esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Embedding Algorithm  
Four different cover images of size 1920 \(\times\) 1080 are selected. These cover images are color and contain information about the precious object. Each cover image consists of three planes, i.e., Red, Green, and Blue. Thus, the A, B, and C outputs obtained from the previous step are embedded into the HH subband of the cover image using following the steps:  
Step 1: Read the color cover image (1920 \(\times\) 1080).  
Step 2: Divide the color image into Red (R), Green (G) and Blue (B) Planes.  
Step 3: Apply 2D-Haar IWT to R-plane of the cover image and generate LL, LH, HL and HH band.  
Step 4: Select the HH band in R-plane of the cover image.  
Step 5: Divide HH band into 4 \(\times\) 4 blocks and select one block at a time randomly, using a random number generator.  
Step 6: Read the black and white binary data image (1920 \(\times\) 1080).  
Step 7: Apply IWT. This gives LL, LH, HL and HH sub-bands. Select LL band.  
Step 8: Replace LSB of each integer coefficient in the HH band of Red plane with LSB of corresponding integer coefficients in the LL sub-band of the binary data image1 using 8 patterns [(24)] as shown in Figure 4a-h.  
Step 9: Calculate Mean Square Error of each pattern and apply pattern with Least MSE for that block.  
Step 10: Select 3-bit key for the applied pattern from Table 1 and shift the selected key to left by three bits.  
Step 11: Repeat from Step 8 to Step 10 for the selected block until all data blocks of LL sub-band are embedded within HH sub-band of cover image in R-plane.  
Step 12: Apply IIWT for LL, LH and HL subbands of R-Plane and modified HH band and create R-plane of the stego image.  
Step 13: Repeat Step 4 to Step12 for G&B planes by using black and white binary data image2&3.  
Step 14: Combine the all these layers into single stego image.  
Step 15: Transmit the created stego image to display screen.  
Step 16: Transmit the generated key secretly.  
Figure 4: Eight patterns (**a**) Straight Up (SU), (**b**) Straight Down (SD), (**c**) Straight Forward (SF), (**d**) Straight Backward (SB), (**e**) Flipped Down (FD), (**f**) Flipped Up (FU), (**g**) Flipped Forward (FF), (**h**) Flipped Backward (FB).  
The obtained stego images are given to the display screen inside the object showcase room. The flowchart representation of the proposed embedding algorithm is given in Figure 5.
## 2 Materials and Methods  
The micro- and nanostructures were grown using the vapour-solid method (VS). The initial precursor material was composed of ZnS (90 wt%) (Sigma-Aldrich, 99.99% purity, Saint Louis, MO, USA) and Li\({}_{2}\)O (10 wt%) (Alfa Aesar, 99.5% purity, Haverhill, MA, USA). The powders were homogenized by milling them for 5 h at 180 rpm in a centrifugal ball mill (Retsch S100, Haan, Germany) and compacted to form disk-shaped samples. Then each sample was placed in a horizontal tubular furnace (C.H.E.S.A. Microtest, Madrid, Spain) at 950 \({}^{\circ}\)C for 10 h under a constant N\({}_{2}\) flow.  
For an initial content below 10 wt%, the resonances are less defined. In a previous work [19], it was shown that the incorporation of Li in the growth process of the ZnO structures produces a change in the morphology. It is for a Li\({}_{2}\)O starting content of 10 wt% when the best results in terms of obtaining intense and defined optical resonant modes are found. Therefore, this study was focused on the structures obtained starting with a 10 wt% of Li\({}_{2}\)O.  
The optical resonances were measured by recording the photoluminescence signal coming from different points of the nanostructures. The system used to perform the \(\upmu\)-photoluminescence (\(\upmu\)-PL) measurements was a confocal microscope Horiba Jobin Yvon LABRAM-HR (HORIBA Jobin Yvon, Villeneuve d'Ascq, France). A He-Cd laser was used to excite the sample with a wavelength of 325 nm. The laser was focused onto the sample using an LMU-40X-NUV Thorlabs objective (0.47 Numerical Aperture - NA), and the scattered light was also collected using the same objective. The excitation and collection points on the sample were the same. The collected PL signal was analyzed using a 600 lines/mm grating and recorded with an air-cooled charge-coupled device (CCD) camera. For obtaining the \(\upmu\)-PL maps, a step size of 2 \(\upmu\)m was employed. The PL spectra were analyzed using Labspec 5.0 software (France, Labspec version 5.0). All the measurements were done at room temperature. Morphological images and thickness measurements were recorded using an FEI Inspect (FEI Company, Eindhoven, Netherlands) scanning electron microscope (SEM) in secondary electron (SE) mode operating at accelerating voltages of 15-20 keV.
