esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Continuous Wavelet Transform and Cross-Wavelet Transform  
After the filtering process, we eliminated the first and the last seconds of each time series to avoid edge effects. Then, 21 periods of artifact-free 90 s intervals were intercepted for each CL and PD group. We again applied the CWT to all these periods.  
Figure 1 shows two representative continuous wavelet spectra from the Fp1 channel. Figure 1A exemplifies a CL man, and Figure 1B a man diagnosed with PD. On the spectra, the original time series is shown at the upper panel. The central panel shows the power wavelet spectrum (PWS), where the cone of influence is represented by the black curved line. The cone of influence limits the sections on the PWS where the edge effects can be ignored. The color bar on the top represents the normalized continuous wavelet power, where red represents the highest power and blue represents the lowest power. The right panel shows the global wavelet spectrum (GWS), where the continuous line (global wavelet  
curve) represents the normalized power in each period value and the dotted line denotes the 95% confidence level for the power. The confidence level in wavelet analysis is related to the red noise power level at the 95% confidence interval, which refers to the range of confidence about a given value [27]. Figure 1C corresponds to the zoom of the GWS in frequency, highlighting the power of the brainwaves from the CL man and the man diagnosed with PD. The brainwave frequency ranges used in this research were as follows (in Hz): \(\delta\) (2-3.9), \(\theta\) (4-7.9), \(\alpha\) (8-12.9), \(\beta\) (13-29.9) and \(\gamma\) (30-50).  
To perform the CWA, we used the spectral information obtained by the CWT and applied multiple XWT to pairs within each group (PD < 60 and >60; CL < 60 and >60) to eliminate individual features. The pairs were performed while taking into consideration a symmetric square matrix per group, such as was previously described [23]. In each group, the possible pairs were six for CL < 60, six for CD > 60, six for PD < 60, and three for PD > 60. For each pair, 21 XWT were applied, which corresponded to each one of the 21 electroencephalographic channels used in this research. Thus, after executing the XWT within the groups, 126 cross wavelet spectra (XWS) were obtained for CL < 60, 126 for CL > 60, 126 for PD < 60, and 63 for PD > 60.
## 4 Results and Discussion
### Performance of the Ensemble Precipitation Forecasts
To further assess the forecast's ability to discriminate heavy rain events, the ROC curves for 50 mm/day rainfall thresholds, at 1, 3, 5, and 7 lead days for raw and postprocessed ECMWF (solid line) and NCEP (dash line) are shown in Figure 6. The figure shows forecast discrimination of ECMWF is stronger than that of NCEP, where solid lines are closer to left corner of the plot than dash lines. For raw- and GPP-EPFs (Figure 6a,b), the ROC curves at 1, 3, 5, and 7 lead days increasingly move away from the top left corner of the plot, indicating that forecasts for shorter lead times have higher discriminative ability than those for longer lead times. This is not the case for BMA-EPFs (Figure 6c) with ROC curves for 7 lead days closer to the top left corner. Compared to the raw EPFs, GPP-EPFs demonstrate better forecasting ability in discriminating heavy rain events, while the opposite is true for BMA-EPFs. Figure 4: Line chart of evaluation indexes for ensemble precipitation forecasts by raw and postprocessed ECMWF (**a1**,**a2**) and NCEP (**b1**,**b2**).
