esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation and Results
### Implementation
#### 3.1.3 Artifact Elimination  
After the ICA step, we obtained 20, 35, and 40 ICs for the FA, GM, and fALFF datasets, respectively. For dMRI, we used the ICBM-DTI-81 white-matter labels atlas and JHU white-matter tractography atlas [66; 67; 68], provided in FSL (fsl.fmrib.ox.ac.uk/fsl/fslwiki/, accessed on 10 November 2019) to identify the WM tracts. For sMRI and fMRI, each IC was transformed into \(Z\)-scores to obtain the zero mean and unit variance, and the voxels with a \(Z\)-score greater than the threshold 2 (\(|Z|\geq 2\)) were converted from MNI coordinates to Talairach coordinates and entered into a database to assign anatomical and functional labels for the left and right hemispheres.  
To remove artifacts caused by body movements such as breathing and heart beating, we eliminated ICs from the brain ventricles or areas where large blood vessels are located [69; 70]. We also eliminated ICs that were spread across wide regions of the brain or at the edges of brain images to remove motion-related signals [57; 71]. More specifically, for the FA, we removed the ICs with spotty, diffusely distributed patterns over most of the brain, which represented noise. For the sMRI, we removed the sinus susceptibility noise and WM components [72]. For the fMRI, we removed ICs that were located beyond the area of GM; e.g., ventricles, skull, and surrounding tissue, WM, frontal/sagittal sinus susceptibility noise, CSF, and motion artifacts with ring patterns [72; 73; 74; 75; 76; 77]. As a result, 10, 27, and 17 ICs were retained for FA, GM, and fALFF datasets, respectively. Their corresponding subject covariations were formed into three reduced subject covariation matrices with sizes of \(10\times 162\) (FA), \(17\times 162\) (fALFF), and \(27\times 162\) (GM), which were then used in the IVA step. The spatial maps of the retained components were converted to \(Z\)-scores and thresholded at \(|Z|\geq 2\) (Figures 4-6).
## Appendix A  
The frequency \(\omega_{p}\) is the plasmon frequency at \(B=0\) and satisfies the following dispersion relation  
\[\omega_{p}=\sqrt{\frac{n_{s}e^{2}k}{2m^{*}\epsilon_{0}\epsilon(k)}}, \tag{1}\]  
where \(k\) is the wave vector of the (magneto)plasmon. In the case of samples with gratings of the period \(\Lambda\), plasmon wave vectors can take values equal to an integer multiplicity of \(2\pi/\Lambda\). The effective dielectric constant \(\epsilon(k)\) is a \(k\)-dependent function which allows to describe the electromagnetic environment of the 2 DEG. There are two typical situations: when the 2 DEG is ungated and when it is covered with a metallic gate. In these cases, one has  
\[\epsilon_{ug}(k)=\frac{1}{2}\left(\epsilon_{1}+\epsilon_{2}\frac{1+\epsilon_{ 2}\tanh(kd)}{\epsilon_{2}+\tanh(kd)}\right), \tag{2}\]  
and  
\[\epsilon_{g}(k)=\frac{1}{2}(\epsilon_{1}+\epsilon_{2}\coth(kd)), \tag{3}\]  
respectively [38; 39; 40]. In these formulas, \(\epsilon_{1}\) and \(\epsilon_{2}\) is the dielectric constant of the quantum well and the barrier material, respectively, and \(d\) is the barrier thickness.  
These formulas are derived under the assumption of a uniform and infinite 2 DEG covered with a barrier and a metallic gate or a barrier only. In real situations, plasmons are typically observed in samples with metallic gratings which cover the 2 DEG only partially. We have found that in all studied cases of a 2 DEG in CdTe-, GaAs- and GaN-based structures [24; 27; 30], a very good description of experimentally determined dispersion relation was obtained with Equation (1) and a dielectric constant equal to a weighted average of \(\epsilon_{ug}\) and \(\epsilon_{g}\):  
\[\epsilon=\alpha\epsilon_{ug}+(1-\alpha)\epsilon_{g}, \tag{4}\]  
where \(\alpha\) is equal to the percentage of the period \(\Lambda\) not covered with metal. In the case of etched gratings, \(\alpha\) will denote a fraction of the period \(\Lambda\) which was not etched.  
