esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing
## 2 Materials and Methods
### Sample Preparation  
All experimental single-track profiles were prepared using a commercial Impact Innovations (Haun, Germany) 5/11 cold spray gun guided by an ABB (Zurich, Switzerland) 4600 robot with 6 degrees of freedom, as can be seen in [39]. The gun was equipped with a long pre-chamber and an OUT1 tungsten carbide de Laval nozzle with a 6.2 mm exit diameter from Impact Innovations. The powder feedstock in this study was commercial purity grade \(-2\) titanium from AP&C (Boisbriand, Canada) which was prepared by gas atomisation and distributed within the size of 15 to 45 \(\upmu\)m (i.e., D\({}_{10}\) = 19 \(\upmu\)m, D\({}_{50}\) = 34 \(\upmu\)m and D\({}_{90}\) = 45 \(\upmu\)m). The working gas was Nitrogen, preheated to 600 \({}^{\circ}\)C at a pressure of 5 MPa, accelerating the powder particles injected into the nozzle upstream at a feed rate of 1.9 kg/h. All spray variables and conditions were held constant during all experiments, except those listed in Table 1. A strip of commercial purity grade \(-2\) titanium was used as a substrate with a dimension of 6 \(\times\) 30 \(\times\) 200 mm, having its surface processed with a milling machine from Avenax Machinery (Taichung City, Taiwan) and subsequently ground with a P120-SiC energy paper from LECO (Moenchengladbach, Germany). This surface processing was followed by cleaning with ethanol before the experiments. The fabrication of experimental single-track profiles was randomised to ensure statistically unbiased results with minimal effects of potential extraneous factors [40]. RobotStudio(r) software version 6.08 (ABB Robotics, Zurich, Switzerland) was used to confirm that there was sufficient distance beyond the substrate's edge to ensure that the robot's trajectory and traverse speed were stabilised before fabricating the profiles.  
The geometry of each single-track profile was measured at five randomly selected locations using a LEXT OLS4000 confocal laser scanning microscope (Tokyo, Japan) and scanControl 2950-100 laser scanner from Micro-Epsilon (Ortenburg, Germany) with a \(z\)-axis measuring precision of at least 12 \(\upmu\)m. These measurements were processed with the in-built filtering methods: flat Surface filtering in LEXT OLS4000 and average filtering with a filter size of 7 in scanControl Configuration Tool version 6.0. Additional filtering was applied with a local regression method using weighted linear least square and second-order polynomial model in MATLAB version R2018a. The five filtered track profiles were averaged to form each sample profile, as depicted in Figures S1-S3 in the Supplementary Materials, which was then considered for all modelling approaches in this study.
## 3 Materials and Methods
### Hardware  
As physical support for all the tests carried out in this work, the custom-developed YetiMote IoT devices presented in Figure 1[24] have been used. These nodes are based on a high-performance low-power STM32L476 RE microcontroller [25] running at 48 MHz. This microcontroller is internally equipped with 512 KB of Flash memory and 128 KB of RAM memory, and supports several low-power modes. Several sensors (accelerometers and temperature) are provided in YetiMote nodes as well as several Input/Output interfaces: UART, SPI, I2C, USB and a microSD card slot. Therefore, these nodes have many more resources than the nodes traditionally used in WSN--such as Micaz or TelosB. This makes them suitable to run computing-intensive processing tasks as expected in some IoT applications in which sensor devices must provide processed high-level information. For this reason,the experimental tests carried out in this work are performed using YetiMote nodes that run processing and communication tasks.  
These nodes also include an 868 MHz radio interface, which consists of a SPIRIT1 low-power transceiver [26], used with the following configuration:  
* Modulation scheme: GFSK.
* Receiver bandwidth: 540 kHz.
* Frequency deviation: 127 kHz.
* Bit rate: 250 kbps.
* Output power: 11 dBm.
* Automatic CRC handling: 1 byte.
* Preamble: 4 bytes.
* Synchronization Word: 4 bytes.
* Forward Error Correction (FEC) enabled.
* Data whitening enabled.
