esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Experiment
### SHPB Test System  
In impact experiments, A Split Hopkinson Pressure Bar [34] was employed, as shown in Figure 2. The length of the impact bar, the incident bar, and the transmitted bar are 400 mm, 3000 mm and 2000 mm, respectively, and the diameters of the bars are 80 mm. The material parameters of the incident bar and transmitted bar made by 40 CrmoV alloy are listed as follows: density is 7 600 kg/m\({}^{3}\), the elastic modulus is 210 GPa, Poisson's ratio is 0.25 and the longitudinal wave velocity measured is 5164 m/s. The voltage signals, recorded from the strain gauges deployed in the middle of the incident bar and the transmitted bar, respectively, and crack propagation gauges glued on the specimens, were collected by ultra-dynamic strain amplifier, then stored in a digital oscilloscope. Furthermore, voltage signals could be converted into strain signals after noise reduction operation. The impact velocity of the impact bar was measured by the photoelectric velocity acquisition system. A waveshaper, a piece of purple copper of coin size, was employed in the experiment for the purpose of obtaining the ideal wave, as it has the function of attenuating the diffusion effect of a stress wave and reducing the oscillation effect of high-frequency stress waves.  
The crack propagation gauges (CPG)s were applied in the experiment, and the CPG test system is illustrated in Figure 2. CPG, which is a kind of gauge made up of a glass-silk baseplate with fine wires equispaced on it, was used to measure the velocity of the running crack. During the experiment, the CPGs were tightly glued on the surface of specimens, and the area that they were located on was large enough to cover the conceivable crack propagation path. During the crack propagation process, the wires break one by one as the crack propagates. Then, the total resistance of the CPG and the voltage signal associated changes accordingly. Once the fine wire is fractured, the voltage signal at that moment changes sharply in response. This means that the voltage signal curve presents a step-by-step growth trend, which could be used to identify the location of running crack tip at a certain moment. Therefore, after analyzing the voltage signal data, the derivative of the voltage with respect to time could be obtained, and the time corresponding to the extreme derivative value was the moment when the running crack tip arrived. Based on the above principles, the average velocity of a moving crack between two fine wires which had a fixed distance of 2.25 mm could be calculated according to the gaps between the two fine wires and the fracture time.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & \(c_{d}\) **(m/s)** & \(c_{s}\) **(m/s)** & \(c_{R}\) **(m/s)** & \(\rho\) **(kg/m\({}^{3}\))** & \(E_{d}\) **(GPa)** & **\(K\) (GPa)** & \(\
u_{d}\) \\ \hline Sandstone & 3630 & 2267 & 2140 & 2797 & 25.97 & 13.53 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of YaMeng black sandstone.
## 4 Discussion  
In this study, the data required for learning were secured by applying an image-generation method through the DCGAN and an automated augmentation method using a CNN and RNN. Through the Xception-based image quality-measurement method, it has been augmented by approximately 18 times, compared to the existing training data. Comparing the performance of the model with the augmented data, the AutoAugment with the CIFAR-10 dataset is more suitable for the classification of actual gastroscopy images because it delivers better performance than that of the DCGAN. Since a larger amount of data are required to generate various types of data through the DCGAN, data with relatively better quality than that of CIFAR-10 could not be obtained. Moreover, many of the AutoAugment policies of the CIFAR-10 dataset are based on a variety of colors, which seem to yield superior classification results on the gastroscopy images. In this study, our proposed model, which aims to augment data using the DCGAN and CIFAR-10 datasets, delivered superior performance. The accuracy, Az value, and F1 score of the model were 0.851, 0.9, and 0.841, respectively. The value of precision increased to 0.896, but the value of recall decreased to 0.793. The higher the values of both indicators, the better the model. However, the two values have a trade-off relationship; thus, the higher the precision, the lower the recall. The findings of this study confirm that the classification model generated through the DCGAN and the policies of the CIFAR-10 dataset with the addition of an image quality-measurement method performs the best. Moreover, securing sufficient learning data by augmenting data through the DCGAN and CIFAR-10, as proposed herein, and selecting data through an image quality-measurement method are effective in improving the performance.  
