esa-sceva/satcom-chunk-collection huggingface dataset:
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 2 The NSBAS Processing Chain Used in the FLATSIM Service
### Enhanced Spectral Diversity Correction  
The S-1 TOPSAR default acquisition mode [24] for IW Sentinel-1 data, while enhancing the swath width and then the revisit time, required to adapt the InSAR processing chains to take into account the antenna forward-backward sweeping inside each burst, and the division of the full wide-swath into three overlapping sub-swaths. Slight inaccuracies in azimuth positioning of the images also result in phase jumps across bursts that are accounted for using the enhanced spectral diversity method (ESD, [46]), while subswath merging is facilitated by the accurate timing of subswath SAR SLC as provided before data and a high quality ESD correction.  
Here, enhanced spectral diversity is computed on the selected network of interferograms, using forward and backward SLCs of all burst overlaps. The SLCs overlaps are coregistered using the same function as described above, and differential backward-forward interferograms are multi-looked by a factor 64 \(\times\) 16, using colinearity [47] in the multi-looking operation to enhance the signal to noise ratio. The differential forward-backward wrapped phase  
Figure 2: (**a**) perpendicular baseline versus time for the Sentinel-1 descending track D079 on the Afar region (red dot indicates the primary date); (**b**) coregistration network; (**c**) histogram showing the distribution of time interval between consecutive acquisitions; (**d**) small-baseline interferogram network; (**e**) histogram showing the distribution of temporal baselines between acquisitions used for the calculated interferograms.  
screens for all overlaps are then fitted by a four-parameters function: a constant, two linear coefficients describing the range and azimuth change, and a coefficient describing the average linear phase change in azimuth within the bursts. For long tracks, a fifth coefficient, modeling a quadratic phase change across bursts in azimuth was also found necessary. To obtain consistent and stable parameter values for each secondary image, all these parameters, estimated on interferograms, are inverted within the network by a least-squares minimization with outliers removal (Figure 3, left). Using these estimated phase screens, a sawtooth-shaped phase screen is subtracted to the original interferograms to cancel phase jumps across burst boundaries.  
The above strategy is applied independently to each sub-swath, and all sub-swaths are then mosaicked, correcting for any phase jumps between the sub-swaths by computing the phase difference in the overlap between sub-swaths, and inverting for these phase differences within the interferometric network (Figure 3, right).
# 5G-NR Physical Layer-Based Solutions to Support High Mobility in 6G Non-Terrestrial Networks
###### Abstract  
Non-terrestrial network (NTN) systems can offer wide area coverage for applications requiring high mobility, which is expected in the sixth generation (6G) of telecommunication systems. This paper proposes a high-mobility support system based on the 5G-NR physical layer components for NTN connectivity. In this paper, we propose the optimization of 5G-NR numerologies and the impact of various modulation and coding schemes (MCS), 3 GPP NR-NTN channel models, and MIMO/beamforming schemes with link-level simulation under pilot-aided-based perfect and DM-RS-based practical channel estimation at stationary UE and high mobility of 500 km/h, respectively. This paper also develops a link-level simulation of the 5G-NR physical downlink shared channel (PDSCH) under the 3 GPP NR-NTN tapped delay line (TDL) channel model to support UE mobility up to 500 km/h. The bit error rate (BER), maximum achievable throughput (Mbps), and spectral efficiency (bps/Hz) are analyzed for the 5G-NR-based potential elements to be utilized in the evolution of NTN. Furthermore, the denser DM-RS symbol pattern is proposed for utilization in channel estimation to support high mobility, as simulation results prove their capability of fast decoding while using the front-loaded symbol structure. The simulation results show that the large 5G-NR numerologies, such as 120 kHz and DM-RS-based channel estimation, support the high UE mobility by providing high link reliability and the maximum achievable throughput of 368.832 Mbps and spectral efficiency of 3.68 bps/Hz under 64-QAM for TDL-E (LOS) channel model, which can also be a potential solution to support transonic speed mobility in the NTN of 6G services.  
3 GPP; 5G-NR; 6G; beamforming; channel model; DM-RS; MIMO; non-terrestrial networks (NTN); numerology +
Footnote †: journal: Advances and  
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Footnote †: journal: Advances and  
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Footnote 0: 0203 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))).
# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 4 Results
### Performance Analysis
#### 4.2.2 Maximum Embedding Capacity  
The maximum embedding capacity refers to the maximal amount of supplementary or hidden data that can be inconspicuously integrated into a carrier medium. It represents the upper limit of information that can be embedded within the carrier while maintaining a balance between the additional payload and the integrity of the original content.  
Figure 6 shows that the CT modality exhibits the highest maximum embedding capacity of 0.52, indicating a more significant potential for concealing additional data within the images produced using CT scans without substantially compromising the original image quality or being easily detectable by observers or analysis tools. This suggests that CT images have a comparatively larger capacity for steganographic data hiding than the other modalities listed.  
Conversely, the X-ray modality demonstrates the lowest maximum embedding capacity of 0.16, indicating a smaller potential for hiding additional data within X-ray images. X-ray images have a more limited capacity for steganographic data embedding than the other modalities listed in the figures.  
However, it is important to note that while the maximum embedding capacity quantifies the potential for data hiding within these modalities, the clinical relevance and ethical considerations must be carefully weighed, as altering medical images can have significant implications for diagnostic accuracy and patient care.  
Figure 5: Visual image quality measured in PSNR.
# Effect of Stirrup on Bond Strength Degradation in Concrete Cracked by Expansion Agent Filled Pipes
## 3 Experimental Results
### Results of the Pull-Out Test
#### 3.2.2 Bond Stress and Slippage  
According to the employed short bond length, we can first assume that the bond stress is uniformly distributed along the bonded section. Therefore, Equation (2) estimates those stresses with the pull-out load. Secondly, the tested rebar remains in its elastic domain due to a relatively small pull-out load. Consequently, the measured slip estimated the local slip in the focus bond length because the slip at the free-end and loaded-end are almost identical.  
\[\tau=\frac{P}{\pi dl} \tag{2}\]  
where \(P\) is the pull-out load, \(d\) is the diameter of the rebar, and \(l\) is the bond length.  
Figure 11 shows a comparison of bond stress-slip relationships for specimens with the similar induced crack width. The results indicated almost linear relationships between load and slip till the bond strength. Then, the load continuously drops with the increase of slip. In addition, no significant correlation was found between the slip at maximum load and the induced crack width. However, in the same induced crack width, the confined specimen gives higher slip at bond strength.  
Moreover, stirrup can reduce bond strength degradation due to induced cracks and impact post-peak behavior. The bond stress in specimens without a stirrup (S0) suddenly drops after its peak because of the abrupt splitting of the concrete cover. However, in a specimen with a stirrup, this splitting crack was controlled. Thus, those specimens show ductile behavior.
## 3 Structural Damage under MS-AS Sequences  
The damage index (\(DI\)) is used to simulate structural damage. In this study, four kinds of structural damage are considered, including the mainshock-induced damage for the initial system, i.e., \(DI^{\text{MS}}\), the AS-induced incremental damage for the mainshock-damaged system, i.e., \(DI^{\text{AS}|\text{MS}}\), the accumulative damage caused by MS-AS sequences, i.e., \(DI_{1}{}^{\text{MS-AS}}\), and the ratio of between \(DI^{\text{AS}|\text{MS}}\) and \(DI^{\text{MS}}\), i.e., \(DI_{2}{}^{\text{MS-AS}}=DI^{\text{AS}|\text{MS}}/DI^{\text{MS}}\). In the following studies, \(DI_{1}{}^{\text{MS-AS}}\) is related to \(IM^{\text{MS}}\), \(IM^{\text{AS}}\), and \(IM_{1}{}^{\text{MS-AS}}\), and \(DI_{2}{}^{\text{MS-AS}}\) is related to \(IM_{2}{}^{\text{MS-AS}}\).  
To determine \(DI_{1}{}^{\text{MS-AS}}\) and \(DI_{2}{}^{\text{MS-AS}}\), the structural damage index proposed by [PERSON] [44] is adopted herein. The Park-Ang damage index is a linear combination of maximum deformation response and hysteretic energy dissipation. Therefore, it increases monotonically so that it is beneficial to quantify the accumulative damage caused by MS-AS sequences [15]. The Park-Ang damage index has been modified by [PERSON] et al. [45], and the modified damage index, \(DI\), is expressed by  
\[DI=\frac{u_{max}-u_{y}}{u_{u}-u_{y}}+\beta\frac{E_{H}}{F_{y}u_{u}} \tag{1}\]  
where \(u_{y}\) and \(u_{u}\) are the yielding and ultimate displacements of a specific SDOF system under monotonic loading, respectively; \(u_{\text{max}}\) and \(E_{H}\) are the maximum displacement and absorbed energy of the system under earthquake loading, respectively; and \(\beta\) is the energy consumption factor, which is taken as \(\beta=0.15\) herein following [PERSON] et al. [15].  