## 4 Simulations
Note also that in Table 1, motion indices are referred to the steps of the first simulated scenario (Procedure 1). In order to guarantee proper safety bounds, the manipulator joint velocities and the end-effector linear velocity are limited to \(40\,\mathrm{deg/s}\) and \(200\,\mathrm{mm/s}\), respectively. Therefore, under the further following assumptions:  
* \(T_{j}\) is the time required to move the AMR from the initial pallet to the storage pallet;
* \(T_{s}=1\,\mathrm{s}\) is the time required to move the AMR from the current to the next column of the grid,  
and the overall time needed to complete the transport of all the 21 boxes can be fairly approximated as follows:  
\[{}^{s}T_{\mathrm{tot}}=22\,T_{j}+7(^{s}T_{l}+^{s}T_{u}+^{s}T_{l1}+^{s}T_{u1})+6 \,T_{s}\approx 464+22\,T_{j} \tag{1}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Loading** & **Unloading** \\ \hline  & Motion & Time (s) & Time (s) \\ \hline  & 1 & 5.75 & 5.55 \\ Box & 2 & 1.04 & 1.04 \\
1 & 3 & 3.10 & 3.05 \\  & 4 & 1.55 & 1.55 \\ \hline  & Partial & \({}^{s}T_{l1}=11.44\,\mathrm{s}\) & \({}^{s}T_{u1}=11.19\,\mathrm{s}\) \\ \hline  & 1 & 5.25 & 3.55 \\ Box & 2 & 1.04 & 1.04 \\
2 & 3 & 3.10 & 3.05 \\  & 4 & 1.55 & 1.6 \\ \hline  & Total & \({}^{s}T_{l}=22.38\,\mathrm{s}\) & \({}^{s}T_{u}=20.43\,\mathrm{s}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulated elapsed time for loading/unloading two aligned boxes. where the term \(22\,T_{j}\) considers \(11\) round-trip journeys, the terms \(7^{\,s}T_{l}\) and \(7^{\,s}T_{u}\) include the loading/unloading of the aligned boxes in the first and second grid columns, while \(7^{\,s}T_{l1}\) and \(7^{\,s}T_{u1}\) are the times taken to handle the remaining single box of the third grid column. It is worth noting that the additional time required to re-execute the procedure in the case of a collision between the tool and the boxes due to the inaccurate detection of the gap, as described in Section 6, is not considered here, because it is expected that the probability of this type of events will be reduced and ideally made negligible for the future industrial release of the system. The second manipulation scheme (Procedure 2) involves the usage of a vacuum gripper mounted on the robot end-effector instead of the shovel-shaped tool. The main difference with the previous approach is that the robot can execute a standard pick-and-place operation with approaching movements at different heights, so that the lifting device is no more required. Moreover, the detection of the gap between boxes is no longer required and the vision system can identify the picking frame exploiting a single camera acquisition, so that the first robot motion is faster with respect to the former manipulation approach. The algorithm of Procedure 2 describes the sequence of robot motions required to pick a single box from the pallet and place it on the AMR.
# De-Noising Process in Room Impulse Response with Generalized Spectral Subtraction
## 2 Principle of Spectral Subtraction
### Generalized Spectral Subtraction  
The generalized spectral subtraction algorithm (GBSS) does not require access to further information and affords extraordinary ability in background noise reduction with little effect on speech intelligibility [28; 34]. It is the primary motivation for investigating the algorithm to de-noise the RIR to improve the dynamic range. The algorithm consists of two additional parameters that offer a significant amount of flexibility in the generalized spectral subtraction to reduce the remnant noise, the over-subtraction factor and the noise spectral floor. The proposed method is given as follow:  
\[\left|\hat{X}(\omega)\right|^{2}\ =\ \begin{cases}\left|Y(\omega)\right|^{2}- \alpha\big{|}\hat{D}(\omega)\big{|}^{2},&if\ |Y(\omega)|^{2}>(\alpha+\beta)\big{|}\hat{D}(\omega)\big{|}^{2}\\ \beta\big{|}\hat{D}(\omega)\big{|}^{2}&else\end{cases}, \tag{4}\]  
where \(\alpha\geq 1\)_and_\(0<\beta\leq 1\).  