This paper proposed a method of solving the problem of performance deterioration of deep learning owing to insufficient data. The proposed method augments the data required for learning and solves the problem of the lack of data through the image quality-evaluation process. Not only in the medical field but also in various areas, such as defect inspection in a smart factory, pest classification, and distracted driving detection, the problem of data shortage is being solved by data augmentation [21; 22; 23]. The method proposed herein is expected to be applicable to not only medical images but also various areas where data are insufficient. Future studies will be required to evaluate the adaptability of our methods to other modalities.  
To generate data through DCGAN, there is a limitation in that the larger the amount of data, the more diverse and high-quality data can be generated. Therefore, in the future, we plan to conduct research to generate data through a GAN after going through different data-augmentation methods. In addition, we planned to compare the performance of the model by creating an image through a different type of GAN other than DCGAN or applying it to other CNN models.
# A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring
## 1 Introduction
### State of the Art  
In this section, first supervised and unsupervised approaches for robot condition monitoring are presented. As this research area does not present the fields of anomaly detection and trend detection models completely, a broader overview of these research fields is given subsequently. Finally, the state of the art is summarised and the research gap is presented that we are addressing.
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Dependent Variable** & **Groups** & \begin{tabular}{l} **Group of** \\ **Comparation** \\ \end{tabular} & \begin{tabular}{l} **\(\Delta\)X between** \\ **Groups** \\ \end{tabular} & \begin{tabular}{l} **SE** \\ **\end{tabular} & \
\begin{tabular}{l} **\(p\)** \\ **CI—Lower** \\ \end{tabular} &
\begin{tabular}{l} **95\%** \\ **CI—Upper** \\ \end{tabular} \\ \hline \multirow{4}{*}{Facial floatation—TI} & E1 & E2 & \(-1.009\) & \(0.660\) & \(0.426\) & \(-2.739\) & \(0.721\) \\ \cline{2-7}  & E3 & \(-7.832\) * & \(0.652\) & \(<\)\(0.001\) & \(-9.540\) & \(-6.124\) \\ \cline{2-7}  & E4 & \(-17.974\) * & \(0.644\) & \(<\)\(0.001\) & \(-19.663\) & \(-16.286\) \\ \cline{2-7}  & E2 & E3 & \(-6.823\) * & \(0.687\) & \(<\)\(0.001\) & \(-8.622\) & \(-5.024\) \\ \cline{2-7}  & E4 & \(-16.965\) * & \(0.679\) & \(<\)\(0.001\) & \(-18.745\) & \(-15.185\) \\ \cline{2-7}  & E3 & E4 & \(-10.142\) * & \(0.671\) & \(<\)\(0.001\) & \(-11.901\) & \(-8.384\) \\ \hline \multirow{4}{*}{Facial floatation—TF} & E1 & E2 & \(-0.819\) & \(0.645\) & \(0.585\) & \(-2.509\) & \(0.871\) \\ \cline{2-7}  & E3 & \(-7.178\) * & \(0.637\) & \(<\)\(0.001\) & \(-8.846\) & \(-5.509\) \\ \cline{2-7}  & E4 & \(-17.378\) * & \(0.629\) & \(<\)\(0.001\) & \(-19.027\) & \(-15.729\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Multiple comparisons (Turkey HSD) of the groups of the subjects and tests. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Group** & **Test of Study** & **Tests** & \(\Sigma\) & **df** & **MS** & **F** & \(p\)** \\ \hline \multirow{3}{*}{Facial floatation} & TI & 4798.288 & 3 & 1599.429 & 315.353 & \(<\)\(0.001\) \\ \cline{2-7}  & TF & 4502.385 & 3 & 1500.795 & 310.219 & \(<\)\(0.001\) \\ \cline{2-7}  & TI & 6774.849 & 3 & 2258.283 & 354.793 & \(<\)\(0.001\) \\ \cline{2-7}  & TF & 6593.573 & 3 & 219.858 & 359.522 & \(<\)\(0.001\) \\ \hline \multirow{3}{*}{Front tuck} & TI & 6608.349 & 3 & 2202.783 & 425.618 & \(<\)\(0.001\) \\ \cline{2-7}  & TF & 6682.262 & 3 & 2227.421 & 471.267 & \(<\)\(0.001\) \\ \cline{2-7}  & TI & 7824.850 & 3 & 2608.283 & 359.173 & \(<\)\(0.001\) \\ \cline{2-7}  & TF & 7787.918 & 3 & 2595.973 & 380.889 & \(<\)\(0.001\) \\ \hline \multirow{3}{*}{Front layout with nose clip} & TI & 5966.166 & 3 & 1988.722 & 219.966 & \(<\)\(0.001\) \\ \cline{2-7}  & TF & 5944.514 & 3 & 1981.505 & 230.943 & \(<\)\(0.001\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANOVA (analysis of variance) between 4 groups in the tests of the study.