Figure 3: Calculated displacement-related IMs for the selected MS and AS ground motions.  
Figure 2: Calculated velocity-related IMs for the selected MS and AS ground motions.
# Electronic Cigarettes' Toxicity: From Periodontal Disease to Oral Cancer
## 4 Chemical Composition of e-Liquids and Vapors and the Implications in Oral Carcinogenesis
### Flavoring Agents  
The number of e-cigarette brands and flavored products is impressive (over 8000 different flavorings). Many flavoring ingredients are labeled as \"generally recognized as safe\" (by the Flavors Extracts Manufacturers Association), but their evaluation was performed on food products (at ingestion), and not for inhalation toxicity. Despite the banning of certain flavors from conventional cigarettes, these flavors are still used in all the other products, including e-liquids. [PERSON] et al. make an extensive presentation of the main e-cigarette flavorings (Table 1) and their toxic potential [65; 66].  
In another study, [PERSON] et al. identified a positive correlation between the cytotoxicity of different e-liquids and the total number and total concentration of flavor chemicals [67].  
Flavoring chemicals also affect the formation of free radicals. [PERSON] et al. found that compounds like non-phenolic terpen (linalool, dipentene, citral) contribute to a high level of free radical production, probably due to their autooxidation process during which hydroperoxides form. These hydroperoxides can rapidly degrade into allyaxyl and carbon-centered radicals. Ethyl maltol and maltol also promote the generation of radicals, by interaction with iron and copper. On the other hand, in the same study, some flavors inhibited the formation of free radicals. The best results were obtained for ethyl vanillin [66].  
Figure 5: Effects of nicotine on growth factors receptors.  
Some flavors present in e-liquids are associated with higher reactive oxygen species production rates, and more severe cytotoxicity and inflammatory responses. Pyrazine derivatives are found in chocolate and roasted nut flavors. The pyrazine moiety can interact with p53 tumor suppressor protein, estrogen receptor, and the endothelial growth factor [27]. The p53 gene inactivation is linked to several types of cancers, and p53 is considered \"the guardian of the genome\" due to its importance in regulating intracellular functions [68].  
Aldehydes, like vanillin or cinnaldehyde, generate oxidative stress, cause protein carbonylation of extracellular matrix, and promote DNA damage responses. As a result, dysregulated repair and impaired wound healing will occur [27; 42]. DNA damage appears due to the high reactivity of aldehydes, that bind covalently to macromolecules (DNA and proteins) and cause DNA adducts. A positive correlation between high DNA adduct levels in target organs and elevated cancer risk was observed [43].  
Furan derivatives (found in sweet and fruity flavors) caused damage to the nasal mucosa, while furfural derivatives exhibited tumorigenic activity in mice [27].
# Review of Advances in Active Impulsive Noise Control with Focus on Adaptive Algorithms
###### Abstract  
Mitigating low-frequency noise in various industrial applications often involves the use of the filter-x least mean squares (FxLMS) algorithm, which relies on the mean square error criterion. This algorithm has demonstrated effectiveness in reducing noise induced by Gaussian noise within noise control systems. However, the performance of this algorithm experiences significant degradation and does not converge properly in the presence of impulsive noise. Consequently, to uphold the stability of the ANC system, several robust adaptive algorithms tailored to handle shock noise interference have been introduced. This paper systematically organizes and classifies robust adaptive algorithms designed for impulse noise based on algorithmic criteria, offering valuable insights for the research and application of pertinent active impact noise control methods.  
