esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Non-linear Methods:  
In contrast to the traditional linear techniques, nonlinear methods have the ability to deal with complex nonlinear data. Previous studies have shown that nonlinear techniques outperform their linear counterparts on complex artificial tasks. Examples are for instance the Swiss Roll dataset comprising a set of points that lie on a spiral-like two-dimensional manifold within a three-dimensional space [49, 50]. These methods aim at finding the intrinsic dimensionality (\(d\)) of a data set, being the minimum number of parameters that is necessary in order to account for all information contained within.  
Techniques for intrinsic dimensionality estimation can be subdivided into two main groups: (1) estimators based on the analysis of local properties of the data and (2) estimators based on the analysis of global properties of the data. Local methods used in this analysis comprise the correlation dimension estimator (_CorrDim_), the nearest neighbor dimension estimator (_NNDim_), and the maximum likelihood estimator (_MaxLike_). Global estimators additional to the PCA considered in this analysis are the packing number estimator (_PackNum_), and the geodesic minimum spanning tree estimator (_GMST_). These methods are briefly introduced in the Appendix, but readers are referred to the \"Matlab Toolbox for Dimensionality Reduction drtoolbox\" [51, 52, 53], for a more comprehensive discussion and comparison.  
Table 3 compares these different methods for both the complete spectral dataset including all spectral bands and the dataset given by the spectral indices listed in Table 1. The results show similar  
Figure 4: Histogram of the absolute values of pairwise correlation between individual bands (left) and vegetation indices (right) that are listed in Table 1. \(N\) indicates the number of spectral bands or vegetation indices considered. The total number of pairs can be calculated as _N * (N – 1) /2_.  
values when comparing the estimated intrinsic dimensionalities for the two data sets, suggesting that the much smaller (\(N=28\)_vs._\(N=2151\)) set of indices might well be able to represent the entire information content of the original dataset of all spectral bands. Comparing \(d\) estimations from different techniques, they show some more pronounced deviations indicating the different foci of the methods with respect to the non-linear characteristics of the data. However, the estimated intrinsic dimensionality of both datasets ranges around 3-6 (_PCA, GMST_) and does not exceed a value of 8 (_MAXLike_).  
These values give some guidance on the maximum number of SI's as independent variable that will be needed in any type of regression or non-linear analysis.
# Erosion Resistance Properties of Iron-Carbon Composite Plating to Molten Lead-Free Solder
## 2 Experimental Procedure
### Erosion-Resistance Test  
To evaluate the erosion characteristics of the Fe plating layer, Fe-graphite and Fe-MWCNT composite plating layers in molten SAC305, an erosion-resistance test was conducted. SAC305 solder wire with a volume of 39.7 mm\({}^{3}\) was melted on each plating layer at 623 K for 7 h. After the test, the erosion depth was measured from the cross-section of the specimen using a SEM. Moreover, electron back scatter diffraction pattern (EBSD) analysis was performed for Fe-graphite and Fe-MWCNT composite plating layers. EBSD analysis was performed on a measurement field with a size of 5 \(\upmu\)m \(\times\) 4 \(\upmu\)m using an acceleration voltage of 15 kV.
