esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data and Data Processing
#### 2.2.2 MODIS-GPP Data (GPPm)  
The MODIS-GPP data used in this study were available from the Oak Ridge National Laboratory Distributed Active Archive Center [39]. Two different versions of the GPPm data were available: the standard product NASA MOD17 Collection 5 (Coll. 5) (available from 2000 to present) and Numerical Terradynamics Simulation Group (NTSG) MOD17 Collection 5.1 (Coll. 5.1) (available from 2000 to 2006). The NTSG recommends the use of NTSG MOD17 Coll. 5.1 data when it is available [40]. Hence, for all six sites and all years, the MOD17 Coll. 5.1 GPP data was used.  
The data consisted of a 49 pixel subset of the corresponding eight-day GPPm composite centred over each flux tower location. Pixel 25 of the subset corresponded with the closest pixel to the tower, but in many cases it was not available for the eight-day period due to quality issues (e.g., cloud contamination, dead detectors, geometry issues, _etc._) limiting the amount of GPPm data available for the study. Thus, we decided to use the average GPPm value of the 49 pixel subset, as long as the pixel met the quality requirements and corresponded to the same land cover as the tower site; otherwise they were rejected on a pixel by pixel basis and afterward the GPP values from the remaining pixels were averaged. A t-test showed that there were no significant differences (\(p<0.05\)) between the average of the 49 pixel subset and the GPP value of pixel 25 (for each site and year).
## 4 GA Application in Goods Distribution Systems
### System Setting  
In order to apply a GA to the considered class of systems, a mapping between the logistic and GA entities needs to be established. It is assumed that:  
* The vector of RSLs reflects an individual in the population. Consequently, the stock level at a controlled node will correspond to the chromosomes in a given individual.
* Since the domain of inventory stock levels is continuous (any value from the assumed range can form an individual), a CGA is employed instead of a binary-form GA.
* The fitness function value is obtained via numerical simulations of the system behavior, which is in response to the control inputs established either according to (12) (classical, distributed policy) or (14) (networked, centralized policy).
## 3 Performance Analysis of the PTSR in the Underdamped State
### The Simulation Results  
In order to show more intuitively that the PTSR in the underdamped state has better output, a periodic signal with noise is input into the PTSR system under underdamped and overdamped conditions, respectively (\(A=0.5\), \(f_{d}=5\)). Sampling frequency \(f_{s}\) and noise intensity D are set to 2000 Hz and 30, respectively, Figure 3 is the original signal.  
Figure 3: Simulation signal (**a**) time domain curve (**b**) frequency domain curve.  
Figure 2: The SNR curve of the output signal.  
It can be seen from Figure 3 that both the time domain and frequency domain diagrams are disorderly, the periodicity and the characteristic frequency bar cannot be recognized. Meanwhile, the original SNR is calculated to be \(-32.4038\) dB.  
The signals processed by both methods are displayed in Figure 4. By contrasting with Figure 4a,b, it can be seen that both signals show a trend of resuming periodicity, but the signal in Figure 4b is obviously clearer. At the same time, by contrasting Figure 4c,d, it can be seen that the amplitude of the characteristic frequency is extracted, but the characteristic frequency in Figure 4d is obviously more prominent, and the SNR of the PTSR and the PTSR in the underdamped state are calculated to be \(-18.8225\) dB and \(-13.8189\) dB, respectively. Thus, it can be concluded that the PTSR method in the underdamped state has a stronger degree of noise filtering and better output characteristics.
# Target Speaker Extraction by Fusing Voiceprint Features
## 3 Experiments and Results
### Experiment Settings
#### 3.1.1 Configurations of Speech Separation Models  
As illustrated in Section 2.1.3, three speech separation models were selected for performance evaluation, i.e., DPCL, PITNet, and VoiceFilter. Table 1 shows the configurations of these models used in the experiments. The LSTM networks in DPCL and PITNet were used to generate speaker embeddings and the Adam optimizer was used to optimize the models.
