Consequences and Discussion
### Experimental Results on ZDT Problems  
Table 3 demonstrates a comparison of the experimental consequences of the five methods employed to solve the ZDT problem. The consequences show the satisfactory application prospects of CMGBO in the ZDT of the convex and concave PFs, the best performance of which was on ZDT1 of the convex PF and ZDT2 and ZDT6 of the concave PF. Single-peak double targets were observed for the three problems, indicating the strong approximation ability of CMGBO to the PF of multi-objective problems with simple targets. In addition, CMGBO was the third best on ZDT3, whose PF was not connected convex, which was not much different from MOCLPSO, which displayed the best performance. As can be observed in Table 3, all the algorithms performed poorly on ZDT4, with CMGBO also being the third best. This may be due to the multi-modal Rastrigin functions of the local PFs of ZDT4. In addition, considering the overall and average performances in all aspects, CMGBO demonstrated the best performance among the five algorithms. Moreover, comparable to CMPSO, the consequences of the test verified the significantly superior performance of CMGBO in the ZDT problem set compared with the other three competitors. Figure 3 indicates non-dominated solutions achieved by different algorithms in solving the five ZDT problems. Since some of the algorithms demonstrated similar performances to CMGBO on the same problem, only CMGBO was used as a representative in the drawing. As can be observed in Figure 3, the solution achieved by CMGBO not only properly approximates the whole PF, it indicates good distribution on the whole PF.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 4 Experiment and Simulation
### Multi-Robot Formation Control Simulation Experiment
#### 4.1.1 Square Formation Simulation and Results Analysis  
First, we initialized the density function to a constant value of 1. As shown in Figure 5a, when the initial density function in the area \(Q\) is constant, the Voronoi algorithm is used to divide the area, and the robot tracks the position of the generated point to complete the square formation. In Figure 5b, the solid lines in different colors represent the tracking motion trajectories of the robots.  
In the initial stage of the experiment, the positions of the robots were randomly distributed near the origin, and the cost function value was \(6\times 10^{3}\) at this time. With the iteration of the CVT algorithm, the generation point tends to the centroid of each Voronoi cell, and the cost function value gradually decreases. When the number of iterations \(k=35\), the controller is affected by the integral saturation and overshoots, and the cost function increases to \(2.8\times 10^{3}\). When \(k=55\) or so, by reducing the integral control effect of the controller or separating the integral control process, the cost function value is gradually reduced. When \(k=90\), the generation points of the Voronoi cells reach their respective centroid positions, and the CVT algorithm converges, as shown in Figure 5d. Figure 5c shows the position error between the robot and its corresponding centroid. In the initial stage of control, the error between the robot and the generating point is the largest. When the CVT algorithm converges, the position error between the robot and the generating point eventually tends to zero.  
Figure 4: Initial positions of robots.
## 1 Introduction
### Related Works
In [49], the authors proposed a jamming and relay selection approach to improve wireless networks' physical layer security. rity in the presence of a malicious eavesdropper by combining cooperative beamforming, cooperative jamming, and relay-jammer selection. The suggested strategy aims to reduce the system's operational complexity while enhancing security performance.
## 5 Sequentially Coupled Physico-Chemical and Biological Treatments
The performance of the integrated process was stable and resulted in the COD reduction from 84.7 to 47.0 mg/L and removing most organic micropollutants. The final effluent showed low acute toxicity and genotoxicity. The energy demand, however, increased by 44.1% with the electricity consumption mainly from the ozone generator, leading to increased operational cost and indirect carbon emission. Several recent studies reported a performance of microbial fuel cells (MFCs) and their reactor systems for simultaneous energy production and wastewater treatment using single bacterial cultures and microbial consortia. Using a wild-type bacterium _Pseudomonas putida_ BCRC 1059, refinery wastewater (COD of 2213 mg/L) was treated in a single chamber air-cathode MFC and generated electric current over four-batch cycles for 63 cumulative days [78]. A maximum voltage of 355 mV was obtained with the highest power density of 0.005 mW/cm\({}^{2}\) in the third cycle with a maximum current density of 0.015 mA/cm\({}^{2}\) regarding the external resistor of 1000 \(\Omega\). A maximum coulombic efficiency of \(6\times 10^{-2}\%\) was obtained in the fourth