esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
A backplane board provides an electrical interface among CubeSat components. It has additional advantages, such as ease of assembly/disassembly and fewer harnesses than a de facto standard PC/104 style interface. To confer more flexibility to the interface, a software-configurable backplane was developed. The backplane with an spMACH LC4256 ZE CPLD was demonstrated onboard a 1U CubeSat for more than 2 years in orbit. Through a CPLD, digital interfaces can be rerouted without requiring hardware changes by reprogramming the CPLD. This saves both money and time when redesigning the board.  
The designed configurable interface board was verified to be scalable and adaptable to different CubeSat sizes while absorbing the challenges in the process. While a hardwired backplane is applied to a specific satellite, the scalable standard bus in this study exhibited its reusability in multiple satellite projects. Hence, it has the advantage of providing additional time for system-level integration and verification, which are essential for a reliable and successful mission. The design concept could also be advantageous to satellite manufacturers that offer hosted payload services, where bus resources are utilized to support many payloads.  
Several future studies have been identified to fully realize the configurable backplane's scalability. The first is to verify that it can support communication protocols other than UART and SPI. Protocols such as I2C, CAN, and Ethernet are currently being used and are desired to be utilized in future projects by CubeSat developers and vendors. Secondly, a bridge circuit that translates different protocols can be incorporated into the configurable backplane. The bridge circuit will allow the use of the BIRDS electrical bus architecture on missions that require protocols other than UART and SPI. Lastly, on-orbit reconfiguration of the CPLD as a contingency could be explored.  
Conceptualization, M.S.; methodology, M.S.; software, M.S. and Y.O.; validation, M.S., Y.O., and N.C.O.; formal analysis, M.S.; investigation, M.S. and N.C.O.; resources, M.C.; data curation, M.S.; writing--original draft preparation, M.S.; writing--review and editing, N.C.O., T.Y., Y.O., and M.C.; visualization, M.S. and N.C.O.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by JSPS Core-to-Core Program B: Asia-Africa Science Platforms (JPJSC2020005) and the \"Acquisition and dissemination promotion project consignment fee for international standards related to energy conservation\" by the Ministry of Economy, Trade and Industry, Japan.
# XOR Binary Gravitational Search Algorithm with Repository: Industry 4.0 Applications
## 6 Industry 4.0 Applications
### Motion Planning of Industrial Robots Inside a Digital Twin Created by V-REP Software
#### 6.1.1 Encoding Movements  
To optimize movements, they are encoded in terms of an evolvable array according to the proposed XOR BGSA with repository which is implemented under Matlab software. In this paper, it is assumed that the initial orientation of the robot remains the same during optimization and its position needs to be changed to perform the displacement from the start point to the end point. Considering three x, y and z dimensions and the fact that they can be increased and decreased, there would be six movements which can be represented by any integer value from the set \(S=\{0,1,\ldots,5\}\). However, the proposed optimization algorithm would generate three bits for each movement. To avoid infeasible solutions, the set is considered to have all eight conditions and duplicate movements may be added. Moreover, during optimization duplicate coordinates may occur as a result of back and forth in the movements. To avoid such loops in robot movement, all coordinates are compared with each other and all intermediate movements which result in a loop are eliminated. The interpretation of movements are presented in Table 5.
## 3 Results
### Experimental Results  
In this section, a comparison between the results of the electromagnetic simulations and the measurements of the manufactured prototype is carried out. Figure 8 shows a photograph of the fabricated prototype, which has been fabricated using a mechanical milling machine (LPKF S103 from LPKF Laser & Electronics, stationed at Garbsen, Germany). Measurements have been carried out using Agilent PNA-E8363B with a Test-Set N4420B extension (thus, with a 4-port system), both devices from Keysight, under normal conditions of pressure, temperature and humidity. This network analyzer has a single source, so the DM and CM have been obtained using the program _Physical Layer Test System_ from Keysight that allows the user load calibrations and implement a transformation between the actually measured 4 \(\times\) 4 scattering matrix of the 4-port single-ended circuit and the 2 \(\times\) 2 differential-differential, common-common and differential-common 2 \(\times\) 2 scattering matrices of the balanced 2-port system. Simulated and measured DM and CM responses are depicted in Figure 9. This figure shows a good agreement between simulated and measured results, although the measured lower band is slightly shifted when compared with the simulated one. This fact can be explained if we take into account that multilayer structures can suffer from the presence of small air gaps that may appear if layers are not perfectly stuck. Furthermore, the mechanical milling machine might sometimes remove a thin layer of the substrate, and this affects the effective dielectric constant of the substrate. This effect can be minimized using some suitable prepreg layer, which should be included in the simulations, of course. In order to verify the effect of this hypothesized air gap, the response of the differential filter has been simulated considering a 0.025 mm air gap between the stacked dielectric substrates. This response is depicted in Figure 9b. The results show a good agreement between simulations and measurements. The use of a low permittivity substrate has the puspose of reducing the air gap effect, since the closer the dielectric constant is to that of air, the less the effective permittivity will be affected. The use of low permittivity substrates will also decrease the capacitance between layers so that the cross coupling between resonators will also be lower. The measured results show a CM rejection level greater than 39 dB and 33 dB for the first and second DM passbands, respectively. Measured insertion loss at the first and second DM center frequencies is 0.9 dB and 1.9 dB respectively. In addition, band-to-band isolation is better than 36 dB, which is satisfactory for many practical applications.