# Active Learning for Biomedical Article Classification with Bag of Words and _FastText_ Embeddings
## 3 Results
### Active Learning Predictive Performance: Aggregated
A simple way to verify this is to compare the PR AUC values achieved by these strategies to the common baseline of random sampling seperately for different groups of datasets:  
* datasets with below-median size;
* datasets with above-median size;
* datasets with above-median relevant class percentage;
* datasets with below-median relevant class percentage. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**(a) size**} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline random–uncertainty & \(-0.47\) & \(-0.59\) & \(-0.60\) & \(-0.58\) \\ random–diversity & \(-0.47\) & \(-0.59\) & \(-0.60\) & \(-0.65\) \\ random–minority & \(-0.46\) & \(-0.52\) & \(-0.57\) & \(-0.65\) \\ random–threshold & \(-0.49\) & \(-0.64\) & \(-0.68\) & \(-0.65\) \\ random–random & \(-0.46\) & \(-0.60\) & \(-0.61\) & \(-0.62\) \\ cluster–uncertainty & \(-0.45\) & \(-0.59\) & \(-0.58\) & \(-0.61\) \\ cluster–diversity & \(-0.48\) & \(-0.59\) & \(-0.62\) & \(-0.66\) \\ cluster–minority & \(-0.49\) & \(-0.52\) & \(-0.58\) & \(-0.60\) \\ cluster–threshold & \(-0.40\) & \(-0.63\) & \(-0.66\) & \(-0.60\) \\ cluster–random & \(-0.50\) & \(-0.60\) & \(-0.59\) & \(-0.64\) \\ \hline \multicolumn{2}{c}{**(b) relevant \%**} \\ \hline  & SVM-BOW & SVM-FT & RF-BOW & RF-FT \\ \hline random–uncertainty & 0.74 & 0.78 & 0.71 & 0.78 \\ random–diversity & 0.77 & 0.82 & 0.75 & 0.83 \\ random–minority & 0.77 & 0.75 & 0.71 & 0.77 \\ random–threshold & 0.73 & 0.79 & 0.74 & 0.78 \\ random–random & 0.71 & 0.77 & 0.80 & 0.80 \\ cluster–uncertainty & 0.74 & 0.78 & 0.69 & 0.84 \\ cluster–diversity & 0.75 & 0.84 & 0.75 & 0.87 \\ cluster–minority & 0.77 & 0.75 & 0.69 & 0.80 \\ cluster–threshold & 0.60 & 0.76 & 0.75 & 0.77 \\ cluster–random & 0.74 & 0.77 & 0.76 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Rank correlations of PR AUC values at 33% of labeled data to data size and relevant class percentage. The results of such comparisons are presented in Table 6 with subtables corresponding to these four dataset groups showing the difference of PR AUC at the active learning stage of 33% labeled data between each non-random query selection strategy and random sampling (positive values indicate performance better than that of random sampling and negative values indicate performance worse than that of random sampling). It can be easily seen that the advantages of uncertainty sampling, minority sampling, and threshold sampling over random sampling are more prominent for bigger and more imbalanced datasets, i.e., those which were found harder to classify. Diversity sampling remains the worst query selection strategy regardless of dataset properties.
## 4 Experimental Results
### Humeral Head Shape Analysis Using Statistical Shape Model  
To evaluate the interindividual humeral head shape quantitatively, the measurements were done for 25 statistical shape models constructed by changing the standard deviation along the first PC from \(-3\sigma_{1}\) to \(+3\sigma_{1}\) at intervals of 0.25\(\sigma_{1}\), where \(\sigma_{1}\) is the Eigen value of the first PC. The measurement was performed on the shape of each subject. Table 1 shows the average and variance of the measured values between different points. The results showed that the first PC had a large variance in the distance between P\({}_{1}\)-P\({}_{2}\) and P\({}_{3}\)-P\({}_{4}\), i.e., the interindividual variation in the anterior-posterior to lateral-medial (AP-LM) direction was large. In particular, it was found that the variance of the distance P\({}_{1}\)-O was large. The results showed that the variance in the distance P\({}_{1}\)-O was large in both of the measurements, i.e., measurement from the SSM and measurement from the subject's shape. Thus, it confirmed that the SSM by the proposed method can grasp the tendency of the subject population. Table 1 also shows the measured values for original data. Figure 9 and Table 2 show the correlation coefficients between the thicknesses and the distances between different points, i.e., P\({}_{1}\)-O, P\({}_{2}\)-O, P\({}_{3}\)-O, and P\({}_{4}\)-O, considering both the measurements. These results showed that the thicknesses and distances of P\({}_{1}\)-O and P\({}_{3}\)-O had a strong positive correlation (\(>\)0.8) when using the proposed method, by the SSM. In other words, it could be considered that the prosthesis should be deformed by considering the same tendency at each size, because the head is not just a scale-up, but rather the extension of anterior and lateral is dominant.  
Figure 8: Schematic diagram of SSM validation.