cycle and the COD removal efficiency of 30% was reached as a function of time. Very recently, a bioelectrochemical treatment of synthetic produced water (contained 0.8 g/L of engine oil; TPH of 124-130 mg/L) and simultaneous bioelectrogenesis was studied in single and dual chamber MFCs [79]. Among both configurations, dual chamber MFC showed higher efficiency with respect to bioelectrogenesis, sulfate removal, COD and TPH reductions. The estimated polarization behavior of both MFCs also indicated an effective response of the electroactive anode biofilm. Microbial communities and bioelectrochemical activities were evaluated in two laboratory-scale MFC reactors inoculated by petrochemical industrial or domestic microbial consortia for degrading 2,4-dichlorophenol and producing electricity [80]. Wastewater-inoculated MFCs were capable of simultaneous energy generation and phenolic degradation. Members of bacterial genera _Arcobacter_, _Aeromonas_, _Pseudomonas_, _Acinetobacter_, _Cloicobacterium_ and _Shewanella_ were found to be important bacteria for 2,4-DCP degradation while _Bacillus_ sp. dominated in the industrial consortium was contributing to higher electricity production. _Cloicibacterium_ sp. was identified for the first time to contribute to phenol degradation in MFC. A laboratory performance of integrated constructed wetland and MFC reactors (CW-MFC) to treat oil-contaminated wastewater (COD of 520 \(\pm\) 42 mg/L; oil content of 235 \(\pm\) 12 mg/L) was compared with the MFC and CW alone [81]. The COD removals of three reactors were between 73% and 75% and oil removals were over 95.7%. Compared with MFC, the CW-MFC with a MnO\({}_{2}\) modified cathode produced higher power density and output voltage. Maximum power densities of CW-MFC and MFC were 3868 mW/m\({}^{3}\) (102 mW/m\({}^{2}\)) and 3044 mW/m\({}^{3}\) (80 mW/m\({}^{2}\)), respectively. Wetland plants in CW-MFC play a positive role for the reactor cathode potential.
## 6 Discussion  
The proposed WL-DR-RLS-DCD algorithm has several advantages compared to previous work. First, as compared to the DR-RLS method presented in [23], the proposed solution has two specific features: (i) it can be applied in the framework of SAEC thanks to the WL model, as shown in Section 2, and (ii) it achieves a lower computational complexity due to the DCD iterations, as presented in Sections 3 and 4. Second, as compared with the WL-RLS-DCD from [5], the proposed version leads to better tracking capabilities due to the DR approach, as also supported by the simulation results presented in Section 5.  
Figure 4: Misalignment of the WL-DR-RLS-DCD algorithm for different values of \(N_{it}\) and \(\alpha_{\mathrm{r}}\). The input signal is a speech sequence, and the length of the four unknown impulse responses is \(L=256\) and \(\lambda=1-1/(64L)\). The four echo paths change in the middle of the simulation.  
As mentioned in Sections 4 and 5, the WL-DR-RLS-DCD algorithm also comes with some compromises. When increasing the number of DR iterations (i.e., the value of \(N_{it}\)), the algorithm runs the DCD iterations for a number of \(N_{it}\) times. The arithmetic complexity needed for solving the associated auxiliary systems of equations becomes proportional to the parameter \(N_{it}\) (besides the length of the complex valued adaptive filter and \(N_{u}\)). However, in practice, the values of \(N_{it}\) and \(N_{u}\), are small, and the overall costs associated with the WL-DR-RLS-DCD, in terms of necessary chip areas, remain attractive for practical implementations on hardware devices.  
Another part of the compromise corresponding to the DR approach is the loss of some accuracy at steady state for the WL-DR-RLS-DCD with respect to the WL-RLS-DCD. In order to mitigate this downside, we can employ several adjustments to the WL-DR-RLS-DCD, which add minimal extra arithmetic costs. Based on past experience with implementation of variable parameters for adaptive algorithms, a new version of the WL-DR-RLS-DCD with a variable number of DR iterations for SAEC scenarios can be developed. The new algorithm will adjust the value of \(N_{it}\) with respect to the convergence state of the algorithm by gradually increasing it when approaching steady state and by decreasing it when tracking situations occur.  
Furthermore, we will explore the option of mitigating the DR accuracy reduction at steady state by employing a variable forgetting factor approach, which was studied by our team in [26; 27]. The goal is to implement a system with variable memory features in order to counteract the negative aspects of the compromise between convergence speed and accuracy.  
A third option is to apply decomposition techniques based on the nearest Kronecker product (NKP), in conjunction with the DR version [28; 29; 30]. The DR-NKP approach applied on the WL-RLS-DCD would replace a single long adaptive filter with a combination of two shorter filters. Consequently, the advantage of such decomposition is twofold. Gains can be obtained in terms of both performance and complexity.