# Energy-Efficient Respiratory Anomaly Detection in Premature Newborn Infants
## 5 SNN-Based Respiratory Classification
### SNN Mapping to Neuromorphic Hardware  
The SNN model generated using the conversion approach is analyzed in CARLsim [40] to generate the following information.  
* Spike Data: the exact spike times of all neurons in the SNN model.
* Weight Data: the synaptic strength of all synapses in the SNN model.  
The spike and weight data of a trained SNN form the SNN workload, which is used in the NeuroXplorer framework [41] to estimate the energy consumption. Figure 10 shows the NeuroXplorer framework.  
The framework inputs the 1 DCNN model and estimates the accuracy and energy consumption of the model on a neuromorphic hardware. Internally, NeuroXplorer first converts the 1 DCNN to SNN using the steps outlined before. It then simulates the SNN using CARLsim. The extracted workload is first decomposed using the decomposition approach presented in [42]. This is to ensure that the workload can fit on to the resource-constraint hardware.  
Typically, neuromorphic hardware is designed with tile-based architecture [43], where each tile can accommodate only a limited number of neurons and synapses. The tiles are interconnected using a shared interconnect such as Network-On-Chip (NoC) [44] or Segmented Bus [45]. Therefore, to map an SNN into a tile-based neuromorphic hardware, the model is first partitioned into clusters, where each cluster consists of a proportion of the neurons and synapses of the original machine learning model [46]. Each cluster can then fit onto a tile of the hardware. Then, the clusters are mapped to the tiles to optimize one or more hardware metrics such as energy [47; 48], latency [49; 50; 51; 52; 53], circuit aging [54; 55; 56; 57; 58; 59], and endurance [60; 61; 62]. We use the energy-aware mapping technique of [48].  
Once the clusters of the converted 1 DCNN model are placed with the resources of the neuromorphic hardware, we perform cycle-accurate simulations using NeuroXplorer, configured to simulate the Loihi neuromorphic system. Table 2 shows the hardware parameters that are configured in NeuroXplorer.
# Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
###### Abstract  
Feature selection is a preprocessing step for various classification tasks. Its objective is to identify the most optimal features in a dataset by eliminating redundant data while preserving the highest possible classification accuracy. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. Afterwards, each approach manipulates the solutions obtained by both algorithms in a different way. The objective of this combination is to overcome the GWO stuck-in-local-optima problem that might occur by leveraging the PSO-wide search space exploration ability on the solutions obtained by GWO. Both S-shaped and V-shaped binary transfer functions were used to convert the continuous solutions obtained from each proposed approach to their corresponding binary versions. The three proposed approaches were evaluated using nine small-instance, high-dimensional, cancer-related human gene expression datasets. A set of comparisons were made against the original binary versions of both GWO and PSO algorithms and against eight state-of-the-art feature selection binary optimizers in addition to one of the recent binary optimizers that combines PSO with GWO. The evaluation results showed that one of the proposed S-shaped and V-shaped approaches achieved 0.9 and 0.95 average classification accuracy, respectively, while selecting the fewest number of features. The results also confirmed the superiority of one of the proposed V-shaped approaches when compared with the original binary GWO and PSO approaches. Moreover, the results confirmed the superiority, in most of the datasets, of one of the three approaches over the state-of-the-art approaches. Finally, the results revealed that the best approach in terms of classification accuracy, fitness value, and number of selected features had the highest computational complexity.  