On the other hand, we would like to stress that applicability of Equation (4) must be limited since recent analitical considerations [41; 42] show that this is rather the width of the metallic stripe, not the period of the grating, which determines the dispersion relation in a partially gated 2 DEG.
## 1 Introduction
This study aims to quantify the uncertainty of COSMIC-2 bending angle profiles caused by the excess phase processing and bending angle inversion approaches. Studies (i.e., [3; 6; 30; 36]) demonstrated that the best way to quantify the retrievals uncertainty from RO data products is to compare them to those derived from the independently processed data products. To understand the COSMIC-2 data uncertainty induced in each step of the RO processing, the NOAA NESDIS/STAR developed an independent processing package (hereafter STAR package) to convert the COSMIC-2 pseudo-range and carrier phase observations to excess phases. In addition, we used the Radio Occultation Processing Package (ROPP)\(-\)9.0 [37] to convert the excess phases to bending angles. The ROPP has been used in the operational European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) RO inversion and ECMWF data assimilation for RO data, including CHAMP, Metop-A/-B, COSMIC, Fengyun\(-\)3 GNSS Occultation Sounder (GNOS) [38], and Korean Multi-purpose Satellite\(-\)5 (KOMPSAT\(-\)5) RO missions. Close to 12,000 COSMIC-2 occultations in Oct. 2019 were processed and used in this study. We compared the intermediate data products with those from UCAR to quantify the possible processing-dependent COSMIC-2 data products between the two independently developed datasets. Section 2 describes the detailed approaches to converting the COSMIC-2 carrier phase to the excess phase and converting the derived excess phase to bending angle. Section 3 compares the STAR bending angles with the UCAR product and those derived from the ERA5 data. The comparison uncertainty is discussed in Section 4. Section 5 concludes this study.
## A Metasurfaces Review: Definitions and Applications
###### Abstract  
This paper is a critical review of metasurfaces, which are planar metamaterials. Metamaterials offer bespoke electromagnetic applications and novel properties which are not found in naturally occurring materials. However, owing to their 3D-nature and resonant characteristics, they suffer from manufacturing complexity, losses and are highly dispersive. The 2-dimensional nature of metasurfaces allows ease of fabrication and integration into devices. The phase discontinuity across the metasurface offers anomalous refraction, thereby conserving the good metamaterial properties while still offering the low-loss characteristics. The paper discusses salient features and applications of metasurfaces; wavefront shaping; phase jumps; non-linear metasurfaces; and their use as frequency selective surfaces (FSS).  
metasurface; frequency selective surface; FSS; metamaterial; review 2019; 9, 2727; doi:10.3390/app913727
## 7 Receding Horizon Trajectory Planning
### Disturbance Estimation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Motion Model**} & \multicolumn{2}{c}{**Gradient Estimator**} \\ \cline{2-5}  & **Linear** & **Nonlinear** & **Hamiltonian** & **Gradient** \\ \hline \multicolumn{5}{c}{Differential Dynamic Programming (DDP) [109]} & ✗ & ✓ & ✗ & ✓ \\ \hline \multicolumn{5}{c}{Linear Quadratic Regulator (LQR) [110]} & ✓ & ✗ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Iterative LQR (iLQR) [111]} & ✗ & ✓ & ✗ & ✓ \\ \hline \multicolumn{5}{c}{Linear Model Predictive Control (MPC) [112]} & ✓ & ✗ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Nonlinear Model Predictive Control (NMPC) [62]} & ✓ & ✓ & ✓ & ✗ \\ \hline \multicolumn{5}{c}{Constrained Nonlinear Model Predictive Control CGMRES (NMPC-CGMRES) [113]} & ✗ & ✓ & ✓ & ✗ \\ \hline \multicolumn{5}{c}{Corridor-based Model Predictive Contouring Control (CMPCC) [114]} & ✓ & ✗ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Constrained Nonlinear Model Predictive Control Newton (NMPC-Newton) [115]} & ✗ & ✓ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Model Predictive Path Integral Control (MPPI) [116]} & ✓ & ✓ & ✗ & ✗ \\ \hline \multicolumn{5}{c}{Cross Entropy Method (CEM) [117]} & ✓ & ✓ & ✗ & ✗ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the properties of receding horizon trajectory planning techniques. Checks and cross marks indicate whether a feature is available or not.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 5 The \(D^{3}\)-Tree  
In this section, we present our proposed structure, \(D^{3}\)-Tree, which introduces many enhancements over the solutions in literature and its predecessor, \(D^{2}\)-Tree [15].  