###### Abstract
[34] proposed hybrid deep learning clustered models for the prediction of wind speed and wind power using various combinations of the Recurrent Kalman Filter (RKF), the Fourier Series (FS), the Wavelet Neural Network (WNN), and the ANN for optimal performance. [PERSON] et al. [35] carried out a study using both time-series data and multivariate regression data to forecast wind speeds in Taiwan. The SARIMA method and the Least-Squares Support Vector Regression for Time Series with Genetic Algorithms (LSSVRTSGA) were applied to forecast wind speed in a time series, and the Least-Squares Support Vector Regression with Genetic Algorithms (LSSVRGA) and Deep Belief Network with Genetic Algorithms (DBNGA) models were utilized for the prediction of wind speed in a multivariate format. [PERSON] et al. [36] analyzed four methods (ANN with RBF, ANFIS, ANN-GA, and ANN-PSO) based on AI to forecast short-term wind speed data. An AI approach composed of a hybrid NN modified by GA and PSO, which functioned as a model predictor, was recommended to forecast maritime weather variables an hour ahead by [PERSON] et al. [37]. In this study, a comprehensive and detailed comparison is made by using four different wind speed and wind power estimation approaches. For the first time in the literature for the Canakkale region, both hourly and daily wind speed estimations are realized using these four methods. Two different statistical methods are utilized to determine the hourly wind speed: the Weibull and Rayleigh distributions. In addition, the ANN-GA and ANN-PSO hybrid approaches are proposed for daily estimation. After evaluating the performances of these four methods separately, wind power density estimation is performed for all methods on a daily basis and a comparison is established. Meteorological data, including the average wind speed and direction at an altitude of 10 m, the wet bulb temperature, the actual pressure, the maximum and minimum temperature, the relative humidity, the average temperature, the total global solar radiation, the areal precipitation, the amount of cloudiness, the sea water temperature, and the hours of sunshine, are taken from the Turkish State Meteorological Service for a 12-month time frame and analyzed in detail. There is a lack of publications on wind speed and wind power estimation for the Canakkale region using hybrid and AI-based methods. It is found that the hybrid ANN-GA and ANN-PSO methods are not applied together with the Weibull and Rayleigh techniques and the performance comparison of these methods has not been presented previously in the few existing studies carried out for this region. With the aim of filling the gap in the literature, this paper is proposed and organized into four sections. After this introduction, which presents a comprehensive literature review, theoretical calculations and an analysis of wind data in Canakkale are provided in Section 2. The analysis results and discussion are given in Section 3. Finally, the conclusions of this study are presented in Section 4.
## 3 Results
### Thr-Prx Are More Widely Distributed and Ser-Prx Is More Prevalent in Bacteria  
As the presence of Ser or Thr provokes dramatic alterations in the biochemical and structural properties of yeast 2-Cys Prx, we investigated the phylogenetic distribution of Ser-Prx and Thr-Prx. We analyzed 45,939 unique protein sequences, out of which 15,034 accessions presented AhpC-TSA, Redoxin and 1-CysPrx_C domains denominations according to Pfam and no other domain fused, except for SCO1-SenC. Out of the sequences with taxonomic annotation at domain level, Thr-Prx represented the majority (14,092 sequences, approximately 93.7%), whereas the remaining (941 sequences, approximately 6.3%) are Ser-Prx (Table 3). Notably, the proportions between Thr-Prx and Ser-Prx are different in the three domains of life. Ser-Prx sequences are proportionally more representative in bacteria (7.4%) compared to eukaryotes (2.7%) and archaea (1.4%) (Figure 4). We also found 45 Thr-Prx records deposited in the NCBI database obtained by metagenome approaches and three of them were uncategorized. Figure 3: Inactivation of yeast Tsa1, Tsa2, and mutants by CHP. Similar to Figure 2, _S. cerevisiae_ Tsa1 (**A**), Tsa1T445 (**B**), Tsa2 (**C**), or Tsa2547 (**D**) were treated with growing amounts of CHP and the absorbance of NADPH was monitored at 340 nm. The experiment was executed with 1 \(\upmu\)M Prx, 2 \(\upmu\)M Trx1, 0.3 \(\upmu\)M TrxR1 and increased amounts of CHP (0 \(\upmu\)M = \(\bullet\) black, 500 \(\upmu\)M = \(\blacksquare\) blue, 1 mM = \(\times\) orange, 5 mM = \(\blacktriangledown\) green and 10 mM = \(\blacklozenge\) red) in buffer containing 50 mM HEPES pH 7.4, 100 \(\upmu\)M DTPA, 1 mM sodium azide, and 150 \(\upmu\)M NADPH. Error bars represent the standard deviations. All the experiments were performed in triplicate and repeated at least three times with similar results. The number of sequences of Archaea 2-Cys Prx deposited in the database is very low compared to the other domains of life. Nevertheless, the number of Thr-Prx (71) from Archaea is superior to that of Ser-Prx (1). Thr-Prx are also more widely distributed in Eukarya (2949 representatives present in 12 Superphyla groups) than Ser-Prx (90 sequences found in only four groups) (Table 3). For instance, only one species presents Ser-Prx in chordates (killfish _Fundulus heteroclitus_), whereas a wide range of tetrapods presented only Thr-Prx (See Supplementary Material, Spreadsheet S1). Concerning bacteria, 16 Superphyla groups contain Thr-Prx, with the prevalence of the Proteobacteria followed by Sphingobacteria (FCB group) and Thermodesulfobacteria groups. Conversely, Ser-Prx occurs with striking predominance in the Terrabacteria group (Table 3). Curiously, Thr-Prx are distributed similarly in Gram negative and Gram positive, whereas Ser-Prx are found mostly in Gram negative (Actinobacteria and Firmicutes).