In order to reduce distortion of the signal by the GBSS algorithm, experimental results showed that the \(\alpha\) should vary from frame to frame. The formula applied to de-noising the RIR is as follows,  
\[\alpha\ =\ \alpha_{0}+(snr-snr_{min})((\alpha_{min}-\alpha_{0})/20)\ \ \ \mathit{snt}_{min}\leq snr\leq snr_{max}\ . \tag{5}\]  
Here, \(\alpha_{0}\) is the desired value at the \(snr=0\), and the snr is a short time posterior calculated in each frame which is based on the ratio of the noisy RIR to estimate the noise power,  
\[snr\ =\ 10*log10(\sum_{\omega\ =\ 1}^{N}\left|Y(\omega)\right|^{2}/\sum_{ \omega\ =\ 1}^{N}\big{|}\hat{D}(\omega)\big{|}^{2}). \tag{6}\]  
Here, \(snr_{max}\ =\ 20\), \(snr_{min}\ =\ -5\), and \(\alpha_{min}\ =\ 1\). \(N\) is the number of frames in the signal. The noise floor factor \(\beta\) depends on the snr effects in converting the narrow band noise to wideband noise to reduce the perceived musical noise and the residual noise levels. Although a large \(\beta\) gives less musical noise, it may increase the added broadband noise levels that do not belong to the original signal. The over-subtraction factor \(\alpha\) is applied to reduce the overall level of the residual noise, including the broadband noise and the musical noise, decided by the \(\alpha_{0}\). Studies have shown that the noise levels can be remarkably removed from the signal with a higher \(\alpha_{0}\). At the same time, the signal may be severely distorted to the point causing the speech to suffer from intelligibility damage. That could explain why special attention should be paid to the spectral subtraction rule applied in de-noising the RIRs, because the de-noising effects are essentially controlled by parameters \(\beta\) and \(\alpha_{0}\)[28].
## 2 Materials and Methods
### Evaluation of Size-Thresholding  
The classification result of the classifier with subset 1 was edited in FIJI based on the results of the Analyze Particles function. The mean size of single worms was determined by visual inspection, measuring the size of 20 worms with a result of 6283 (\(\pm\)1243) pixels. Various size-thresholds (3000, 3500, 4000, 4500, and 5000 pixels) were validated to differentiate between worm and non-worm areas by comparison of size-thresholded machine learning results to those of manual segmentation. Calculations were performed as described in Evaluation of Attributes on test sets.
## 2 Kinematic Analysis of a Dual-Parallelogram Passive Rocking Vibration Isolator  
### Description of Mechanism  
Figure 1 shows the mechanism of a dual-parallelogram passive rocking vibration isolator. In Figure 1, two rocking rods (\(A\) and \(B\)) in a pair of central symmetric parallelograms are fixed on the basic platform at an angle of 45 degrees to coordinate axis \(OX\). One supporting rod is fixed on the rocking rod at one end of each parallelogram, and at the other end the supporting rod connects with the vibration isolation platform through revolute joint \(C\). A spring-damper rod is arranged to connect the two supporting rods. At the initial position, the spring-damper rod is parallel to the vibration isolation platform, where load m is fixed.  
Figure 1: Mechanistic diagram of the vibration isolator.  
The absolute coordinate system of the basic platform (i.e., where it rocks) is \(o_{1}-xyz\). \(o_{2}\) is the dynamic coordinate system established at the rotating center of the load mass. When the basic platform is fixed, the rocking system holds one rotational degree of freedom around \(o_{2}\), and the internal rods of the system move as angular \(\phi\) changes. The rocking rods, \(l_{1},l_{2},l_{3}\), are the rods in the parallelogram, the mass center of which hinges on the respective shape. To simplify the calculation, \(l_{2}\) and \(l_{3}\) have an integrated body that holds one associated center of mass. The length of the vibration isolation platform is \(l_{4}\). \(d_{1}\) is the distance from \(o_{2}\) to the vibration isolation platform, and \(l_{5}\) is the initial length of the spring. At the initial position, no tension or pressure is exerted on the spring. \(d_{3}\) is the distance from the ends of the spring-damper rods to points (\(C_{1}\) and \(C_{2}\)) on the ipsilateral vibration isolation platform. According to the geometric relationship, the relationship of rod lengths with position can be determined as:  
\[\left\{\begin{array}{l}d_{1}=\frac{l_{4}}{2}\\ d_{3}=\frac{\sqrt{2}}{2}(l_{5}-l_{4})\\ L=\frac{\sqrt{2}}{2}l_{3}+2l_{4}-l_{2}-\sqrt{2}l_{1}\end{array}\right. \tag{1}\]  
where \(L\) refers to the distance from the double parallel quadrilateral mechanisms (DPQM) to the fixed points \(A_{4}\) and \(B_{4}\).