# Hybrid Harmony Search for Stochastic Scheduling of Chemotherapy Outpatient Appointments
## 4 Numerical Experiments and Analysis of Results
### Extended Comparison Campaign  
The two different metaheuristics, i.e., hybrid HS and GRASP, were compared based on a newly generated full factorial experimental plan, as indicated in Table 4. To sum up, three classes of problems at varying \(P\)s were considered, each one involving 16-factor configurations and 10 numerical instances that were randomly generated. Hence, 480 runs were elaborated by each algorithm. For each class of problem, Table 7 reports the median \(RPD\) values for each configuration, computed over the provided 10 instances, each one entailing 300 scenarios.  
The numerical outputs reveal the outperformance of HS over the GRASP algorithm. The minimum \(RPD\) values for each configuration are highlighted in bold and all of them  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Instance** & **HS [\%]** & **HS\_NH [\%]** & **HS\_NL [\%]** & **HS\_NHL [\%]** \\ \hline
1 & **0.00** & **0.00** & 0.05 & 0.01 \\
2 & **0.00** & 0.02 & 0.02 & 0.36 \\
3 & 0.03 & 0.02 & 0.04 & **0.00** \\
4 & **0.00** & **0.00** & 0.07 & 0.43 \\
5 & **0.00** & **0.00** & 0.04 & 0.04 \\
6 & **0.00** & 0.03 & 0.12 & 0.13 \\
7 & 0.08 & **0.00** & 0.49 & 2.37 \\
8 & **0.00** & **0.00** & 0.18 & 0.20 \\
9 & **0.00** & 0.02 & 0.17 & 0.14 \\
10 & 0.02 & **0.00** & 0.06 & 0.10 \\
11 & **0.00** & **0.00** & 0.80 & 6.06 \\
12 & **0.00** & 0.15 & 1.72 & 2.51 \\
13 & **0.00** & **0.00** & 0.07 & 0.06 \\
14 & 0.01 & **0.00** & 0.31 & 0.30 \\
15 & **0.00** & **0.00** & 0.13 & 0.55 \\ \(RPD\_med\) & _0.01_ & _0.02_ & _0.28_ & _0.88_ \\ \(RPD\_max\) & _0.08_ & _0.15_ & _1.72_ & _6.06_ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Design of HS: RPD values.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Config./Class and Algorithm** & **Small** & & **Medium** & & **Large** & \\
**and** & **HS** & **GRASP** & **HS** & **GRASP** & **HS** & **GRASP** \\ \hline
1 & **0.00\%** & 0.13\(\%\) & **0.00\%** & 0.47\% & **0.00\%** & 0.84\% \\
2 & **0.00\%** & 0.16\(\%\) & **0.00\%** & 0.55\% & **0.00\%** & 1.23\% \\
3 & **0.00\%** & 0.11\(\%\) & **0.00\%**  
are in the HS-related column. Regardless of the problem configurations, the median \(RPD\) (\(RPD_{med}\)) and the maximum \(RPD\) (\(RPD_{max}\)) support the primacy of HS over the GRASP algorithm. Looking at the \(RPD_{max}\) values it is clear that the GRASP technique worsens as the complexity of the problem rises.  
Finally, the HS algorithm was compared with the LPT rule, which is the strategy adopted by the staff of the oncology unit under investigation. A total of 20 instances, based on the real-life parameter values, were used to compare the LPT rule and HS algorithm. Table 8 shows the results in terms of \(RPD_{s}\). The experiments demonstrate that the HS algorithm outperforms the LPT by approximately 7% on the average \(RPD\) (\(RPD_{ave}\)). These results are consistent with the comparison between the metaheuristic algorithm and the LPT method shown in the work of [PERSON] et al. (2019).
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 3 Methodology
### Susceptibility Modeling and Mapping  
For landslide susceptibility modeling and mapping using DNN, Kernel-based DNN and CNN models, a landslide training dataset was first constructed based on landslide occurrence locations selected from the research area. Since the study area is a small mountain located in the downtown area, the oversampling step was performed, considering that the number of landslide locations was only 103. The same number of samples where a non-landslide would occur was extracted as the landslide location samples, by random sampling with a slope of less than 5 degrees. After that, the sample was randomly divided into two sections: 80% was allocated to the training set, and the remaining 20% was extracted separately to use for performance evaluation [84; 85; 86]. The training set and the test set were adjusted through oversampling, which randomly adjusts the number of data by extracting the same data several times from imbalanced data. Finally, a total 20,000 of training (16,000) and test (4000) datasets were extracted and used for modeling.  