# New Training Tasks for Stepwise Loading in Isometric Bodyweight Squat with Active Posture Control
## 4 Discussion  
The influence of changing the knee flexion angles on muscle activity in bodyweight squats has been previously reported in several studies [18; 19; 20; 21]. However, in most studies, the subjects used equipment such as weight bars [19] or decline boards [18], or were analyzed on unstable floors [20; 21]. The COP (or center of gravity) was not controlled in any of the studies. Contemporarily, muscle activity in isometric bodyweight squats has rarely been analyzed. Previously, muscle activity patterns were reported to vary significantly according to the COP location in the squat posture [15]. Precise control of the COP is essential for designing training tasks for bodyweight squats. In this study, the impact of changing the knee flexion angles on muscle activity patterns was analyzed using previous datasets; thus, new training tasks were designed for stepwise loading. First, heterogeneous effects of knee flexion on muscle activity patterns were found in bodyweight squats under precise COP control. For example, VM muscle activity increased the load from the FSP to the NSP at knee flexion angles of 90\({}^{\circ}\), while the GL muscle activity decreased under the same conditions. Furthermore, deeper squats do not always provide a higher load to the lower limb muscles (Figure 1B-D).  
Isometric exercises provide moderate muscle strengthening without joint motion [13; 14], and a bodyweight squat can be performed in a limited space without any equipment. Additionally, it can be performed at home without supervision. Therefore, it is suggested that isometric bodyweight squats could be an ideal training approach for elderly people. In a recent study, the effect of mechanically assisted squat exercise on lung function in older females with sarcopenia was reported [22]. Furthermore, an assisted squat may be another possible approach for elderly people. However, the muscle activity characteristics in a simple squat posture should be investigated comprehensively before starting the assisted approach.  
In the experiments, the participants could confirm the COP shifting by themselves, and assistance was needed to maintain the knee flexion angles. It would be ideal for active posture control if the participants could also visually ensure knee flexion angles using some devices. This pilot study has some limitations, including the sample size, sex, and training background of the participants. Further investigation of the appropriate training intensity should be considered as it is essential to modulate it by the number of training tasks for individual conditions. Stepwise loading may relieve the impact for elderly people, especially those at risk of frailty.
# Multimode Guided Wave Detection for Various Composite Damage Types
## 3 Detection of Various Damage Types Using ABT-ABT on a 2-mm Quasi-Isotropic Composite Plate
### Pure Mode Generation Using ABT-ABT
#### 3.2.2 Pure SH0 Mode Generation  
Second, the ABT-ABT was used to excite a pure shear horizontal (SH0) wave in the 2-mm quasi-isotropic CFRP composite plate. The same pitch-catch experiment (shown in Figure 9) was conducted. Only the incident angles of the wedges were adjusted to the theoretical tuning angle of SH0 mode. For the excitation of pure SH0 mode at 500 kHz, an incident angle of 49\({}^{\circ}\) was used, based on [PERSON]'s law. The excitation signal applied to the transmitter ABT was a narrow-band three-count tone burst at a central frequency of 500 kHz.  