## 2 Data Acquisition  
A commercial FSO communications system located on the Hellenic Naval Academy's building roof and on Pisitalia's island lighthouse establishes an optical link with a length of 2958 m in a maritime environment [15]. The optical path has an average height above sea level of 35 m and almost 95% of it is above water. The operating FSO system is an MRV/TS5000/155, employing an Intensity Modulation/Direct Detection (IM/DD) communication scheme and yielding a maximum data rate of 155 Mbps (Figure 1). The selected metric for the FSO link performance evaluation was the received signal strength indicator (RSSI), a parameter that has been extensively used elsewhere [16; 17; 18; 19]. The system has been set to obtain a measurement of the received signal strength indicator (RSSI) every minute, during each day, and then store these values in a connected PC for further analysis [20].  
The data collection period of the RSSI parameter spanned over a year, from the 30 November 2019 to the 27 October 2020, to include 144,802 data points in total. At the same time, several macroscopic meteorological parameters were also measured from a standalone weather station located close to the FSO system so that their fluctuations can be monitored and estimate their influence on RSSI. This dataset of the RSSI measurements is used to apply the PDF fits comparison. The descriptive statistics of the dataset are shown in Table 1.
# LAMDA Controller Applied to the Trajectory Tracking of an Aerial Manipulator
## 4 Experimental Results
### Experiment 4: LAMDA Controller  
The results of the new control strategy applied for the trajectory tracking of an aerial manipulator robot are shown in Figures 14-17. Figure 14 shows the trajectory carried out by the end-effector of the aerial manipulator robot, corroborating that it follows the desired trajectory with an expected margin of error.  
Figure 12: Trajectory tracking: SMC controller.  
Figure 13: Control errors: SMC controller.  
Figure 14: Trajectory tracking: LAMDA controller.  
Figure 15: Control errors: LAMDA controller.  
Figure 15 shows the end-effector position errors, with a maximum error of approximately 0.4 m. This corroborates the good performance of the new proposed LAMDA control system that has not been used yet for aerial manipulators robots. This type of control even responds favorably to the presence of external disturbances such as wind velocity of approximately 8.3 km/h.  
The control signals are shown in Figure 16, where we can observe that there is a good velocity tracking by the aerial manipulator robot, verifying that its control signals are smooth and slightly oscillatory, which allows its actuators to not receive abrupt signals that shorten its useful life.  
The velocities of the joints of the robotic arm \(\hat{q}_{1}\), \(\hat{q}_{2}\)\(y\)\(\hat{q}_{3}\) are shown in Figure 17. We observed that joint 3 generates an angular velocity greater than the rest of joints of robotic  
Figure 16: Control signals: LAMDA controller.  
Figure 17: Joint velocities: LAMDA controller.  
arm since it is easier for the aerial manipulator robot to move this joint of the robotic arm to achieve the control objective. Therefore, minor movements are generated in joint 1 and 2 as verified in this figure.  
Taking into account the experiments carried out, it is possible to confirm the good behavior of the LAMDA controller, exhibiting a better performance compared to the Kinematic controller and a similar performance in comparison to the Inverse Dynamics and Sliding Mode control strategies for trajectory tracking.  
The obtained Integral square error (ISE) factor is shown in Figure 18, verifying that the new LAMDA control proposal can be applied to aerial manipulators robots, obtaining a good performance that can be comparable to control techniques with dynamic compensation for these systems.
## 2 Theoretical Background
### Linear Viscoelastic Model  
For linear viscoelastic materials, including asphalt mastic, the stress-strain constitutive relation is expressed by convolution integrals. In the case of stress response at constant strain, the convolution relations is explained in Equation (1) for one-dimensional problems:  
\[\sigma(t)=E_{\infty}\varepsilon+\int_{0}^{t}\Delta E\Big{(}\varphi^{t}- \varphi^{\tau}\Big{)}\frac{d(\varepsilon)}{d\tau}d\tau \tag{1}\]  
where the \(E_{\infty}\) is the instantaneous elastic modulus, \(\varphi^{\tau}\) is the reduced time and \(\Delta E\) is the transient modulus. It is given by Equation (2):  
\[\Delta E(\varphi)=\sum_{n=1}^{N}E_{n}\exp(-\rho_{n}\varphi) \tag{2}\]  
where \(E_{n}\) is the n-th coefficient of the Prony series and \(\rho_{n}\) is the n-th relaxation time.  