## 5 Decision Making  
Once the SPEA-2 algorithm completes the last generation or converges to a solution state where no improvement is achieved, it stops and provides both the history of individual solutions generated and the Elite solutions found in each generation. It is time for the DM to analyse the solution space and search among them for the final selected solution.  
There is at least one solution for each cross-section size considered, which ensures that the initial bending stress is minimal in the study area. In addition to these, there may be other valid solutions to the design problem that, being near-minimal, improve other aspects not considered by the FF.  
In a real design process, the final solution to be adopted depends not only on mechanical criteria such as the maximum initial bending stress, but also on other factors such as the available cross-sections, the minimum cross-section width to be used (which is usually determined by the diameter of the fastener), the minimum end distances to the fasteners, or non-programmed criteria, such as aesthetic criteria. In addition, during the design process, it may be required to change the size of the initially chosen cross-section for reasons beyond the DM's control. Therefore, it is necessary to work with a space of valid solutions rather than a single optimal solution found by the FF.  
The process proposed in this work consists of searching for a near-optimal solution within the solution space offered by SPEA-2 by means of additional specific constraints incorporated by the DM, in a two-step process:  
* In Step 1, the field of solutions is limited by incorporating the maximum initial bending stress that cannot be exceeded in the study area. This value must be lower than the design strength of the material, maintaining the structural reserve necessary to resist external loads, and considering the rheological phenomena of the material and the multilayer system action [37]. In this research, _Eucalyptus globulus_ laths GL45 [7; 38] and a maximum initial bending stress of 33 MPa were considered. This constraint provides a set of valid cross-section sizes for the DM to choose from.
* In Step 2, once the cross-section size is chosen by the DM, a new optimisation process is run in which the algorithm offers a field of near-optimal solutions for the DM to choose according to other non-programmed issues, such as end distances or aesthetic aspects. Step 2 deals only with the topologic genes once the size genes become fixed.
## 3 Current Developments
### Data Quality Analysis
#### Evaluation of Points of Interest (POI) and Other Features  
When considering OSM for navigation and routing purposes, it is important to implement an exact transformation of an address or textual description of a place into a geographic location. This process, referred to as geocoding, was investigated in more detail by [PERSON] [98], who compared the results of the geocoding functionality in Google Maps with the results gathered from OSM. Nearly all requests on the municipal, street and, in particular, house number level were classified as not sufficient for detailed spatial analysis purposes in OSM, highlighting one of the most profound caveats of the project. [PERSON] _et al_. [99] showed similar results with regards to address information in their data comparison analysis of point features in OSM and other datasets.  
Other studies compared OSM land cover features, such as land use or natural areas, for several countries to pseudo ground-truth data [20, 100]. The analysis investigated spatial sample point characteristics of the polygons to retrieve results about cases where features are under- or over-represented(in terms of the number of points used to represent a polygon feature). Furthermore, the distance between the polygons' adjacent points was computed for quality measurements. [PERSON] _et al_. [101] manually compared the spatial coverage currency and ground-truth positional accuracy of OSM in comparison to Google Maps and Microsoft Bing Maps, revealing no clear pattern in favor of any of the tested sources.  
POI features take another major role in VGI data sources, such as OSM. The collection of untraditional places of interest by volunteers, not available in governmental or proprietary datasets, drives the interest of many researchers in this domain. [PERSON] _et al_. [102] compared POI from Navteq and Yelp with the data collected in OSM for London (UK) and Rome (Italy) and found a highly accurate correlation in terms of geographic position. [PERSON] _et al_. [103] used a different approach to POI analysis and tried to map different community engagements based on the contributed POI data in OSM. The results showed that spatially clustered communities produce a higher quality of coverage than those with looser geographic affinity. In a different study, the spatial-semantic interaction of OSM POI was investigated in more detail [104]. The authors presented a semantic similarity measure that can be used to support tools and contributors in collecting and cleaning up POI data. Lastly, a study that investigated the completeness of POI in OSM in the U.S. showed that, in contrast to prior findings about the road network, the imported data from the Geographic Names Information System (GNIS) database was subsequently updated by the OSM community [105].