To validate the generated pure-mode guided wave, the response signals at five different locations were measured, as shown in Figure 11a. It was found that strong single-wave packets were received at all of the locations. The TOF of the direct wave packet was determined for each location and plotted as a function of distance, as shown in Figure 11b. Similarly, linear regression was used to estimate the group velocity, yielding a value of 3.41 mm/us, which compared favorably to the theoretical SH0-mode group velocity of 3.39 mm/us at 500 kHz. Therefore, the excited guided wave in the 2-mm quasi-isotropic CFRP composite plate was in pure SH0 mode, as expected. The experimental results demonstrate that although the ABT excitation consisted of pressure waves, SH0 was successfully generated due to the anisotropy of the quasi-isotropic composite layup.
# Modified Equation of Shear Strength with Respect to Saturation
###### Abstract  
Unsaturated soil shear strength is a significant topic in geotechnical engineering. The measurement of unsaturated soil properties such as shear strength and matric suction could be costly, hard, time-consuming, and often impractical to obtain. The purpose of this research is to propose a new shear strength model for unsaturated soil and to predict the shear strength by using the degree of saturation directly because the water saturation is easy to obtain. More specifically, this study focused on [PERSON]'s shear strength theory and [PERSON]'s soil-water characteristic curve to describe the effect of saturation on shear strength. The new shear strength model was expressed as a function of saturation degree, residual degree of saturation, effective shear strength indices, net normal stress, and five parameters. The performance of the presented model in this paper was verified by fitting it to data obtained by laboratory tests on silty sand including the triaxial shear test and soil-water characteristic curve test. From these two laboratory tests, we obtained the variation in the matric suction and shear strength with the degree of saturation under various dry densities. This proposed model was also validated against the shear strength results of the clayed-silty sand and Ankara clay recorded in the literature. Comparing the result of the shear strength under the variation of one parameter, this model was much more sensitive regarding parameter m, which was related to the material characteristic. The comparison between the predicted values and experimental points was particularly fine and showed the ability of this model to be applied to a wide range of soils.  
shear strength; unsaturated soil; suction; van Genuchten model; degree of saturation
# Design and Signal-Decoding Test Verification of Dual-Channel Round Inductsyn Decoding Circuit
## 4 Experimental and Analysis
### Experimental Results Analysis  
In the Multisim simulation experiment, the excitation signal output from the AD2S1210 chip's _EXC+_ and _EXC-_ pins, as well as the induced signal from the coarse channel _SIN+_ terminal of the round inductsyn, were simulated by setting an external signal source. The output signals of each functional module were then detected. The simulation results of the decoding circuit are in line with the theoretical design results, meeting the design requirements.  
In the physical experiment of the decoding circuit, the excitation signal of the dual-channel round inductsyn is directly generated by the AD2S1210. This excitation signal is input to the round inductsyn through a power amplification circuit. Waveform recordings from an oscilloscope and feedback from the induced-signal output indicate that the excitation signal effectively drives the round inductsyn's operation. The induced signals from the coarse and fine channels, _SIN+_ and _SIN-_, are differentially inputted for testing. Although the original induced-signal amplitudes from the coarse and fine channels differ by a factor of two and exhibit significant noise, comparing the oscilloscope results after signal processing reveals that both channel signals can be amplified to meet the input requirements of the AD2S1210 and filter out noise, retaining a relatively intact sinusoidal characteristic.  
The simulation and physical experiment results demonstrate that the decoding circuit meets the design requirements and can achieve the expected functionality, thereby validating the feasibility of the proposed solution presented in this paper.
## 4 Data Acquisition
### Ultrasonic Dataset  
Since the ultrasonic equipment for inspection and the specimens to be tested with realistic flaws are generally awfully expensive, acquiring the ultrasonic data is a costly process. Additionally, due to the small proportion of defect region in the tested specimen, a large number of defect signals are difficult to collect. This shortcoming can be remedied by generating or simulating signals to develop a deep learning model. Though these methods may be useful for training models, estimation should be conducted on an actual dataset to guarantee the credibility of the results obtained. So, in acquiring data, we scanned the weld back and forth in both directions and repeated twice to obtain more defect data. The original database acquired contains a total of 4400 signals, including 900 defect signals and 3500 non-defect signals.  
However, notice that the allocation of classes within the database is unbalanced, i.e., the non-defect class has significantly more signals than the defect class. The number of non-defect class signals was reduced to that of the defect class to deal with this imbalance by taking random samples from the non-defect class. Meanwhile, the reduced database was divided into training and testing datasets where 90% of each class is reserved for training and the remaining 10% for testing. The reason for this is that the trained model can present good performance on the seen examples while possibly failing to estimate the unseen examples. Hence, common practice is that the data are divided into training and testing datasets to investigate the practical performance of the model. The model is trained on training datasets and its performance is evaluated on testing datasets.  