# Deep Learning-Based Cyclic Shift Keying Spread Spectrum Underwater Acoustic Communication
## 2 System Structure
### Conventional CSK-SS UWA Communication System Structure
\begin{array}{ll}Z_{k}(t)&=\int_{\tau_{0}}^{T_{s}+\tau_{0}}r(t)c_{k}(t-\tau_{0 })\cos(2\pi f_{c}t+\varphi_{0})dt\\ &=\frac{1}{2}A_{0}R_{k}(0)+\frac{1}{2}\sum\limits_{l=1}^{L-1}A_{l}R_{k}(\tau_ {l}-\tau_{0})\cos[2\pi f_{c}(\tau_{l}-\tau_{0})]\\ &+\int_{\tau_{0}}^{T_{s}+\tau_{0}}n(t)c_{k}(t-\tau_{0})\cos(2\pi f_{c}t+\varphi _{0})dt,\end{array}  \tag{5}\]  
The correlation functions \(R_{k}(0)\) and \(R_{k}(\tau_{l}-\tau_{0})\) in Equation (5) can be expressed in the form of spread spectrum code  
\[R_{k}(0)=\int_{\tau_{0}}^{T_{s}+\tau_{0}}c_{j}(t-\tau_{0})c_{k}(t-\tau_{0})dt, \tag{6}\]  
\[R_{k}(\tau_{l}-\tau_{0})=\int_{\tau_{0}}^{T_{s}+\tau_{0}}c_{j}(t-\tau_{l})c_{k }(t-\tau_{0})dt. \tag{7}\]  
\(Z(t)\) changes with the change of \(k\), and the position of its maximum value is the information modulated on the code phase. In the multipath fading channel, the influence of multipath fading on the performance of the CSK-SS system can be explored through the correlation function of the spread spectrum code. As shown in Figure 2, When SNR = 10 dB, the correlation peak of the spreading sequence decreases by nearly 15% after the CSK-SS signal passes through the multipath fading channel and decreases by nearly 28% when SNR = 0 dB. Figure 3 shows the correlation peak of the spreading sequence under the influence of multipath fading of the DSSS signal. When SNR = 10 dB, its peak value decreases by about 10% and decreases by nearly 18% when SNR = 0 dB. The above comparison shows that compared with the DSSS system, the CSK-SS system breaks the limitation of spreading gain on communication rate, and the improvement of communication rate is at the cost of destroying the excellent PACF characteristics of sequences in the DSSS system. In order to reduce the impact of multipath fading and noise on the technology of the CSK-SS system, this paper proposes a DL-based CSK-SS communication system. At the receiving end of the system, the CSK-SS signal is demodulated through the neural network model, which replaces the de-carrier, despreading, and related decision operations in the conventional system. The trained neural network establishes the mapping relationship between the CSK-SS signal and its carrying bits and solves inaccurate decision results caused by multipath fading in a conventional CSK-SS system. Figure 2: Correlation results of original despreading and despreading with multipath in CSK-SS.
# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
## 3 Methods
### Smartaugment  
SmartAugment optimizes the number of sampled color and geometric augmentations and their magnitude separately (see Figure 1b and Algorithm 1). Having these distinct sets of augmentations allows control over the type of applied augmentation instead of optimizing the total number of sampled augmentations and their magnitude collectively. SmartAugment also optimizes a parameter that determines the probability of applying data augmentations \(P\) instead of having the Identity operation in the augmentation list, as done by recent approaches.  
SmartAugment uses Bayesian Optimization (BO) [15] to search for the best augmentation strategy. The space of augmentation strategies include the following parameters: number of color augmentations \(N_{C}\), number of geometric augmentations \(N_{G}\), color magnitude \(M_{C}\), geometric magnitude \(M_{G}\), and probability of applying augmentations \(P\). These hyperparameters are optimized with the BO algorithm until a given budget is exhausted. Once BO chooses the augmentation parameters, the augmentations are sampled randomly without replacement for each epoch and image from the given list of augmentation operations as listed in Table 2. RandAugment, in contrast, samples with replacement and therefore allows sampling the same augmentation several times for the same image.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Color Ops** & **Range** & **Geometric Ops** & **Range** \\ \hline Sharpness & (0.1, 1.9) & Rotate & (0, 30) \\ AutoContrast & (0, 1) & ShearX & (0.0, 0.3) \\ Equalize & (0, 1) & ShearY & (0.0, 0.3) \\ Solarize & (0, 256) & TranslateX & (0.0, 0.33) \\ Color & (0.1, 1.9) & TranslateY & (0.0, 0.33) \\ Contrast & (0.1, 1.9) & Identity * & \\ Brightness & (0.1, 1.9) & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed overview of data augmentation operations and their magnitude ranges. We use the same augmentations as in RandAugment paper [12]. * The Identity operation only belongs to this list for the RandAugment and TrivialAugment approaches.  
Figure 1: Comparison of RandAugment and SmartAugment.