###### Abstract  
In this work, mesoporous silicas with two types of mesoporous structures were synthesized and functionalized with sulfonic acid groups: MCM-41-SO\({}_{3}\)H (honeycomb-like hexagonal structure) and MSU-2-SO\({}_{3}\)H (three-dimensional porous structure with wormhole pores). The synthesized materials were characterized by X-ray diffraction, transmission electron microscopy, scanning electron microscopy, nitrogen adsorption-desorption, Fourier-transform infrared spectroscopy, \({}^{29}\)Si solid-state nuclear magnetic resonance spectroscopy, and elemental analysis. The obtained functionalized materials were evaluated as sorbents for strong cation-exchange solid-phase extraction (SPE) to determine their efficiency in the adsorption and desorption of tropane alkaloids (atropine and scopolamine). The loading solvents, loading volume, analyte concentration, and elution volume were studied, using 50 mg of both materials. Analyses were carried out by ultra-high performance liquid chromatography coupled to triple quadrupole tandem mass spectrometry. The synthesized MCM-41-SO\({}_{3}\)H material presented the highest recovery efficiency and has proven to be a promising sorbent for strong cation-exchange SPE of atropine and scopolamine in aqueous media. The high degree of functionalization of MCM-41-SO\({}_{3}\)H and the high accessibility of the sulfonic groups for the target analytes, due to the regularity and uniformity of their pores, maximize the contact between the alkaloids and the sorbent, favoring efficient adsorption.  
Keywords:atropine; scopolamine; functionalized mesoporous silica; MCM-41-SO\({}_{3}\)H; MSU-2-SO\({}_{3}\)H; strong cation-exchange solid-phase extraction +
Footnote †: journal: Applied Sciences
# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 2 OFDM Signal Model  
The system model of MC for the OFDM system is shown in Figure 2. It consists of an adaptive OFDM transmitter, a receiver with statistics-based MC, ML-based MC, and DL-based MC. The transmitter can adjust its baseband modulation format and the number of subcarriers according to the requirement of the data rate and the available CSI. The signal is transmitted over a frequency-selective fading channel. This channel introduces all kinds of impairments into the transmitted signal, including timing, frequency, and phase offsets. The receiver consists of an MC system pre-processing block and selection of a proper MC algorithm. In the following subsections, we provide the mathematical framework of the OFDM signal for MC.  
The discrete baseband OFDM samples \(d_{m}[n]\) of the \(m\)th OFDM symbol, obtained by \(N\)-point inverse discrete Fourier transform (IDFT), which can be written as  
\[d_{m}[n]=\sum_{k=0}^{N-1}D_{m}[k]e^{j2\pi kn/N},\quad 0\leq n\leq N-1, \tag{1}\]  
Figure 1: The organization of the paper.  
where \(N=\rho_{s}\times N_{d}\), \(\rho_{s}\) is the oversampling factor, \(N_{d}\) is the number of data subcarriers, and \(D_{m}[k]\) is the baseband modulated oversampled data obtained by zero-padding the baseband modulated information, i.e., M-PSK/M-QAM and denoted by \(\hat{D}_{m}[k]\). Thus, \(D_{m}[k]\) is given by  
\[D_{m}[k]=\begin{cases}\hat{D}_{m}[k]&0\leq k\leq N_{d}/2-1\\ \hat{Z}_{0}&N_{d}/2\leq k\leq N_{d}(\rho_{s}-1/2)-1\\ \hat{D}_{m}[k]&N_{d}(\rho_{s}-1/2)\leq k\leq N-1,\end{cases} \tag{2}\]  
where \(\hat{Z}_{0}\) is a vector of zeros of length \(N_{d}(\rho_{s}-1)\). To combat the effect of intersymbol interference (ISI), a cyclic-prefix (CP) of \(N_{cp}\) samples from the end of the OFDM symbol are added at the beginning of the OFDM symbol before the transmission. The transmitted baseband OFDM symbol \(\tilde{d}_{m}[n]\) of length \(N+N_{cp}\), with CP is then given by  
\[\tilde{d}_{m}[n]=\begin{cases}d_{m}[n+N]&-N_{cp}\leq n\leq-1\\ d_{m}[n]&0\leq n\leq N-1.\end{cases} \tag{3}\]  
After passing through a frequency-selective fading channel with impulse response \(g[l]\) of length \(L\), the received baseband OFDM samples of the \(m\)th OFDM symbol are given by  
\[x_{m}[n]=e^{(j2\pi\epsilon n/N+\phi)}\sum_{l=0}^{L-1}g[l]\tilde{d}_{m}[n-l- \tau]+\omega[n],\quad 0\leq n\leq N_{s}-1 \tag{4}\]  
where \(\epsilon\) is the normalized carrier frequency offset (CFO), \(\phi\) is the phase offset, \(\tau\) is the symbol timing offset (STO), \(N_{s}\) length of the OFDM symbol with CP, \(N_{s}=N+N_{cp}\) and \(N_{cp}\geq L\), and \(\omega[n]\) is the AWGN with zero mean and variance \(\sigma_{\omega}^{2}\).