In general, a \(D^{3}\)-Tree structure with \(N\) nodes and \(n\) data elements residing on them achieves: (i) \(O(\log N)\) space per node; (ii) deterministic \(O(\log N)\) searching cost; (iii) deterministic amortized \(O(\log N)\) update cost both for element updates and for node joins and departures; (iv) deterministic amortized \(O(\log N)\) bound for load-balancing; (v) deterministic amortized \(O(\log N)\) search cost in case of massive node failures.
## 3 Results
### Compression Properties  
The core performance of the natural intervertebral disc lay in its ability to bear the weight of the head and its resilience to deformation. Therefore, the study of the compressive properties of materials intended to replace the natural intervertebral disc was crucial for determining their potential application in the field of intervertebral disc replacement.  
Figure 5: Uniaxial tensile stress–strain curves of PCUs of different hardness via different molding methods: (**a**) 75A; (**b**) 85A; (**c**) 95A; (**d**) 55D.  
Figure 6: Three-dimensional distribution diagram of average stress level values of gradient-hardness PCU under different molding methods and different elongation rates.  
Figure 7 presents the compressive stress-strain curves of PCU with four different hardness gradients processed under various forming methods. It could be observed that, regardless of the processing method, the stress level of the material at the same strain consistently increased with increasing material hardness, indicating an incremental increase in compressive stiffness. Table 3 shows the average stress values and their standard deviations at 15% strain for the compression specimens processed under different forming methods. All tests reached a maximum displacement of 4.8 mm, corresponding to 80% of the specimen height. Based on the requirements proposed by surgeons' experience, elastomers suitable for the cervical spine had to withstand at least 200N of spinal compressive load with a deformation of no more than 1 mm. In conjunction with existing research findings, elastomers needed to have a stress level corresponding to at least 0.9 MPa at 15% strain to meet the load-bearing requirements of the natural intervertebral disc [25; 26]. Under the IM method, the average stress levels of PCU with hardness of 75A, 85A, 95A, and 55D were 1.32 MPa, 1.38 MPa, 2.01 MPa, and 3.63 MPa, respectively, all exceeding 0.9 MPa. However, the stress levels of 75A-hardness PCU processed by CM and 3D were 0.77 MPa and 0.89 MPa, respectively, slightly below 0.9 MPa.
###### Abstract  
To address the inability to quantitatively analyze the performance of the direct sequence spread spectrum (DSSS) communication system in the presence of short-period pseudo-noise (PN) sequences, this study derives the interference components resulting from correlation processing of single-frequency interference. Given the short period of the spread spectrum sequence used in underwater acoustic systems, a theoretical expression is proposed for analyzing the anti-single-frequency interference performance of DSSS communication systems in underwater acoustics. This expression combines the interference power output by the system to enable quantitative analysis. Simulations are conducted to validate the accuracy of the theoretical expression. The results reveal that the interference sequence generated by correlation processing assumes either a direct current (DC) or sinusoidal distribution. The bit error rate (BER) of the system is influenced by signal-to-interference ratio (SIR), interference frequency offset, phase deviation, and the structure of the spreading code itself. The BER exhibits regular variation with changes in the interference phase. In all working conditions where the interference frequency offset is an integer multiple of the bit bandwidth, single-frequency interference with a frequency offset equivalent to the bit bandwidth has the greatest impact on the system. The concise theoretical expression presented in this study has significant practical value as it allows for quick prediction of variation in the anti-single-frequency interference performance of underwater acoustic DSSS communication systems using short-period PN sequences.  
short-period PN sequence; single-frequency interference; interference power; standard Gaussian approximation Article
## 4 Implementation and Experimental Results
### Dataset Analysis  
The CHB-MIT EEG dataset has many interesting points that represent challenges to the typical data mining techniques as follows:  
1. The frequency of classes values is an imbalance, and the dataset contains only two values for the target class, zero for the normal EEG signal and one for the EEG signal. The total number for the normal class instances in the extracted rows was 109,141, and that represents 99.64% of the whole extracted data. This majority leads most classification techniques to incorrect high accuracy; for example, if the classifier predicts all extracted rows as a normal EEG signal, the accuracy will be 99.64%.