## 5 Numerical Results
### Two Force Identification Simulation  
In this subsection, we study the proposed method when capturing loads applied at multiple degrees of freedom. To this end, two forces are applied on the same four-degrees-of-freedom system studied in Section 5.1. The loads are located at the first and the second degrees of freedom. The displacements are measured at the first three degrees of freedom in order to identify the unknown loads. Again, the identification results using traditional Tikhonov regularization method and the augmented Tikhonov regularization method are shown alongside in the Figures 14-21. The considered noise levels of the responses SNR are assumed to be 40 dB and 30 dB. We take two external loading cases sinusoidal and impact loads. The accuracy of the identified loads are shown in Tables 2 and 3.  
Figure 14: Identification result of two sinusoidal loads at SNR 40 dB (Force 1): (a) [PERSON] method; (b) the proposed method.  
Figure 15: Identification result of two sinusoidal loads at SNR 40 dB (Force 2): (a) [PERSON] method; (b) the proposed method.  
Figure 16: Identification result of two sinusoidal loads at SNR 30 dB (Force 1): (**a**) [PERSON] method; (**b**) the proposed method.  
Figure 17: Identification result of two sinusoidal loads at SNR 30 dB (Force 2): (**a**) [PERSON] method; (**b**) the proposed method.  
Figure 18: Identification result of two impact loads at SNR 40 dB (Force 1): (**a**) [PERSON] method; (**b**) the proposed method.  
Figure 19: Identification result of two impact loads at SNR 40 dB (Force 2): (**a**) [PERSON] method; (**b**) the proposed method.  
Figure 20: Identification result of two impact loads at SNR 30 dB (Force 1): (**a**) [PERSON] method; (**b**) the proposed method.  
The results again confirm the previous set of conclusions. The accuracy of the augmented Tikhonov regularization method is consistently better than the Tikhonov regularization method. The augmented Tikhonov regularization method can effectively capture multiple loads for the four-degrees-of-freedom system. It should be noted that the identification accuracy is relatively lower when compared to a single load case. This is caused by the interaction in between the two loads. However, the identification results still capture all the dynamics of the external loads.
# Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
## 4 The Rogue Node Detection Scheme
### The Process of the U-Test for Rogue Node Detection  
Hypothesis testing is used to test specific predictions by calculating how likely it is that a pattern or relationship between variables could have arisen by chance. The basic idea of hypothesis testing is the small probability reduction method. The small probability idea believes that small probability events are basically impossible to occur in an experiment [31; 32]. Based on this idea, we first make a hypothesis, which is very likely to be true. If, in an experiment, the experimental results deviate from the original hypothesis, then the small probability event occurs. Following this, we doubt the authenticity of the original hypothesis and reject it. The U-test is a commonly-used hypothesis testing method. The U-test uses the theory of standard normal distribution to determine the difference so as to compare whether the difference between two samples is significantly different or not [33]. The specific steps for using the U-test for fog nodes to detect rogue nodes are as follows.  
Step 1: For samples \(\mu_{1}\) and \(\mu_{2}\), put forward a hypothesis H0. H0 believes there is no significant difference between the two samples, which is \(\mu_{1}=\mu_{2}\). At the same time, an alternative hypothesis H1 is provided, which is \(\mu_{1}\ \
eq\mu_{2}\).  
Step 2: Denote the value of the statistic \(u\) as:  
\[u=\frac{\overline{x}-\overline{y}}{\sigma/\sqrt{n}} \tag{5}\]  
Figure 7: The process of vehicle classification using the K-means algorithm.  
where \(\overline{x}\) represents the mean value of the speed received by the fog node, \(\overline{y}\) represents the overall average speed calculated by the cluster head (the same as \(V_{avg}\) in this paper), \(\sigma\) represents the standard deviation of the overall speed and \(n\) represents the number of samples received by the fog node.  
Step 3: Suppose \(\alpha=0.05\), which means the probability that the test statistic \(u\) falls in the receptive field is \(1-\alpha=95\%\). This paper uses a two-sided test, and the upper and lower limits of the receptive field are \(u_{u/2}\) and -\(u_{u/2}\), respectively, that is, if the value is greater than \(u_{u/2}\) or less than \(-u_{u/2}\), the sample is different from the population, and it is initially determined to be an abnormal sample. According to the U-test, when \(u<1.96\), that is, the \(p\) value is greater than 0.05, it means the difference between the samples is significant. Then, hypothesis H0 is accepted and hypothesis H1 is rejected  
After multiple rounds of classification and inspection, all rogue nodes are obtained. The fog node sends the information of rogue nodes to the cluster head. The cluster head also sends the rogue node information to other clusters on the road.