All the signals were normalized before feeding to the neural network with Equation (8). This normalization was used to obtain all the signals on the same scale, which can avoid activation function saturation [35].  
\[signal\_nor=\frac{|signal|}{\max(|signal|)} \tag{8}\]
# Construction and Evaluation of Pepper Core Collection Based on Phenotypic Traits and SSR Markers
###### Abstract  
To streamline germplasm preservation, enhance resource utilization, and improve breeding efficiency, a core germplasm bank was established using 24 phenotypic traits and DNP markers from 155 pepper resources across various regions. Selection of the optimal core germplasm was based on intra-group retention ratio, overall retention scale, and intra-group stepwise clustering retention. Evaluation of phenotypic trait data for the core germplasm utilized mean, variance, range, and coefficient of variation, with principal component analysis confirming the selection. For molecular evaluation, the core germplasm pre-selection plan was based on SSR clustering, allele retention ratio, and Shannon-Weaver diversity index. This approach resulted in a core germplasm of 41 resources, including 6 var. _fascicultatum_, 7 var. _grossum_, 3 var. _censorme_, 6 var. _conoides_, and 19 var. _longum_, representing the maximum phenotypic retention and genetic diversity of the 155 pepper resources. Additionally, a core germplasm of 32 resources was generated based on SSR markers, retaining all 54 polymorphic loci. By integrating phenotypic and molecular core collections, a combined core collection of 57 varieties was developed. This collection achieved a 92.55% phenotype retention ratio and a 100% polymorphism site retention ratio. With a 90% compression ratio, it encapsulates the broad genetic diversity of the original germplasm, serving as a comprehensive resource for further research and breeding applications.  
Capsicum L.; germplasm resources; phenotypic traits; SSR molecular markers; core collection
# Comparison of SNAP-Derived Sentinel-2A L2A Product to ESA Product over Europe
## 4 Results
### Comparison of Vegetation Indices and Biophysical Parameters  
Figure 8 shows scatter plots between VIs (NDVI, NDWI and EVI) as well as vegetation parameters computed from ESA-L2A and S2C-L2A (analysed in Section 4.3) over each site. In addition to \(R^{2}\), RMSE and bias, the RMSE relative to the average parameter value for the site is presented to better analyse the magnitude of generated error on the estimated vegetation parameters.  
All three VIs indicated a similar range of \(\sim\)0.8 units for each site. In contrast to reflectance, the VIs showed strong linear relationships (\(R^{2}\geq 0.99\)) between products with low RMSE for NDWI (\(\approx\)0) and NDVI (\(\approx\)0.01), and slightly higher for EVI (between 0.03 to 0.06). Differences were proportional to VI indicating a multiplicative error source for differences. These may due to numerical differences in algorithm implementations since signal quantization was observed for NDVI \(>\) 0.9 and EVI \(>\) 0.9.  
Figure 7: (**a**) Spatial difference maps between S2C-L2A and ESA-L2A BOA reflectance for Barrax, La Crau and Harth; (**b**) the difference between S2C-L2A and ESA-L2A BOA reflectance as a function of the topographic slope for for Barrax, La Crau and Harth.  
Figure 8: Density scatter plot of vegetation indices and vegetation bio-physical parameters derived from ESA-12A compared to those derived from S2C-12A for Barrax, La Crau and Harth sites.  
The scatter of ESA-L2A versus S2C-L2A vegetation parameters around the 1:1 line is generally proportional to the parameter. In contrast to LAIxCw, where the difference is very low (\(R^{2}\) = 0.99; RMSE = 0; bias = 0), substantial differences are obtained for other vegetation parameters, with the relative RMSE ranging from 10% to 14% for LAI, 10% to 36% for LAlxCw, 5% to 9% for fAPAR and 7% to 15% for fcover. The bias, relative to the average parameter value for the site, ranged between 0% and \(-\)6%. Regressions of differences versus local topographic slope indicated \(R^{2}\) (linear slopes) reaches 0.14 (0.007 units/degree) for LAI, 0.09 (0.308 units/degree) for LAI Cab, 0.45 (0.002 units/degree) for fAPAR and 0.48 (0.003 units/degree) for fcover.