2. Very weak correlation between the features on the one hand and the target class on the other hand. In Table 2 and Figure 3, we can notice that all correlation values (in columns correlation B) are very close to zero, which makes the classification task more difficult.  
Figure 2: The placement of EEG electrode based on the 10–20 International system.  
3. High correlation between most of the features, as can be seen in Table 2 and Figure 3, the average correlation between each feature and other features is shown in Table 2 in columns (correlation A), and most values are more than 0.8. The features are not completely matched (i.e., the correlation is not 1) to be considered as a frequent feature and will be removed.
# Locating Image Sources from Multiple Spatial Room Impulse Responses
## 2 Methods
### Detect Image Sources  
At this point, the source-receiver measurements have been aligned to the world coordinate frame. This way the extracted ER objects are also comparable w.r.t. each other. In other words, the ER objects from one sound source should be located close to each other if they have been reflected from the same planar surface. In theory, the ER objects from multiple receivers should gather around the approximate image source positions when presented in the common coordinate frame. However, not all receivers report the same image sources, and some of the objects might still be false detections, as shown on the left in Figure 4. As shown on the right, combining the receiver data not only allows finding more reflections than it is possible with a single measurement, but also manages to filter out additional false detections. Therefore, the next step is to detect image sources by clustering the extracted ER objects.  
The stage presented here aims at creating multivariate Gaussian distributions out of ER objects. For each sound source, the ER objects from associated source-receiver measurements are clustered with the so called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm [17] (grouping distance 1 m (Euclidean), min. 2 neighbors to consider as a core point). After this, each cluster selects one ER object per receiver that has the highest sound level. If the cluster still contains ER objects from a defined number of receivers, they form an image source. The required number of receivers is an important tuning parameter that allows a trade-off between detection noise and precise image source locations.  
The found image source is presented as a multivariate Gaussian distribution that is weighted by DoA stability of the ER objects. The distribution mean is considered as the actual image source location and the covariance as the reliability of the location estimate.  
Figure 4: On the **left**: early reflection (ER) objects from 5 receivers measuring one source in a rectangular room. The detected receiver positions are found to be noisy and not all receivers are detecting the same reflections. On the **right**: image sources detected from 10 receiver measurements. The algorithm is able to filter out most of the noise and reveal image source positions that are mostly accurate.  
This way, each image source is not only presented as a point in space but as a volume where the actual image source most probably resides in.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
###### Abstract
For example, an iterative decision method was proposed for ACO-OFDM, which improves the demodulation process by accurately estimating the sign information of the original unclipped signal, thereby enhancing overall performance [11]. Additionally, researchers introduced a semi-deterministic approach to mitigate clipping noise in DCO-OFDM systems [12]. Recently, a new decision-directed signal reconstruction (DDSR) algorithm with optimal thresholds for mitigating clipping noise was proposed in optical OFDM systems [13]. Theoretical analysis and simulation results verified that using optimal thresholds proposed in [13] significantly enhanced the DDSR algorithm's performance. Another receiver-based algorithm using a nonlinear technique to mitigate the clipping distortion in optical OFDM system was studied [14], in which the clipped signal could be extracted using a nonlinear process. These advancements represent significant progress in the ongoing efforts to optimize optical OFDM systems, especially in the presence of challenging nonlinear effects caused by the characteristics of light sources. In this paper, we introduce a novel interference degradation algorithm, referred to as the tolerance-aided interference degradation (TAID) algorithm, designed for implementation at the receiver end. The primary objective of TAID is to mitigate both clipping distortion and partial additive noise inherent in the received signals. Specifically, when employing the CEO-OFDM technique, the proposed algorithm operates by introducing a decision tolerance at the receiver stage. This tolerance value is strategically chosen to assess the signals received in the initial two signal frames, determining whether the clipped portion transmitted in the extra frame is essential for accurate reconstruction. Through a judicious selection of an appropriate decision tolerance, our algorithm significantly reduces additive noise while concurrently compensating for clipping distortion, thereby enhancing the overall signal quality and system performance. The remainder of the paper is organized as follows. The problem descriptions are discussed in Section 2. In Section 3, we describe the proposed TAID algorithm and analyze its performance. The numerical results of TAID and the performance comparison with DCO-, ACO-, and CEO-OFDM are discussed in Section 4. The paper is concluded in Section 5.