Keywords:swarm intelligence; feature selection; particle swarm optimization; grey wolf optimization +
Footnote †: journal: Applied sciences
# An Experimental Evaluation of Path-Based Product Line Integration Testing and Test Coverage Metrics
## 7 Threats to Validity  
The proposed method has the following internal and external threats to validity:  
* _Instrumentation issue_: The values of cost factors, such as \(C_{driving_{path}}\), \(C_{exec}\), and \(C_{aux}\), are not calculated based on actual data; rather, they are assumed values derived from the data reported in an existing technical report. Even though the estimation has been performed conservatively, their values may vary from one SPL organization to another and from one product line to another within the same organization. Consequently, the values of these cost factors are not stable, which may result in inconsistent results when applying the proposed method.
* _Random selection bias_: To validate the cost savings of the proposed method, this paper randomly generates possible MM-paths for an SPL. While the paper generates a specific number of MM-paths for an SPL using a combinatorial method to reduce selection bias, this approach may still pose a threat to drawing generalized conclusions about the proposed method.
* _Limited generalizability_: (1) The proposed approach analyzes MM-paths based on a product line code base of generative product lines. Therefore, the proposed method is only effective for SPLs that employ variability mechanisms supporting generative product lines based on the product line code base. (2) The proposed method has been validated using a small product line, and, as a result, the cost savings attributed to removing redundant testing may be overshadowed by the cost of deriving a large number of MM-paths in a larger size of product lines.  
Given these potential threats to validity, the conclusions presented in this paper may not be guaranteed to hold when applying the proposed method in real-world SPLs.
## 3 Results
### 3.3 LD Decay Analysis and Markers Significantly Associated with Stripe Rust Resistance  
All chromosomes fell into five groups based on the genetic distance between them (Table 2). The markers with distance <5 cM showed the highest values for LD. Within this group, the highest LD was observed for chromosome 6A, while the lowest LD was found for chromosomes 1B and 6B. Chromosomes 3A, 5B, and 5A had the highest LD values when inter-marker distances were >50 cm. To identify genomic regions associated with SR resistance, we conducted a GWAS using 6280 SNP markers with a mixed linear model (MLM) on the 139 accessions. As shown in the Manhattan plots and quantile-quantile (Q-Q) plots, the MLM model was well fitted to the data and showed less deviation from the expected \(p\)-values (plots not shown).  
A total of 6280 markers were utilized for GWAS of stripe rust responses, and 230 markers were found significant. One hundred and fifty-three SNPs were significantly associated with resistance to _Pstv-37_, among which 100 were distributed across the B sub-genome and 53 across the A sub-genome. Chromosomes 5B, 6B, and 7B contained the greatest number of significant markers within the B sub-genome with 16, 21, and 29, respectively. With respect to sub-genome A, chromosomes 1A and 4A had the greatest number of significant SNPs with 13 and 10, respectively. Seventy-seven significant associated markers were found in response to race for _Pstv-40_. Chromosomes 4A, 1A, and 3B had the greatest number of significant associated SNP markers with both races, respectively. A summary of the marker associations for stripe rust resistance to the two races of _Pst_ is presented in Table S1. The percentage of phenotypic variation (\(R^{2}\)) accounted for by significant SNPs ranged from 4 to 18%. Only three marker-tra  
Figure 2: Population structure analysis based on 139 durum wheat accessions and 6280 SNP markers.  
associations showed an \(R^{2}\) higher than 10% (Table S1). Of the 230 marker-trait associations identified in this study, only 30 were found in response to both races of _Pst_ (Table 3).
# Micro-Raman Spectroscopy Investigation of Crystalline Phases in EAF Slag
###### Abstract  
Electric arc furnace (EAF) slags were investigated by micro-Raman spectroscopy. A slag sample characterised by well-developed crystalline phases was obtained. The EDXS elemental composition made it possible to recognize the grains corresponding to the brownmillerite, larnite and magnesioferrite phases, as identified by XRD in the same powdered sample. The grains were collected and analysed by \(\upmu\)-Raman spectra, and the results showed good reproducibility in each grain and good agreement with spectra of the phases reported in the literature. A Raman database devoted to crystalline phases of EAF slag identification was created to be used by portable Raman instruments, allowing the phase characterisation of the slag directly during steel production.  