For 2D and 3D problems, Equation (1) can be decomposed into deviatoric and volumetric components in Equation (3) to Equation (5):  
\[S_{ij}^{t}=2G_{0}\varrho_{ij}^{t}+2\int_{0}^{t}\Delta G\Big{(}\varphi^{t}- \varphi^{\tau}\Big{)}\frac{d\Big{(}\varepsilon_{ij}^{\tau}\Big{)}}{d\tau}d\tau \tag{3}\]  
\[\sigma_{kk}^{t}=3K_{0}\varepsilon_{kk}^{t}+3\int_{0}^{t}\Delta K\Big{(}\varphi ^{t}-\varphi^{\tau}\Big{)}\frac{d\Big{(}\varepsilon_{kk}^{\tau}\Big{)}}{d\tau}d\tau \tag{4}\]  
\[\sigma_{ij}^{t}=S_{ij}^{t}+\frac{1}{3}\sigma_{kk}^{t}\delta_{ij} \tag{5}\]  
where \(S_{ij}\) and \(\sigma_{ij}\) are the deviatoric stress tensor and volumetric stress tensor, respectively; \(\sigma_{kk}^{t}\), \(G_{0}\), and \(K_{0}\) are the total stress tensor, the instantaneous effective elastic shear modulus, and bulk modulus, respectively; \(\Delta G\) and \(\Delta K\) are the transient shear modulus and bulk modulus, respectively. \(\delta_{ij}\) is the Kronecker delta.  
In most cases, the asphalt mastic Poison's ratio \(\mu\) is assumed to be time independent [32; 33; 34]. So all the material properties in Equations (3) and (4) can be determined from the data of uniaxial tensile tests in Equation (6):  
\[\Delta G_{0}=\frac{E_{0}}{2(1+\mu)},\ K_{0}=\frac{E_{0}}{3(1-2\mu)}\,\ \Delta G(t)=\frac{\Delta E(t)}{2(1+\mu)}\,\ \Delta K(t)=\frac{ \Delta E(t)}{3(1-2\mu)} \tag{6}\]
# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
## 4 Conclusions  
In this paper, a detection and recognition system for concealed objects based on the MIMO-SAR radar is proposed. The contributions made in this paper are as follows:  
1. By using the MIMO-SAR radar, the aperture of the radar antenna is expanded to 90 mm in the \(X\)-axis direction. Eight virtual channels are established in the \(Y\)-direction, which widens the length of the longitudinal direction aperture in each transverse scanning can be equivalent to 4\(\lambda\). Image resolution can reach 1.90 mm in \(X\)-direction and 1.73 mm in \(Y\)-direction, when the object is 90 mm away from the radar. The MIMO-SAR imaging system can effectively reduce the scanning time cost, the system economic cost and improve the image resolution.
2. Gamma transform with a coefficient of 2.4 and linear stretch processing are innovatively carried out for the SAR images to remove the noise caused by distance error and improve visual recognition, which lays a good foundation for the subsequent supervised learning network.
3. The lightweight convolutional neural network is small in size and occupies less resources, but the prediction accuracy is not high. After the optimization of the SE and SK attention mechanism, the prediction accuracy is improved with the increase of a small part of the resource occupancy rate. Combined with the prediction accuracy; computational complexity: Madd, FLOPs; memory occupation rate: MemR + W, parameters. GhostNet_SEResNet56 is the optimal prediction algorithm for SAR data set, which prediction accuracy of the validation set is 98.18%; computational complexity: 134.55 MMadd and 67.81 MFLOPs; memory occupation rate: 18.98 MB (MemR + W) and 0.45 M (parameters).  
Conceptualization, J.L.; methodology, K.Z.; software, Z.S. and H.W.; validation, Z.S. and H.W.; formal analysis, K.Z., W.H.; investigation, Q.W., Z.S.; resources, J.L.; data curation, Q.W., W.H.; writing--original draft preparation, K.Z.; writing--review and editing, J.L.; supervision, W.H.; project administration, Q.W.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (11527801, 20222201 and 61305026) and Beijing Municipal Commission of Education (KM200710005009, PXM2009.014204.09.000154 and KM201310005006).
## 3 Results and Discussion
### Experiment 6: The Effect of the Number of Classes  
In practice, the classification of all crop types is time and cost consuming. Additionally, when different scenarios for image classification occur in terms of the number of classes, different classification results can be obtained. Also, to separate similar crops such as wheat and barley, it is necessary to use the images of the high crop greenness. In other words, if crops are physically distinct in terms of phenology and spectral similarity, they can be classified at earlier growth stages.  