## 5 Conclusions  
We designed a new set of codes called Hadamard Aperiodic Interval codes that are able to produce images with superior SNR with a single extended transmit event. HAPI codes are near orthogonal, so they can be used for parallel transmission in multiple-input multiple-output systems. These codes are also very sparse, as they contain many long intervals of zeros.  
Our simulation results show that imaging with the HAPI-SA method produces images with superior signal-to-noise ratio, contrast-to-noise ratio, and contrast-to-speckle ratio compared to traditional SA imaging and coherent plane wave compounding methods. We were able to achieve a maximum SNR increase of 36 dB over traditional SA, close to the predicted maximum from our theoretical calculations.  
HAPI codes are among the first codes to be reported which aim to achieve a multiple-input, multiple-output paradigm, where multiple codes are transmitted in parallel with carefully designed auto- and cross-correlation properties. They show promise to improve sensitivity and achieve high contrast-lesion detection capabilities at moderately high frame rates compared to synthetic aperture ultrasound imaging methods. HAPI codes used with sparse 2D arrays show promise for fast 3D volumetric imaging with transmit and receive focusing everywhere throughout the volume.  
Conceptualization, R.J.Z.; Data curation, T.K.; Formal analysis, R.J.Z. and T.K.; Funding acquisition, R.J.Z.; Investigation, T.K.; Methodology, T.K. and R.J.Z.; Project administration, R.J.Z.; Resources, R.J.Z.; Software, T.K.; Supervision, R.J.Z.; Validation, T.K.; Visualization, T.K.; Writing--Original draft, T.K. and R.J.Z.; Writing--Review and editing, T.K. and R.J.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC AACASBE 567531, NSERC CIRRP 462510, and NSERC RGPIN 355544), Alberta Innovates (AB Innovat CASBE 212200391, AB Innovat AICEC 202102269), the Canadian Institutes of Health Research (CIIR PS 168936, CIHR CPG 134739), and the National Institutes of Health (NIH EITTSCA R21 EYO33078).  
This study did not involve any humans or animals.  
Visualsonics, which, however, did not support this work. [PERSON] does not declare any conflicts of interest.
# Research on Wind Field Characteristics Measured by Lidar in a U-Shaped Valley at a Bridge Site
## 2 Topography Description and Field Measurement Setup
### Lidar System  
Compared with traditional wind measurement means, such as anemometers and sodar, the Wind3D 6000 3D scanning wind measuring lidar has the advantages of higher measurement accuracy, higher spatial and temporal resolution, and lower detection blind area. An optical scanning mirror is equipped with high-pointing accuracy, 3D scanning detection can be realized, and the maximum detection radius can be up to 6 km. In addition, the lidar has the advantages of small size, lightweightedness, and low power consumption, which makes it easy to build stations and transport in the field, as shown in Figure 4.  
The Wind3D 6000 3D scanning wind measuring lidar is a coherent wind measuring lidar system, which is mainly composed of a laser transmitting and scanning subsystem, a receiving subsystem, a real-time signal processing subsystem, a communication subsystem, etc., as shown in Figure 5. The radial velocity is calculated through the principle of optical pulse coherent Doppler frequency shift detection. On the other hand, based on the assumption of the horizontal homogeneous wind speed [28; 29], the boundary layer wind profile is inversed by the microbeam system, with visibil  
Figure 4: Diagram of the lidar.  
Figure 3: Terrain contour line.  