## 4 AFC Algorithm for FSM Motion System
Based on the experimental results obtained when utilizing only the PI controller (Figure 13), the periodic disturbances with multiple frequencies in the CES were primarily caused by mechanical vibrations simulating the target platform. The rotational frequency of the platform cooling fan was 59 Hz, and the corresponding frequency of the platform's mechanical vibration was 73 Hz, which was applied to the FSM motion system with a vibration generator. Therefore, the power spectrum of the CES (Figure 14) shows that the disturbance was caused by the cooling fan's rotation frequency (i.e., 59 Hz). Another peak was observed at 73 Hz, corresponding to the mechanical vibration of other parts induced by the fan's rotation. To eliminate periodic disturbances with multiple frequencies caused by the cooling fan's rotation and the platform's mechanical resonance frequencies, an AFC algorithm was incorporated into the FSM motion control system. The resulting frequency responses are presented in Figures 15-17. Figure 11:0 Performance of PI controller for FSM motion system (power spectrum). Figure 11:1 Performance of PI controller for FSM motion system (residual CES). Figure 11:2 Open–loop frequency response of FSM motion system (AFC at 59 Hz). To validate the proposed control method, the transfer functions of the AFC algorithm for eliminating the aforementioned disturbance frequencies were modeled as follows:  
Figure 11:3  
Figure 11:4
## 4 Discussion
### Material Structural Change  
The above mechanism explains the transition of the groove formation of diamond, but its relation to the sp2 bonded material deposition mechanism must be considered. Laser irradiation is known to cause changes to the crystal structure [31; 32; 33]. The presence of oxygen in the processing environment is known to greatly reduce the onset temperature of graphitization [34; 35; 36]. Thus, in order for changes in crystal structure to take place, abundant oxygen is required.  
At the lower surface, no sp2 bonding was observed. This may be partially due to the effects of gravity; the ablated non-diamond particles may have been fallen below or may have expanded into the surrounding air. An alternative cause for the limited deposition of particles is that fact that as the laser reaches the surface from inside the diamond sample, and there is a higher probability that it will encounter an inner absorbing center before reaching the lower surface. These absorbing centers have less surrounding oxygen compared to the surface defects. This results in limited graphite production. However, there is some sp2 bonding present within the groove, indicated by peak at 1561 cm\({}^{-1}\) in Figure 11. This may be because as ablation occurs on the lower surface, bonds among the surface atoms are broken. This removal of bonded atoms caused re-arrangement of the crystal structure into a more thermodynamically stable state, namely an amorphous or graphitic structure [37].  
Figure 11 also indicates that recrystallization due to laser heating did not occur. [PERSON] and [PERSON] (2015) proposed that the formation of diamond microneedles occurs by recrystallization through the nanosecond laser heating of diamond-like amorphous carbon [38]. In the proposed mechanism, nano-diamonds nucleate and grow rapidly by liquid mediated explosive recrystallization to form microneedles. However, in our study, the area within the groove was found to include sp2 bonds and did not possess a clear diamond peak. Thus, the rough surface can be attributed to spalling rather than recrystallization.  
The decrease in deposited sp2 bonded particles, indicated by Figure 7, also agrees with the conclusion that much of the material removal occurred by spalling. Additionally, by comparing groove depth, it can be concluded that spalling has a higher material removal rate compared to the oxidation reaction. In addition, although it may be limited to a certain range to laser fluences, this may be used as a method to machine deep grooves while reducing crystal structure change without additional apparatus or processes.  
Figure 11: Raman spectra of a point on the groove wall.