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 3 The Proposed Method
### Fine Estimation  
The initial DOA estimation suffers from quantization-induced estimation accuracy degradation due to the grid mismatch effect. To address this limitation, a refined DOA estimation method is developed in the following section.  
Proceeding from (8), the noise effect can be eliminated by left-multiplying the expression with a selection matrix \(\mathbf{F}\), that is,  
\[\mathbf{r}=\mathbf{F}\mathbf{\bar{y}}=\mathbf{F}\mathbf{\bar{A}}\mathbf{p}+ \mathbf{F}\mathbf{\bar{e}}, \tag{12}\]where  
\[\mathbf{F}=\left[\begin{array}{ccc}\mathbf{I}_{\frac{L-1}{2}}&\mathbf{0}_{\frac{ L-1}{2}\times(\frac{L_{2}}{2}+1)+1}\\ \mathbf{0}_{\frac{L-1}{2}\times(\frac{L-1}{2}+1)}&\mathbf{I}_{\frac{L-1}{2}} \end{array}\right]. \tag{13}\]  
Here, \(\mathbf{I}_{\frac{L-1}{2}}\) denotes the \(\frac{L-1}{2}\times\frac{L-1}{2}\) identity matrix.  
Assume there are \(K\) signals. The \(k\)-th signal can be obtained by isolating it from the contributions of multiple sources, that is,  
\[\mathbf{r}_{k}=\mathbf{r}-\sum_{\genfrac{}{}{0.0 pt}{}{1}{i=k}{j=k}}^{K}\mathbf{ F}\mathbf{a}(\hat{\theta}_{l})\hat{p}_{l}, \tag{14}\]  
where \(\{\hat{p}_{l},\hat{\theta}_{l}\}_{l=1,j\
eq k}^{K}\) are inherited from the estimates obtained in the preceding iterations. The current estimate \(\hat{p}_{k}\) is then derived through a least squares solution, given by  
\[\hat{p}_{k}=((\mathbf{F}\mathbf{a}(\hat{\theta}_{k}))^{H}(\mathbf{F}\mathbf{ a}(\hat{\theta}_{k})))^{-1}(\mathbf{F}\mathbf{a}(\hat{\theta}_{k}))^{H} \mathbf{r}_{k}. \tag{15}\]  
The orthogonal projection onto \(\mathbf{F}\mathbf{a}\) is given by  
\[P_{\mathbf{F}\mathbf{a}(\theta)}^{\perp}=\mathbf{I}-(\mathbf{F}\mathbf{a}( \theta))((\mathbf{F}\mathbf{a}(\theta))^{H}(\mathbf{F}\mathbf{a}(\theta)))^{- 1}(\mathbf{F}\mathbf{a}(\theta))^{H}. \tag{16}\]  
Therefore, the noise subspace can be represented as \(\mathbf{r}_{k}^{H}P_{\mathbf{F}\mathbf{a}(\theta)}^{\perp}\mathbf{r}_{k}\).  
The DOA estimate of the \(k\)th source, denoted as \(\hat{\theta}_{k}\), can be obtained by minimizing the noise subspace projection, that is \(\hat{\theta}_{k}=\arg\min_{\theta}\mathbf{r}_{k}^{H}P_{\mathbf{F}\mathbf{a}( \theta)}^{\perp}\mathbf{r}_{k}\). The formulated minimization problem can be rigorously solved using various traditional optimization methods.  
To summarize, the proposed methodology uses a dual-layer recursive estimation scheme. In the first loop, the current source data are separated from multiple sources. Then, the DOA and power estimates are corrected by alternating optimization. In the second loop, the DOA of a new source is obtained by utilizing the estimated angle and power information. The termination criterion is satisfied when the iterative updates to the DOA estimates fall below a predefined convergence threshold, indicating stability in the solution.