The influential landslide variables were resampled in a grid format with 5 m spatial resolution. The value of each variable was extracted by overlaying 17 landslide influential variables on the training dataset, consisting of landslide and non-landslide samples. Finally, after training the model, susceptibility modeling was performed by applying it to the entire study area. Then, the results of landslide susceptibility were visually mapped with values from 0 to 1 from the three models.
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
###### Abstract
The metamaterial absorber has the potential for certain exceptional qualities, such as simple manufacture, low cost, simultaneous wideband coverage, and perfect absorption. The excellent performance makes the proposed design attractive for applications like solar energy harvesting, photodetection, thermal imaging, photo-trapping, and optical communications.
### NIRS Prediction Equations (Calibration and External Validation)
#### 3.3.1 Lipid Content  
As can be observed in Table 4 (intact) and Table 5 (minced), as it was expected, calibration and cross-validation statistics revealed a better prediction model for lipids in the minced presentation mode in comparison to the intact mode. For both intact and minced LTL, Test 1 provided better results. In fact, the coefficient of determination for cross-validation (1-VR) and root mean square error of cross validation (RMSECV) in minced samples were 0.89 and 0.66 (considering 1111-1400; 1620-1780; 2200-2400 nm wavelength regions; Table 5) whilst in the intact presentation mode, 1-VR decreased to 0.56 and RMSECV was 1.55, in this case considering the full spectral range (Table 4). NIRS prediction models with 1-VR values higher than 0.70 indicated a good predictive ability, whilst values between 0.30 and 0.69 could only be used to separate samples with higher and lower analytical values [30]. Therefore, the model developed with minced LTL could be used to predict the total content of lipids of LTL muscle, regardless of the autochthonous breed they belong to. On the other hand, RPD\({}_{\text{cv}}\)values (widely used for assessing the prediction efficiency of NIRS equations [25]) and RER\({}_{\text{cv}}\)(used to assess accuracy [31]) achieved values of 3.41 and 16.95, respectively, in minced LTL, thus indicating a reliable prediction power for the equations, according to [31,32]. The intact presentation mode, by contrast, would only allow us to classify the lipid meat content into high, low, and medium values. The lower predictive ability of intact samples, when compared to minced samples, was in agreement with scientific literature. For example, [14] obtained better calibration models after sample preparation of subcutaneous fat from Iberian breed pigs, compared to direct measurements in the carcass. The same influence of sample preparation was observed in LTL muscle from commercial pig breeds [33,34], as well as in the Iberian breed [15]. Mean values of 1-VR and RMSEC in lipids content from intact and minced LTL observed in the present study were slightly lower than those reported by [15] in LTL muscle from Iberian pigs. Again, [18] reported higher values than those reported in the present study for these parameters using the same muscle but employing only Krskopolje and Turopolje pig breeds.
## 5 Discussion
### Performance of Massive Deployment by the Proposed BT Solution
Figure 11: The speedup factors achieved by (**a**) the proposed BT solution and (**b**) the traditional multicast solution. Figure 12: The average deployment time of each configuration obtained from the experimental results based on the proposed BT solution as well as the ideal deployment time derived from Equation (2). The multicast solution has several disadvantages. First, the multicast protocol is unreliable for packet delivery due to the nature of the UDP protocol. Therefore, packets may be lost, because no handshaking mechanism is available in the UDP protocol, and the client must request the lost packets. When a client requests these packets, the remaining clients have to wait for the synchronization of all of the clients before receiving the next data. Thus, the deployment performance using the multicast solution is reduced. Therefore, the multicast-based solution is very difficult to scale up. In addition, the multicast function may be disabled in the network switch by the network system administrator for security reasons. Users may not have the authority to enable the multicast function. In this case, the multicast solution cannot be used. Table 6 compares the work and the previous solutions for massive bare-metal provisioning. The multicast solution implemented in the previous work and the BT-based solution proposed and implemented in this work were shown to be feasible. From the aspect of efficiency, the BT solution has better efficiency than the multicast one. Based on the experimental results demonstrated in Section 4, the total time-saving ratio for 32 experiments was 81.760%. From this aspect, the total deployment time required by the BT-based solution is 18.240% of the time for the multicast solution. Thus, the efficiency of the BT-based solution can be 5.482 times (1/0.1824 = 5.482) that of the multicast ones. Considering the scalability issues, the efficiency enhancement can be much higher when the number of clients to be deployed is more than 32. In addition, from the experimental results in this study and previous works by other researchers [94; 95], the BT-based solution was confirmed to have better reliability and scalability than the multicast solution.