In research on the classification of agricultural crops, due to the similarity of wheat and barley in most cases, these two classes are merged into one class [9; 30]. In this experiment, the RF classification is performed on classification case 2 by using the appropriate models obtained through experiment 5 (i.e., Comb. 3, Comb. 6 and Comb. 7 of Table 9). The comparative results are given in Table 10. The achieved results demonstrated that, compared to the classification case 1, the classification accuracy increased up to 7% in case 2 in terms of OA (i.e., when two classes of wheat and barley are merged into one class). As a result, when the RF classifier is needed to classify both wheat and barley, Comb. 7 of Table 9 is an appropriate model which utilizes four images from the first stage of growing season with one weak  
Figure 11: Prioritized variable importance of the produced features.  
temporal resolution. In this regard, if wheat and barley are considered as a single class versus other classes, with the use of images associated with 162 and 163 paths, both crops can be classified in March (at the beginning of the growth stage) with an overall accuracy of 97.1% and kappa coefficient of 93.5% (Table 10).
## 3 Results and Discussion
### Quantitative Analysis of Microstructure  
Figure 4 shows that the alterations in average particle size, aspect ratio, and particle formation per unit area (density) correspond to the various B\({}_{4}\)C additions. The results match well with the microstructure observation in Figure 3, in which with the addition of 2.5 wt% B\({}_{4}\)C, the average particle size decreased by 13% from 47 to 41\(\mu\)m, the aspect ratio decreased by 3.7% from 1.34 to 1.29, and the density also decreased by 16% from 135 to 113 particle/mm\({}^{2}\). With the addition of 5 wt% B\({}_{4}\)C to Al-20%Mg\({}_{2}\)Si composite, the particles size, aspect ratio, and density of primary Mg\({}_{2}\)Si further decreased to 33 \(\mu\)m, 1.27 and 94 particle/mm\({}^{2}\). However, as observed in Figure 3, with a further addition of B\({}_{4}\)C to 10 wt%, the mean size and aspect ratio of primary Mg\({}_{2}\)Si increased to 38 \(\mu\)m and 1.28 respectively. Nevertheless, the density decreased to 63 particle/mm\({}^{2}\) (Figure 4). Therefore, according to the results in Figures 3 and 4, it can be observed that Al-20%Mg\({}_{2}\)Si-5%B\({}_{4}\)C hybrid composite illustrates the best condition in terms of primary Mg\({}_{2}\)Si refinement and B\({}_{4}\)C particles distribution.
# A DFTB-Based Molecular Dynamics Investigation of an Explicitly Solvated Anatase Nanoparticle
## 3 Results  
### Free Energy of Interaction  
To assess the performance of the SCC DFTB-MD methodology for this system, we performed several analyses. The authors' interest in TiO2 stems mainly from the material's application as a photoelectrochemical catalyst in the water-splitting reaction. Thus, a matter of key interest was whether the dissociative adsorption of water molecules on the nanoparticle surface is observed in the system--both experimental and theoretical investigations report mixed dissociative and molecular adsorption at TiO2 surfaces [17]. We identified adsorbed molecules using the free energy of interaction, as defined by Equation (1), as follows:  
\[\Delta\text{E}=\text{E}_{\text{NP+H2O}}-\text{E}_{\text{NP}}-\text{E}_{\text{ H2O}} \tag{1}\]  
Dissociatively adsorbed molecules would have a larger energy of interaction than molecularly adsorbed molecules, which would, in turn, have a larger energy of interaction than non-adsorbed molecules. The reference energies for the isolated nanoparticle and water molecule were chosen as those corresponding to the relaxed geometries used to initialise the simulation. Figure 2 shows a scatter plot of the energy of interaction for water molecules in a single, randomly chosen frame of the trajectory.  
As can be observed, most the interactions are small and favourable, with a few larger, favourable (negative) interactions and a single large, unfavourable (positive) interaction. Selected interactions are summarised in Table 2, with accompanying renderings in Figure 3. As expected, the largest energies of interaction correspond to dissociated molecules. It is important to note that DFTB does not allow for electron transfer between atoms; thus, the interactions do not involve the formation of charged species, as would be expected in reality.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**System** & **N\({}_{\text{TiO2}}\)** & **N\({}_{\text{H2O}}\)** & \(a\) **(Å)** & \(b\) **(Å)** & \(c\) **(Å)** \\ \hline Nanoparticle & 163 & 1256 & 30.21 & 30.21 & 49.97 \\ Anatase (101) & 28 & 65 & 11.41 & 10.47 & 39.26 \\ Rutile (110) & 32 & 45 & 9.31 & 11.04 & 25.04 \\ Bulk Water & 0 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation cell parameters for the various systems.