# First Theoretical Realization of a Stable Two-Dimensional Boron Fullerene Network
## 3 Results and Discussion
Based on the DFT results for the uniaxially stressed atomic configurations, we could not detect the clear failure behavior, which can be a clear indication of the higher ductility of the predicted boron fullerene structure than that of the carbon-based counterparts [6; 10; 11]. It is worth noting that the Poisson's ratio is found to be only 0.009. The thermal stability of the B\({}_{40}\) network was, moreover, tested by the AIMD simulations at three temperatures of 500, 700, and 1000 K for 20 ps long calculations. The AIMD results for the evolution of the per atom total energy for different temperatures are illustrated in Figure 2d, along with the side views for the final atomic configurations. It is observable that up to a moderately high temperature of 700 K, the single-layer B\({}_{40}\) network stays completely stable, whereas at 1000 K, the lattice is partially distorted. The presented DT results clearly confirm the  
Figure 1: (**a**) 3D view of the B\({}_{40}\) cage. (**b**) Top and side views for the 2D boron fullerene network. (**c**) 3D view for the electronic localization function (with yellow color) of the 2D boron fullerene network with an isosurface value of 0.75, illustrated using the VESTA package [31]. Find the energy minimized structures in the Supporting Information document. outstanding energetic, thermal, dynamical, and mechanical stability of the herein predicted B40 fullerene. Figure 3: Electronic band structures of the stress-free and strained B40 2D network by the PBE (solid lines) and HSE06 (dotted lines) methods. Figure 2: **(a)** Phonon dispersion and (**b**) group velocity of the predicted single-layer B40 network. **(c)** Uniaxial stress–strain at the ground state along with side views of the deformed structures. **(d)** The AIMD results for the per atom total energy of the B40 nanosheet during the simulations at temperatures of 500, 700, and 1000 K. The insets in panel **(d)** show the side views for the final atomic configurations after 20 ps of AIMD simulations. Last, but not least, we briefly investigated the electronic character of the predicted B\({}_{40}\) fullerene 2D network. From the results shown in Figure 3a for the stress-free fullerene network, the metallic electronic character is confirmed by the both considered methods of PBE and HSE06, consistent with those of pristine borophene monolayers. As shown in Figure 3b,c, we found that by applying the biaxial straining, the states around the Fermi level become denser and, consequently, enhance the metallicity of the system. On the other side, by applying the uniaxial loading, the valance and conduction bands around the Fermi energy start to separate, and, as shown in Figure 3e, for a relatively large strain of 0.1, a narrow and direct band gap of 0.07 appears in the electronic structure. These results confirm that the electronic structure of the unstrained and strained B\({}_{40}\) fullerene monolayers mostly show a metallic nature, with a low possibility of yielding a narrow gap semiconducting character under large uniaxial loading.
## 5 Taxonomic Distribution of QS Gene Topology Patterns  
The number of complete genomes does not allow us yet to draw definitive conclusions about the preference of certain patterns to appear in various classes or species of bacteria. It is apparent however that the \(\vec{RI}\) topology is predominant in \(\alpha\)-proteobacteria, while the \(\vec{RI}\) pattern is more frequent in \(\gamma\)-proteobacteria. Furthermore, the \(\vec{RI}\vec{I}\) and the \(\vec{RI}\vec{I}\) topologies seem to occur in the \(\beta\) and \(\gamma\) classes but not in \(\alpha\)-proteobacteria. The question arises whether the known QS proteins, such as LuxI and LuxR, cluster simply according to the known taxonomy or rather according to the topological pattern. Cladograms of 154 LuxI and LuxR protein sequences (Supplementary Figures 3 and 4) show a clear tendency for clustering according to topological patterns. For instance, the \(\vec{RI}\vec{I}\), \(\vec{RI}\vec{I}\), \(\vec{RI}\) and \(\vec{RI}\) patterns form clearly distinguishable groups both in the LuxR and in the LuxI trees. Also, if two LuxI (or LuxR) proteins from the same topology group are nearest neighbors within the tree, they are very rarely from the same genome. In more detail, of the LuxI-type proteins that occur in genomes with multiple QS circuits, 38% have a nearest sequence similarity neighbor from the same family, 35% from the same genus, 26% from the same species, and approximately 1% from the same genome. The frequency with which _luxR_ genes occur is similar: 37%, 36%, 27%, and 1%, respectively. This indicates that the patterns may have formed before the modern strains diverged from each other.  