EAF slag; micro-Raman investigation; XRD characterisation; SEM-EDX characterisation; spectra database; crystalline phases +
Footnote †: journal: Applied sciences
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 7 Conclusions and Future Work  
Battery-free energy harvesting sensors provide a promising solution for a maintenance-free and perpetual sensor operation. We have proposed a novel approach for hybrid energy storage, which is able to adapt and reconfigure energy storage depending on the environment and resolve an important trade-off between fast start-up time and amount of stored energy. The evaluation based on simulations and the hardware implementation on the PowerCast RF energy harvesting kit have demonstrated that the hybrid platform can operate in environments where fixed capacitor nodes can fail. In particular, the proposed hybrid approach showed up to 40% and 80% performance improvement over a fixed capacitor design in terms of usable harvested energy and the sensor coverage, respectively. We have demonstrated that a traditional single fixed-capacitor design is not suitable for certain RF energy harvesting applications in sparse mobile networks. While we have used a dual-capacitor configuration as an example of a hybrid storage concept, extending the proposed scheme to three or more capacitors may provide applications with more fine-grained control over energy storage. As a typical sensor node has a limited number of output pins for addressing individual capacitors, this would require modifications to the switching circuit and would lead to higher switching cost due to the increased number of relays and other components. Investigating the design trade-offs for various numbers of energy storage devices is a potential future work.  
Conceptualization, B.M. and V.D.; Investigation, B.M. and V.D.; Resources, V.D.; Supervision, V.D.; Writing--V.D and B.M.; Writing--review & editing, V.D.  
This research received no external funding.  
The authors would like to thank [PERSON] and the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.
# A Practical Approach to Modeling and Performance Analysis of a Turboshaft Engine Using Matlab
## 3 Results
### Engine Performance Calculation for Various Compressor Rotational Speed  
The calculation results are prepared for the engine operating conditions on the ground while ambient parameters are consistent with ISA (T\({}_{0}\) = 288 K, P\({}_{0}\) = 1013 kPa) for three specified values of compressor rotational speed: Nc = 95%, 98%, and 100%. Since there are no changes in reference parameters relative to DP, which are also calculated for an altitude of H = 0 under ISA conditions, the corrected relative speed is equal to the relative speed. Consequently, the operating parameters of the high-pressure spool and the temperature rise Tt\({}_{4}\) correspond to values for Nc_rel_cor = 0.95, 0.98, and 1, respectively. The engine operating points for these three speeds are shown on the compressor map (Figure 8).  
After determining the operating parameters of the high-pressure spool, calculations are continued for the free power turbine and the exhaust diffuser. As a result, the expansion across the power turbine is determined for each NC speed, and then the values of speed and efficiency of the power turbine are obtained using the map of FTP. The results are  
Figure 8: Compressor map with points for Nc = 0.95, 0.98, and 1.  
Figure 7: (**a**) Comparison of the compressor pressure ratio determined according to the proposed model (red points) with data for the PZL-10W engine adapted with permission from [6] (blue line), (**b**) the relative difference in CPR for selected relative compressor speeds.  
presented on the FTP map (Figure 9) and as a plot of power and specific fuel consumption as a function of the free power turbine rotational speed (Figure 10).  
According to the data presented in Figure 9, for a given rotational speed of the high-pressure rotor, a constant expansion ratio across the power turbine is maintained throughout its entire range of rotational speeds. Naturally, as the rotational speed of the high-pressure spool decreases, the expansion ratio of the power turbine is reduced, resulting in a decrease in the power generated at the engine output shaft (Figure 10 blue lines). Additionally, this power output varies with changes in the efficiency of the power turbine. The turbine  
Figure 10: Output shaft power of turboshaft engine (blue lines) and specific fuel consumption (red lines) for Nc = 0.95, 0.98, and 1.  
Figure 9: Free power turbine map with working points for Nc= 0.95, 0.98, and 1.  
achieves its highest efficiency at a reduced rotational speed of approximately 1, where the generated power is also at its peak. Deviating from this speed leads to a reduction in both the efficiency and power of the free power turbine, which in turn decreases the output shaft power. The reduction in engine power leads to an increase in specific fuel consumption. As indicated in Figure 10 (red lines), the lowest specific fuel consumption occurs at a high-pressure rotor speed of 1 and a power turbine speed of 1 (solid red line), consistent with published literature characteristics of turboshaft engines [35].