## 3 Methods
### EU-SENSE Case Study
#### 3.1.2 Heterogeneous Sensor Node Components  
The EU-SENSE system focuses on a sensor node, which combines different detection technologies (therefore, it is called heterogeneous). The reason for the diversification of detection technologies is the increase in the spectrum of detectable substances and the availability of rich datasets for the application of data fusion algorithms. By applying data fusion and machine learning, the system can reduce the number of false alarms, and classify and identify the detected substance. In the design and development of a heterogeneous sensor node, the following detectors were integrated:  
* Airsense IMS detector with ammonia chemistry with integrated photon ionisation detector (PID);
* Airsense IMS detector with water chemistry with integrated electrochemical cell (EC);
* Proengin AP4C flame photometric detector (FPD);
* TNO SRD metal oxide detector prototype.  
The EU-SENSE system, through machine-learning, is also able to limit the impact of the false alarm rate on the sensor readouts, and thus, produce fewer false alarms. The system learns the environmental conditions in the area where the network is deployed and detects the anomalies. Based on the anomaly detection results, the system performs data fusion classification and identification to determine whether the detected anomaly is harmful or not.  
If the alarm is triggered, the EU-SENSE system enables end-users to estimate the source of the hazard because of the source location estimation tool. Moreover, the system provides crucial functions such as modelling the dispersion of a toxic chemical substance. These functionalities largely increase the situational awareness of the first response units, aiding them to apply countermeasures and evacuation.
# Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation +
## 3 Results
### Comparison with TDOA  
The LSSVM direct localisation performs better than TDOA in the dense urban environment as shown in Figure 9, below. This is probably because it is benefiting from the uniqueness of multipaths in such an environment. Also, TDOA depends on the performance of NLOS identification and mitigation, so in a case where there are insufficient LOS paths, the time delays used may result in a significantly over-estimated range.  
Figure 10, below, shows that in open areas, TDOA produces better location accuracy under any given outage probability greater than 0.08. For our chosen standard outage probability of 0.2, TDOA has performs better over the Direct method, by 5m. This is because of the availability and quality of LOS paths. TDOA performance becomes comparable to the Direct method as some NLOS paths start to be included in the algorithm due to unavailability of sufficient LOS paths. Also, some MS positions may suffer from geometric dilution of precision (GDOP) when the 3 BSs chosen are in an undesirable geometry, such as in a straight line.  
Figure 8: Direct method vs. TOA/AOA.  
Figure 9: TDOA vs. Direct method in an urban environment.
# Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
## 3 Experimental Data and Results
### Long-Term Test in Different Driving Conditions  
In the short-term test, we found a relationship between EAR, PERCLOS, MAR, fatigue warning threshold M, and the driver fatigue state. In order to visually evaluate the changes of eye and mouth states under various driving conditions, we require the drivers to carry on two driving conditions, one is drowsy driving, as shown in Figure 11, and the other is normal driving, as shown in Figure 12.  
Figure 11 demonstrates one typical condition that when the driver is in drowsy driving state, throughout the driving period, the values of EAR, MAR, PERCLOS, and M are greater than their threshold values. According to the proposed algorithm, this research can accurately identify the drowsiness and yawning caused by fatigue (red line part), and automatically determines the state of drowsiness: mild fatigue, moderate fatigue, or severe fatigue at this time.  
Figure 12 shows one typical condition that when the driver is in normal driving state, the values of EAR, MAR, PERCLOS, and M are much less than their threshold most of the driving time. Fatigue warning (red line part) occurs in some places due to the occasional blinking at that moment, rather than dozing and yawning out of fatigue, which means during the long period of driving, the driver is sober.  
In Figures 11 and 12, M is the judgment index of facial multi feature fusion which is used to judge the driver's fatigue. Each parameter is fused and calculated according to different weights, that is, it ensures the accuracy of detection and avoids missing detection  
Figure 11: EAR/MAR/PERCLOS and M under drowsy driving.  
Figure 12: EAR/MAR/PERCLOS and M under normal driving.  
or false detection. Moreover, the driver's fatigue state is judged according to the size of the \(N\) value.  
In this study, when the driver finished the driving, they were asked to finish the questionnaire about each segment (when the M line in Figures 11 and 12 is red) according to the video. Finally, the value of scoring from the drivers can be used as the ground truth of driver fatigue levels. The self-report assessment questionnaire is shown in Table 1.  
The test results show that the accuracy of the whole detection system reaches 95.1%, which meets the requirements of the fatigue detection system in the actual situation.  
In summary, the algorithm proposed in this paper can accurately identify different driver fatigue states online, and lays a theoretical and practical foundation for driver fatigue online detection based on deep learning and facial multi-feature fusion.