An example in Figure 2 shows that LuxI proteins present in \(\vec{RI}\vec{I}\) patterns clearly separate from those present in \(\vec{RI}\) patterns within the same genome and cluster together with the respective genes of another (\(\beta\) or \(\gamma\)) class (Figure 2(A)). At the same time, the clustering of the RasaL proteins (Figure 2(B)) is identical to the clustering of their accompanying LuxI genes (Figure 2(A)). In other words, QS proteins seem to cluster according to gene topology at various taxonomic levels, which suggests that the _luxR_, _luxI_ as well as the intervening genes may have evolved together. Gene neighborhoods are known to evolve via complex rearrangements, with different combinations of genes from a neighborhood fixed in different lineages [53,54]. However, when the genes are part of fine-tuned regulatory networks, such as QS circuits, strict constraints may be imposed on the process of rearrangement.
###### Abstract  
Wireless body area network (WBAN) applications have broad utility in monitoring patient health and transmitting the data wirelessly. WBAN can greatly benefit from wearable antennas. Wearable antennas provide comfort and continuity of the monitoring of the patient. Therefore, they must be comfortable, flexible, and operate without excessive degradation near the body. Most wearable antennas use a truncated ground, which increases specific absorption rate (SAR) undesirably. A full ground ultra-wideband (UWB) antenna is proposed and utilized here to attain a broad bandwidth while keeping SAR in the acceptable range based on both 1 g and 10 g standards. It is designed on a denim substrate with a dielectric constant of 1.4 and thickness of 0.7 mm alongside the ShieldIt conductive textile. The antenna is fed using a ground coplanar waveguide (GCPW) through a substrate-integrated waveguide (SIW) transition. This transition creates a perfect match while reducing SAR. In addition, the proposed antenna has a bandwidth (BW) of 7-28 GHz, maximum directive gain of 10.5 dBi and maximum radiation efficiency of 96%, with small dimensions of 60 \(\times\) 50 \(\times\) 0.7 mm\({}^{3}\). The good antenna's performance while it is placed on the breast shows that it is a good candidate for both breast cancer imaging and WBAN.  
full ground antenna; UWB antenna; high gain antenna; breast cancer; microwave imaging Article  
Full Ground Ultra-Wideband Wearable Textile Antenna for Breast Cancer and Wireless Body Area Network Applications  
1  
M.A.Abbasi, Q.H.Full Ground Ultra-Wideband Wearable Textile Antenna for Breast Cancer and Wireless Body Area Network Applications
# Analytical Pyrolysis and Mass Spectrometry to Characterise Lignin in Archaeological Wood
## 3 Results and Discussion
### Single-Shot Py(HMDS)-GC/ms
As regards La Marmotta samples, the identification of methyl esters of vanillic (#72) and syringe acids (#89) with significantly high relative abundances allowed us to hypothesise a heat treatment as explanation for this particular lignin alteration. Heating wood to around 200-300\({}^{\circ}\)C causes the formation of small molecules, including methanol [101; 102], which can easily react in a gaseous phase with acidic moieties (vanillic and syringe acids), leading to the formation of methyl esters. The formation of methyl derivatives has also been observed during the heating of other materials as well, such as diterpenoid resins to produce pitch or tar from resinous woods [50]. Furthermore, the methyl derivatives of vanillic and syringe acids are known toasting markers of wooden barriques used in the production of wine [103; 104; 105]. Wood is also an isolating material, and when it is exposed to high temperatures, the surface can reach temperatures up to several hundred degrees, whereas the core (2-3 cm under the surface) remains at room temperature, thus explaining why these phenomena mainly involve the wood surface. The occurrence of methyl esters of vanillic and syringe acids on the outermost part of wooden piles from La Marmotta site indicates that, before placing the piles in the lake to support the still houses, they had been exposed to artificial heating.