## 2 Materials and Methods  
### Dataset  
A public dataset was the primary resource for processing and analysis in this work. This public dataset from [8] consists of a sample of 28 regular runners, which had familiarity and comfort with running on a treadmill, weekly mileage greater than 20 km, and a minimum average running pace of 1 km in 5 min during 10-km races. The dataset contains 48 anatomical and reflective markers (mm), of which 20 are anatomical, 20 technical, and 8 technical/anatomical; these were tracked using a 3D motion-capture system of 12 cameras, which had 4 Mb resolution, and Cortex 6.0 software (Raptor-4, Motion Analysis, Santa Rosa, CA, USA). During a trial, researchers recorded only data from technical and technical/anatomical markers. In contrast, most anatomical markers were used for a standing calibration trial during the calibration phase, except for the first and fifth metatarsal: (R.MT1, L.MT1) and (R.MT5, L.MT5), respectively; thus, all fully available \(20+8+4=32\) markers were considered as source features when using their three dimensions. These resulted in \(32\times 3=96\) features. Unfortunately, as shown in Section 3, it was found that some markers (2 anatomical and 1 technical/anatomical) have missing values in the dataset; these are: left 1 st metatarsal (L.MT1), right 1 st metatarsal (R.MT1), and left anterior superior 1 ilac spine (L.ASIS); these 9 (3 \(\times\) 3) features were removed from the initial source features considered; hence, data from only 87 features were used (\(96-9=87\)). Physical marker protocol in Figure 1--the figure created by the authors of the dataset [8]. Table 1 is a descriptive table of each marker used, ans was also extracted from the dataset's authors [8]. Figure 1: Markers’ placement protocol during an anatomical calibration trial, including the anterior (**A**), lateral (**B**), and posterior (**C**) views, obtained from [8]. By only considering technical (20), technical/anatomical (8), and 4 anatomical markers, the dataset then contained recording data from 32 markers in 3 dimensions (XYZ), which resulted in a total of 96 marker positions (32 \(\times\) 3 = 96), sampled at a frequency of 150 Hz and used as source features. A predictive model would use such features to predict the target features, which were forces (\(N\)) in three directions (\(F_{x}\), \(F_{y}\), \(F_{z}\)), sampled at a frequency of 300 Hz. An instrumented, dual-belt treadmill (FIT, Bertec, Columbus, OH, USA) collected
## 3 Separate Analysis per Individual Components
### Identification of Optical Elements Contributing to Phase Shifting  
Considering the study published in [24], in which a variation in the confocal microscope optical geometry as a function of IR laser radiation is reported, the first experiment of the manuscript presented here aimed to study the optical trap position with respect to the optical element in which it is formed (no trapped particle), i.e., the relative trap position was changed, moving the substrate from the sample immersion medium (located above the slide) down to the immersion oil, going through the slide. At each height interval, the IR laser power was changed, evaluating the power required for the phase addition to correspond to a value of \(2\pi\) radians. Since power is directly proportional to intensity (Power = Intensity \(\times\) Area), it follows that the intensity-phase ratio is also linear. Experimentally, the laser power can be manipulated, so this variable is used in this work.  
If the power-phase ratio were to remain constant in the different media, then no medium responsible for generating the additive phase could be identified. However, the results presented in the graph in Figure 4 provide evidence that by placing the trap in the  
Figure 3: Optical Trap—Mach–Zehnder interferometer experimental setup. (MO1—Long Working Distance Microscope Objective, MO2—Oil Immersion Microscope Objective).  
immersion oil, the phase addition is more sensitive to the change in power when compared to the slide glass and the medium in which a sample would be immersed over the slide, which in this case was air. It is observed how less power is required to cause a phase shifting of \(2\pi\) when the trap is placed in the oil (between \(-130\) and \(-240\) um).  