The lidar emits an eye-safe invisible light with a wavelength of 1550 nm and transmits laser pulses into the atmosphere at a rate of 1-10 Hz, which is user-definable. The Wind3D 6000 lidar equipped an optical scanner enabling scans from 0-360\({}^{\circ}\) azimuth angles and \(-\)90-270\({}^{\circ}\) pitch angles, and there are up to 398 user-defined range gates at 15 m spacing, with the minimum range at 45 m and the maximum range at 6000 m. In addition, various scan models are available; for example, range-height indicator (RHI), plan position indicator (PPI), Doppler beam swinging (DBS), and so on. RHI is used in this paper to obtain wind speed and direction profiles, as detailed in Table 1.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
## 2 Methodology
### Deep Residual Networks  
ResNet is a neural network structure [30]. In theory, the deeper the layers of the network, the more complex the feature the network can extract. So, the deeper network should perform better than the shallow network. However, in practice, deeper networks did not achieve better results. For a model with many layers, the training accuracy of the model layers decreases with the depth, so deeper layers may perform worse than shallow ones. Besides, the deeper layers have gradient disappearance and gradient explosion problems. ResNet solves such problems by introducing a residual network (Figure 1). The residual network replaces the learning target \(H(x)\) with the difference between \(H(x)\) and input \(x\), expressed as \(F(x)=H(x)-x\). Therefore, the later training goal is to make residual \(F(x)\) converge to 0.  
When the input \(x\) is convolved, if the dimensionality does not change, the output \(F(x)\) and \(x\) have the same dimensionality. The output of the residual block is the sum of \(F(x)\) and \(x\).  
\[H(x)=F(x)+x \tag{5}\]  
If the dimensionality changes, the output \(F(x)\) has a different dimension from \(x\). It is necessary to adjust the dimensionality of \(x\) by convolving it with \(1\times 1\) and then adding it to \(F(x)\).  
\[H(x)=F(x)+W(x) \tag{6}\]  
Figure 1: Residual network.  
ResNet consists of 5 convolutional layers and 1 integrated module. All convolutional layers consist of different numbers of stacked residual modules, and the integrated module consists of fully connected layers, pooling layers and classification modules. Each residual module has some hidden layers. Therefore, ResNet has different numbers of layers, such as 18, 34, 50, 101 and 152 layers. The ResNet with different layers has different number of residual blocks and different parameters in the residual blocks for each convolutional layer.  
As Figure 2 shows, ResNet has two residual structures. The residual structure on the left is for networks with fewer layers, such as ResNet-18 and ResNet-34, and the one on the right can effectively reduce the network parameters and operations for networks with more layers, such as ResNet-101 and ResNet-152.  
ResNet resolves the degeneracy problem of the network, making the network better trained and easier to optimize. It deepens the network and achieves a better classification effect and better application in the field of computer vision.
## 3 Mobility-Aware Duty Cycling
### Mobility Pattern  
When considering the mobility pattern used, we seek to study an environment where all nodes are not treated equally, such as a disaster recovery situation where reaching each node directly is not possible. Many studies aim to have the MSN pass every node and whilst such a mobility pattern has merit, it is clear that this is not always possible in a real-world scenario. Therefore, in our tests we have implemented a circular mobility pattern for the MSN, moving around the periphery of the network.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 5 Parametric Analysis of Main and Sub\(-\)Frame Collisions
### Influence of Main and Substructure Spacing
#### 5.3.1 Influence on Collision Forces  
Table 7 shows the change of the peak collision force of the main and subframe with the gap ratio, from which it can be seen that the change of the collision force is more obvious with the increase of the spacing between the two. The peak collision forces vary significantly under the influences of different seismic waves. Specifically, the peak collision force under the El Centro wave is 66.74 \(\times\) 10\({}^{3}\) kN. Under the Taft wave, it is 49.13 \(\times\) 10\({}^{3}\) KN. Under the artificial wave, it is 51.88 \(\times\) 10\({}^{3}\) kN. From the shown in Figure 17, it can be seen that the collision force does not decrease with the increase of the spacing between the main and subframe, and the peak collision force does not appear when the spacing is minimum.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Gap Ratio** & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** \\ \hline El Centro & 47.76 & 58.89 & 39.12 & 26.87 & 51.39 & 56.17 & 66.74 \\ Taft wave & 37.23 & 48.77 & 12.05 & 28.46 & 49.13 & 23.26 & 17.11 \\ Artificial wave & 51.88 & 41.93 & 33.40 & 27.74 & 48.98 & 41.34 & 24.21 \\ \hline \end{tabular}
\end{table}
Table 7: Influence of gap ratio on collision forces.  
Figure 16: The influence of seismic acceleration variation on the top floor acceleration of the third subframe: (**a**) El Centro wave; (**b**) Taft wave; (**c**) Artificial wave.