## 3 MT-HVdc Grid Topologies
### Wind Farm Ring Topology  
The wind farm ring topology (WFRT) contains offshore WFs accompanied in a ring, possesses the equal number of WFs and dc breakers, and each WF is linked to an associated mainland network as shown in Figure 3[27; 28]. In the event of a dc fault, DCCBs operate on either side of the fault [28], thus a WF side VSC (WF-VSC) and a GS side VSC are disconnected. As a result, the maximum power loss criterion of GBSQSS has not complied. An isolator segregates the faulty region; once the fault current is zero, HVdc breakers reclose its contacts and hence onshore SS and the offshore WF are again functional. WFRT does not permanently cut off the WF under the failure in connecting lines from GS-VSC to WF-VSC, an advantage of WFRT. However, WFRT loses its stability and flexibility when the failure ensues within the GS-VSC, as this leads to a rise of dc-link voltage [30]. Furthermore, in the event of a dc fault within a ring, dc breakers operate on either side of the faulty region [28] and an onshore VSC and an offshore WF are disconnected from the MT-HVdc grid. As a result, the maximum power loss criterion of GBSQSS does not comply.  
Figure 3: WFRT topology.
## 2 Mission Concept
### Mission Concept of Operations
#### 2.3.3 Safe Mode  
Safe mode is used to identify and correspond to any emergency that can occur during the CubeSat's lifetime. This mode is triggered automatically by the on-board computer software as a result of negative feedback from any subsystem. It can also be initiated after receiving a command from the ground station, which enforces a special FDIR plan. When this mode is triggered, it requests to power OFF most of the components except the critical ones, such as the OBC, the EPS, and the UHF transceiver. During this state, the UHF transceiver will only be receiving and accepting commands and transmitting the beacon. If the battery power level drops below the desired limit of 50%, a full Sun-tracking mode is activated in which Light-1's ADCS will entirely point the solar panels toward the Sun to maximize energy production. Moreover, in case of a high angular velocity rate, the de-tumbling process will be executed until it reaches the safety limit. If all the problems are resolved, Light-1 will switch back to normal mode automatically. Otherwise, Light-1 will wait for a command from the ground station to switch to normal mode. Ground station operators may resolve the problem manually through a command from the ground station.
# Influence of the Analytical Segment Length on the Tram Track Quality Assessment
###### Abstract  
In the track quality analysis, numerical values representing the relative condition of track geometry called track quality indices (TQIs) are calculated along a specific track segment. Segments are defined as linear track geometry datasets with the homogeneous characteristics of factors affecting geometry degradation. The 200m-long analytical segment is used most often on inter-city conventional and high-speed rail networks. However, in the case of the small urban rail networks, the homogeneity of track-geometry degradation influential factors is very low. This segment length is usually too long for efficient track maintenance or reconstruction with minimal disruption of the urban traffic. This paper explores the effect of reducing the analytical segment length in the condition assessment of the tram network in the City of Osijek, Croatia. The research had two main objectives: (1) to assess the narrow-gauge tram-track geometry quality through the application of the established synthesized TQIs, and (2) to analyze how a change in the analytical segment length affects this assessment. Two synthesized track quality indices--one based on a weighted value and the other on a standard deviation of measured track geometry parameters--were calculated for the 27.5 km of tracks on consecutive 200-, 100-, 50-, and 25 m long analytical segments. The comparative analysis of the TQIs' calculation results showed that the reduction in the segment length increased the resolution of the track quality analysis in both cases, while the index based on a weighted value of geometry deviations proved less sensitive to this reduction. These results contribute to further segmentation process establishment and TQIs implementation on tram infrastructure.  
2022
## 4 Conclusions  
In this paper, a MWPT system based on the frequency reconfigurable antenna array was proposed. The frequency reconfigurable antenna array works at different frequencies covering a wide range from 3.41 to 3.96 GHz and 5.5 to 6.3 GHz, and the working frequency band could be switched between the S-band and the C-band. The frequency reconfigurable  
Figure 11: The measurement results of the four-coil MWPT system.  
Figure 10: **(a)** The power transfer efficiency (PTE) of the MWPT system at different frequencies (four smaller side patches are all connected to the larger main patch). **(b)** The power transfer efficiency (PTE) of the MWPT system at different frequencies (one smaller side patch is connected to the larger main patch).  
antenna array can work with good performance at different resonant frequencies. The return loss of the frequency reconfigurable antenna array is less than \(-15\) dB at the different frequencies. Radiation patterns of the antenna array at different frequencies are relative stable. Also, the gains of the antenna array are above 6 dBi at different working frequencies. In addition, PTE of the MWPT system based on the frequency reconfigurable antenna remains above 20% at different frequencies. Also, PTE of the MWPT system at the frequency of 3.64 GHz is 25%, 20.5% and 10.3% at the distances of 20 mm, 40 mm and 80 mm, respectively. The MWPT system based on a frequency reconfigurable antenna array can be used to power the receiver at different frequencies, and PTE of the system does not decrease in a clear way as the resonant frequency of the receiver changes, which enables this system to greatly meet the demands of users.  