To corroborate the major contribution of oil to the detected phase shifting, independent tests of oil, water, air, and glass were conducted on a macroscopic scale outside the optical trap. Since in the trap-interferometer setup it is not possible to measure variables separately, three systems were designed: in order to study the refractive index, another Mach-Zehnder interferometer was mounted, and the element was contained; to measure the temperature, a thermocouple was used; to characterize the topography, and then calculate the volume, a Michelson interferometer was used. In the subsequent sections, the optical, thermal, and morphological analysis experiments of these elements, particularly oil, are described. Thus, by knowing the contributions of each material, it will be possible to calibrate and correct measurements.
# Development of Path Planning Tool for Unmanned System Considering Energy Consumption
## 5 Simulation and Experimental Setups  
### Simulation Setup  
All simulations were performed with a quadrotor vehicle with initial system properties \(P_{o}=[0,0,0]\) m, \(\theta_{o}=[0,0,0]\) rad, \(w_{o}=[0,\,0,\,0]\) rad/s, \(v_{o}=[0,\,0,\,0]\) m/s, \(a_{o}=[0,\,0,\,0]\) m/s\({}^{2}\), \(\|v_{min}\|_{2}=0\) m/s, \(\|v_{max}\|_{2}=5\) m/s, \(\|a_{max}\|_{2}=0.5\) m/s\({}^{2}\), \(Alt_{min}=3\) m, and \(Alt_{n}=30\) m, where \(P_{o}\), \(\theta_{o}\), \(w_{o}\), \(v_{o}\), \(a_{o}\), \(Alt_{min}\), and \(Alt_{n}\) are initial position, attitude, angular velocity, velocity, acceleration, minimum flight altitude, and normal flight altitude, respectively. Also, the time step used in MATLAB/Simulink is 0.01 s, installed in a desktop with the following system specifications; Intel Core i5-4590 CPU 3.40-GHz processor, 64-bit operating system, and 4.00-GB RAM. Every simulation flight was performed with a fully charged 4s 3300-mAh battery pack.
# Fatigue Crack Growth from Notches:
## 2 Material Model  
The materials studied were the 6082-T6 and 7050-T6 aluminum alloys. These materials were selected because they are available in the laboratory for future experimental work and because their elastic-plastic behavior is characterized. A mixed elastic-plastic model was assumed, being the isotropic elastic behavior given by the generalized Hooke's law. The shape of the yield surface is given by the Huber-Mises yield criterion, and its evolution, during plastic deformation, is described by the Vocc isotropic and Lemaitre-Chaboche kinematic hardening laws, due to the mixed plastic hardening behavior exhibited by the two materials, under an associated flow rule. The Vocc isotropic and Lemaitre-Chaboche kinematic hardening laws are given in Equations (1) and (2), respectively:  
\[Y(\overline{\varepsilon}^{p}) = Y_{0}+(Y_{Sat}-Y_{0})[1-\exp(-C_{Y}\overline{\varepsilon}^{p} ], \tag{1}\]  
where \(Y_{0}\), \(Y_{Sat}\), and \(C_{Y}\) are material parameters and \(\overline{\varepsilon}^{p}\) is the equivalent plastic strain.  
\[\dot{\mathbf{X}} = C_{x}\bigg{[}X_{Sat}\frac{\mathbf{\alpha}^{{}^{\prime}}-\mathbf{X}}{ \overline{\sigma}}-\mathbf{X}\bigg{]}^{\frac{p}{\varepsilon}}, \tag{2}\]  
where \(\dot{\mathbf{X}}\) is the back stress rate, \(X_{Sat}\) and \(C_{x}\) are material parameters, \(\mathbf{\alpha}^{{}^{\prime}}\) is the deviatoric Cauchy stress tensor, \(\mathbf{X}\) is the back stress tensor, \(\overline{\sigma}\) is the equivalent stress, and \(\dot{\overline{\varepsilon}}^{p}\) is the equivalent plastic strain rate.  
The set of elastic-plastic properties identified, by the fitting of stress-strain curves obtained in smooth specimens, for the AA6082-T6 and AA7050-T6 are shown in Table 1. The AA7050-T6 was found to have a pure kinematic hardening behavior; however, a pure isotropic hardening behavior was also considered for this alloy, as shown in Table 1. The objective was to study the effect of hardening model in the context of the paper.