active impulsive noise control; noise reduction; adaptive algorithm +
Footnote †: journal: Applied Sciences
# Automatic Colorization of Anime Style Illustrations Using a Two-Stage Generator
## 4 Experiments and Analysis  
### Data Set  
Danbooru [28] is a well-known data set for anime style illustrations; however, it not only contains a lot of noise but also contains strokes to aid the ratio of the drawing in the image which are not part of actual artwork. Thus, we made a crawler to gather illustration data from shuushuu-image-board [29]. After acquiring the data set, we manually inspected image that might cause adverse effect on the learning. After the screening, we acquired total of 733,322 color anime style illustrations and 546 pairs of line-arts and its colorized version of artwork. Manually filtered images, which are considered as noise, are black and white image, high/low key image, dimension less than 512 \(\times\) 512, image with skewed color, doodles, and image containing photos of real objects.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
###### Abstract  
Biochar as a stable carbon-rich material shows incredible potential to handle water/wastewater contaminants. Its application is gaining increasing interest due to the availability of feedstock, the simplicity of the preparation methods, and their enhanced physico-chemical properties. The efficacy of biochar to remove organic and inorganic pollutants depends on its surface area, pore size distribution, surface functional groups, and the size of the molecules to be removed, while the physical architecture and surface properties of biochar depend on the nature of feedstock and the preparation method/conditions. For instance, pyrolysis at high temperatures generally produces hydrophobic biochars with higher surface area and micropore volume, allowing it to be more suitable for organic contaminants sorption, whereas biochars produced at low temperatures own smaller pore size, lower surface area, and higher oxygen-containing functional groups and are more suitable to remove inorganic contaminants. In the field of water/wastewater treatment, biochar can have extensive application prospects. Biochar have been widely used as an additive/support media during anaerobic digestion and as filter media for the removal of suspended matter, heavy metals and pathogens. Biochar was also tested for its efficiency as a support-based catalyst for the degradation of dyes and recalcitrant contaminants. The current review discusses on the different methods for biochar production and provides an overview of current applications of biochar in wastewater treatment.  
biochar; thermal conversion; modification; adsorption; wastewater treatment +
Footnote †: journal: Applied Chemistry, Volume 1
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
## 3 Orbit Determination under Different Scenarios
### Duty Cycling and Satellite Numbers
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Elevation Mask [Degree]** & **Mean Integer Number of Satellites** & **Percentile of Valid SPP Solutions** \\ \hline
5 & 9 & 99.9\% \\ \hline
15 & 7 & 99.1\% \\ \hline
25 & 6 & 82.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean number of satellites used in SPP and percentiles of valid SPP solutions applying different elevation masks. Note that the mean satellite numbers are given as rounded values. The real values of the mean satellite numbers amount to about 8.5, 7.4, and 5.7 for an elevation mask of 5, 15, and 25 degrees, respectively. Figure 5: (a) Distribution of the satellite numbers per epoch (observing at least P1 and P2 observations) applying different elevation mask angles and (b) the corresponding 3D orbital errors of the phase-based reduced-dynamic orbits with a duty-cycle of 100% and mean satellite numbers of 7 and 6.  
duty-cycles, however, observing all or almost all visible satellites would be essential to maintain a low RMSE at this level. With a duty-cycle of 20%, decreasing the mean satellite number to 6 would increase the RMSE to about 5 cm.
## Materials and Methods
### Processing of Measured Data  
Signal processing of IMU data during STS motion analysis was performed using MATLAB Release 2016a (MathWorks, Release 2016a, Natick, MA, USA). The coordinate system in the experimental settings is shown in Figure 2. First, the segment orientation and joint angles of each segment were calculated as motion data using IMU data and the same protocol as our previous study [15]. The calculated orientations of body segments and joint angles are shown in Figure 2. The direction of the axis of each IMU was determined as the line of each segment.  
The joint angle was defined as 0\({}^{\circ}\) for an anatomically upright posture. The vertical axis in the sagittal plane was used as the reference axis; thus, it was defined as 0\({}^{\circ}\) to calculate the inclination angle. Joint angles were calculated using Equation (1), where \(\theta\) and \(\phi\) correspond to the symbols used in Figure 2.  
\[\begin{split}\theta_{1}=\varphi_{1}\\ \theta_{2}=180+\varphi_{2}-\varphi_{1}\\ \theta_{3}=\varphi_{3}-\varphi_{2}\\ \theta_{4}=\varphi_{4}-\varphi_{3}\end{split} \tag{1}\]  
Next, the ground reaction forces were calculated using data from the WBBs (body mass acting on the WBBs, kg) under the foot and hip. Thus, the force (N) in each trial was calculated using Equation (2). Then, these motion and force data were inputted to the BoB package with the modified musculoskeletal  
Figure 2: Coordinate system (**left**) and definition of orientations and angles (**right**). Calculated orientations of body segments and joint angles from each IMU are shown, where \(\theta\) and \(\phi\) correspond to the symbols in Equation (1).  
models for pregnant women in order to calculate the joint torque. Finally, the optimization process in BoB was performed using the joint torque to estimate the muscle torque in the model.  
\[F=ma \tag{2}\]  
Here, \(F\) is the reaction force (N), \(m\) is the body mass measured by the WBB (kg), and \(a\) is the acceleration due to gravity (m/s\({}^{2}\)).