[PERSON] proposed the main idea. [PERSON] checked and discussed the results and the whole manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the discussion of this study. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China (Grant No. 2017 YFA0204600), and the National Natural Science Foundation of China (Grant No. 51802352).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions  
This paper proposes an FBG demodulation system based on LPG sensors to improve the demodulation precision of the center wavelength of FBG sensors by jointly using GAN and DNN. Among them, GAN achieves data augmentation without affecting the original data. It solves the drawback that traditional machine learning methods rely on large-scale dataset training to perform well, and it dramatically reduces the data collection cost. DNN is responsible for establishing the complex nonlinear relationship between the LPG fiber's transmitted intensity and the FBG sensor's central wavelength. Thanks to the data augmentation of GAN, DNN can demodulate the FBG sensor with excellent wavelength interrogation performance with a small number of samples. Experiments on a small-scale raw dataset demonstrated that the proposed neural network model possesses excellent performance compared to traditional machine learning algorithms, and its precision can reach the Pm-level of \(\pm 3\) pm. Additionally, the effectiveness of GAN is also proved. This low-cost, high-performance demodulation system can provide a novel platform for fiber-optic-based measurement applications.  
Conceptualization, S.C. and B.Y.; methodology, S.C.; formal analysis, S.L.; data curation, S.R.; writing--original draft preparation, S.L.; writing--review and editing, S.C. and B.Y.; visualization, S.R.; supervision, S.C. and B.Y.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
The work was funded by National Natural Science Foundation of China (61762033, 61865005, 62175054); Open Project Program of Wuhan National Laboratory for Optoelectronics (2020 WNLOKF001); Scientific Research Starting Foundation of Hainan University (KYQD(ZK)1882).  
Not applicable.  
The authors declare no conflict of interest.
# Validating the Adoption of Heterogeneous Internet of Things with Blockchain
## 3 Conceptual Model and Research Hypotheses
### Trust Factors
#### 3.2.2 Security Related Factors  
Security factors are concerned with the crucial pillars that affect the adoption of blockchain in the IoT infrastructure. Considering security, privacy, and reliability of technology and transactions has a noteworthy impact on stakeholder trust. Most of the literature suggests that there is some relationship between security and adoption of blockchain technology in the IoT systems [57; 58; 59; 60]. In conclusion, this focuses attention on defining security-related factors, which can be divided into transaction risk and technological risk.  
**Transaction risk.** Blockchain was initially introduced through cryptocurrencies for traditional banking and financial systems. Nowadays, the effect of blockchain technology has expanded to cover other industries, including technology-based applications in supply chain management, marketing, and finance. Stakeholders seek to ensure decentralization, streamlining, and risk-free transactions within IoT infrastructures [60]. It is important to consider the transaction costs, which have an impact on final decisions [60; 61; 62]. [PERSON] and [PERSON] [61] argued that blockchain provides transparent and valid transactions which critically affect the IoT services produced. [PERSON] et al. [62] presented a strong security-based blockchain service along with some encryption approaches for secure communication. This solution limits the risk of cyber attacks targeting the centralized systems. Hence, securing transactions throughout blockchain services supports the the safe transmission of data.  
**Hypothesis 4A (H4A).**_Transaction risk has a positive impact on the intention to use blockchain technology in IoT systems._  
**Technological risk.** IoT systems have proven their effectiveness for many organizations, but its adoption has tremendous and unforeseen risks which require critical transformations. The risks have been highlighted in various techniques and domains; [PERSON] et al. [58] presented an analysis on the synthesis of potential risks generated by IoT adoption in organizations. They argued that some changes associated with IoT adoption in regard to technology implementation costs, difficult interoperability and integration, and limited trust in IoT services result in low quality benefits. Some long standing assumptions regarding the influencing factors have been questioned [57; 59; 63; 64]. In line with these discussions, the technological risks associated with the adoption of blockchain in IoT systems are included in the research model, hence, the following is hypothesized:  
**Hypothesis 4B (H4B).**_The technological risk positively affects the intention to use blockchain technology in IoT systems._
## 2 Materials and Methods
### Segmented Score Mapping  
Score maps of the very last layer in a segmentation network can be interpreted as attributions of the model. They are information-rich and mappable to the input image. When compared to the final segmented map, score maps contain more information, but this information is not mapped to the segmented regions. One strategy is to merge information from different maps, for example score maps and segmentation maps. An example is segmented score mapping (SSM) based on the XRAI algorithm, introduced by [32]. XRAI identifies regions which are relevant to the predicted class by operating on the output of the integrated gradient. It targets semantic segmentation networks and uses score maps for attribution. To implement SSM, the so-called Felzenszwalb method is utilized [33]. It is governed by a set of hyper-parameters whose selection may affect the segmentation result. Performing the algorithm multiple times using different parameters is equivalent to over-segmenting. The final output result is a set of overlapping segmented regions. The SSM can be implemented in the following manner. First, the sum of attribution values for each area is computed to find the important regions. Subsequently the area with the maximum average value is added to an initially empty mask. The convergence criterion is reached when the mask size is equal to the input image.