## 2 Methodology of Research, Radio Planning, and Design of 5G Network
### Capacity Planning  
In this paper, capacity planning is focused on calculating the data rate and throughput. These parameters are measures that show the bits in data in the transmission process that can be sent per unit time, measured in bits per second. The maximum downlink and uplink data rates supported by the UE are calculated according to the user device's mean category. For the design of a 5G NR network, the approximate data rate and throughput for a determined number of carriers in a bandwidth or bandwidth combination are calculated using the expressions in (14) and (15) [15], with assumptions of throughput services per use case (eMBB) and modulation order being max/min for LOS/NLOS scenarios:  
\[\begin{split}\text{Data Rate(Mbps)}=10^{-6}\sum_{j=1}^{j}\Bigg{(} V_{Layers}^{(P)}\ Q_{m}^{(j)}\times f^{(j)}\times R_{max}\times\frac{N_{PRB}^{BW(j), \mu}\ x\ 12}{T_{S}^{\mu}}\ \Big{(}1-OH^{(j)}\Big{)}\Bigg{)},\\ \text{where}\\ \text{P = Number of carrier components}\\ \text{Rmax}=948/1024\\ V_{Layers}^{(P)}=\text{Maximum number of MIMO layers}\\ Q_{m}^{(j)}=\text{Modulation order}\end{split} \tag{14}\]  
Figure 3: Target area of business/dense area of north zone in Quito city, 5G planning.  
\(f^{(j)}=\) Scaling factor,  
\(\mu=\) Numerology  
\(T_{S}^{h}=\) OFDM Symbol  
\(N_{PRB}^{BW(j),h}=\) Maximum number of resource blocks PRB (according to 3 GPP 38.104) for selected bandwidth, frequency range, and numerology per selected carrier.  
\(OH^{(j)}=\) Overhead  
BW = Bandwidth.  
Number of user per use case DL/UL = Data Rate (Mbps)/DL/UL Throughput service per user (Mbps).
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 5 Experimental Setup of Photodarkening
### Refractive Index Measuring Device  
The RIC induced by PD and bleaching is relatively small. Therefore, interferometric methods must be used to obtain sufficient resolution. Therefore, the mainstream device for measuring RIC in PD experiment is based on the modal interference method and the absorption spectrum measurement device [28]. The core fusion is intentionally offset, which is approximately equal to the core radius of the YDF, as shown in Figure 9.  
At the first splice, both LP\({}_{01}\) and LP\({}_{11}\) modes are excited in the YDF sample. As the probe light propagates along the YDF, the phase difference occurs between the two transverse modes due to the different propagation constants of the two transverse modes. Interference occurs when the light is coupled back to the single-mode fiber at the second splice, when a white light source (spectral range from 400 nm to 1400 nm) is used as the detection light, periodic oscillation of the transmission spectrum is caused. The spectrum with a wavelength between 1050-1100 nm is as shown in Figure 10.  
Figure 10: Observed phase shift in the modal interference fringes at various PD loss levels and corresponding times. Adapted with permission from [28] © The Optical Society.  
Figure 9: Experimental setup for RIC measurement. YDF, Yb-doped fiber; BPF, bandpass filter; WLS, white light source; OSA, optical spectrum analyzer. Adapted with permission from [28] © The Optical Society.  
\[\Delta n_{\rm PD}=\frac{\lambda}{2\pi l(\eta_{1}-\eta_{2})}\Delta\phi \tag{2}\]  
The phase shift of the modal interference fringe can be calculated by \(\Delta\phi=2\pi\Delta T/T\). The change of refractive index can be obtained by introducing \(\Delta\phi\) into formula PD. Where \(\eta_{i}(i=1,2)\) are fractions of power in the core for the LP\({}_{01}\) and LP\({}_{11}\) modes. The fractions are calculated from the mode field distribution by numerically solving the fiber characteristic equation. For both fibers used in the experiment, \(\eta_{1}\) ~0.78 and \(\eta_{2}\) ~0.1 in the spectral range near 1080 nm.