esa-sceva/satcom-chunk-collection huggingface dataset:
# Epigenetic Signatures of Dental Stem Cells: Insights into DNA Methylation and Noncoding RNAs
###### Abstract  
Tooth development (odorotogenesis) is regulated by interactions between epithelial and mesenchymal tissues through signaling pathways such as Bone Morphogenetic Protein (BMP), Wingless-related integration site (Wnt), Sonic Hedgehog (SHH), and Fibroblast Growth Factor (FGF). Mesenchymal stem cells (MSCs) derived from dental tissues--including dental pulp stem cells (DPSCs), periodontal ligament stem cells (PDLSCs), and dental follicle progenitor cells (DFPCs)--show promise for regenerative dentistry due to their multilineage differentiation potential. Epigenetic regulation, particularly DNA methylation, is hypothesized to underpin their distinct regenerative capacities. This study reanalyzed publicly available DNA methylation data generated with Illumina Infinium HumanMethylation450 BeadChip arrays (450K arrays) from DPSCs, PDLSCs, and DEFPCs. High-confidence CpG sites were selected based on detection \(p\)-values, probe variance, and genomic annotation. Principal Component Analysis (PCA) and hierarchical clustering identified distinct methylation profiles. Functional enrichment analyses highlighted biological processes and pathways associated with specific methylation clusters. Noncoding RNA analysis was integrated to construct regulatory networks linking DNA methylation patterns with key developmental genes. Distinct epigenetic signatures were identified for DPSCs, PDLSCs, and DEFPCs, characterized by differential methylation across specific genomic contexts. Functional enrichment revealed pathways involved in odontogenesis, osteogenesis, and neurodevelopment. Network analysis identified central regulatory nodes--including genes, such as _PAX6, FOXC2, NR2F2, SALL1, BMP7_, and _JAG1_--highlighting their roles in tooth development. Several noncoding RNAs were also identified, sharing promoter methylation patterns with developmental genes and being implicated in regulatory networks associated with stem cell differentiation and tissue-specific function. Altogether, DNA methylation profiling revealed that distinct epigenetic landscapes underlie the developmental identity and differentiation potential of dental-derived mesenchymal stem cells. This integrative analysis highlights the relevance of noncoding RNAs and regulatory networks, suggesting novel biomarkers and potential therapeutic targets in regenerative dentistry and orthodontics.  
cell differentiation; DNA methylation; gastroenterology; guided tissue regeneration periodontal; odontogenesis; precision medicine; RNA untranslated
# Benchmark Sea Trials on a 6-Meter Boat Powered by Kite
## 3 Experimental Set-Up
### Kiteboat Specific Sensors
#### 3.1.1 GPS and IMU  
An Inertial Motion Unit (IMU) combined with a dual antenna GPS receiver provided boat orientations and velocities. This sensor was a VectorNav VN-300 Rugged. In addition to provide boat position and velocity, the dual antenna GPS receiver gave also an accurate heading measurement apart from any magnetic interference.
# Channel and Bit Adaptive Power Control Strategy for Uplink NOMA VLC Systems
###### Abstract  
Non-orthogonal multiple access (NOMA) can be an effective solution to the limited bandwidth of light emitting diodes for visible light communication (VLC) systems to support multiuser communication. The current available works for NOMA VLC systems mainly concentrate on downlinks and the existing power allocation algorithms mainly focus on the channel state information and ignore the influence of transmitted signals. In this paper, we propose a channel and bit adaptive power control strategy for uplink NOMA VLC systems by jointly considering the channel state information and the transmission bit rate. Under this adaptive power control strategy, it is proved that the received signal at the photodiode (PD) receiver constitutes a sizeable pulse amplitude modulation constellation and low-complexity maximum likelihood detection is admitted. The simulation results indicate that our proposed adaptive power control strategy outperforms the gain ratio power allocation scheme, fixed power allocation scheme, and time division multiple access scheme.  
visible light communication (VLC); uplink non-orthogonal multiple access (NOMA); adaptive power control (APC); maximum likelihood (ML) detection Article
## 2 Materials and Methods
### Data Provided by AGEA  
Land Parcel Identification Systems (LPISs), as geodatabases for identifying and monitoring land parcels to manage agricultural subsidies, are characterized by technical specifications that vary from one MS to another, as various types of reference parcel exist. The statement of Art 6.1 of the Commission Reg. No. 796/2004 [3] allows four types of representation of reference parcels: cadastral parcel, agricultural parcel, farmers' block and physical block. Member States that base their LPIS model on the ownership declared by land registers adopt the cadastral parcel model; this model is utilized in Italy [13]. Conse  
Figure 1: Test area, encompassed within the red boundary, chosen for the survey. The location of the test area is provided, at national and regional levels, respectively in the higher and in the lower incepts.  
quently, the cadastral dataset provided to AGEA by the Italian Tax Agency (AE--Agenzia delle Entrate) was used to create reference parcel boundaries over the test area.  
Additionally, the orthophoto acquired in June 2023 by AGEA served as the reference scene over the test area. Indeed, among its mandates, AGEA is also responsible for conducting an aerial photogrammetric survey over the whole national land area, conducted both in visible and near infrared ranges, with an average ground sampling distance (GSD) of 20 cm. Subsequently, AGEA is also responsible for the processing of such data to produce RGBNIR orthophotos at a scale of 1:5000, with a pixel resolution of 20 cm [14].  
Finally, the land-cover map generated annually by AGEA from its ortho-imagery was used as a land-cover reference for the study. As shown in Figure 2, the predominant land-cover types within the region are mixed forests (52.6%), croplands (19.6%), pastures (9.6%), grasslands (7.4%), built-up areas (3.5%), and forage crops (3.4%).
# Multi-Objective Optimization for EE-SE Tradeoff in Space-Air-Ground Internet of Things Networks
## 2 System Model and Problem Formulation
### UAV-Satellite Data Transmission  
Because there are few obstacles between UAVs and the satellite, the propagation loss follows the free space loss model. Taking antenna pattern into account, the radio propagation loss between UAVs and the satellite can be expressed as  
\[L_{us}=\frac{v\sqrt{G_{tx}G_{rx}}}{4\pi r_{\mathrm{c}}d_{us}}, \tag{6}\]  
where \(G_{tx}\) and \(G_{rx}\) are the antenna gain of UAV and satellite, respectively. \(d_{us}\) is the distance between UAV \(u\) and the satellite, which is approximately equal to satellite orbital height \(h_{\mathrm{S}}\). So the gain between UAVs and the satellite can be expressed as  
\[g_{us}=L_{us}^{2}. \tag{7}\]  
Similarly, the data transmission rate between UAV \(u\) and the satellite can be expressed as  
\[r_{us}=W\log_{2}\left(1+\frac{p_{u}g_{us}}{WN_{1}}\right), \tag{8}\]  
where \(N_{1}\) is the noise power spectral density and \(p_{u}\) is the transmit power of UAV \(u\).
## 3 AIS Sensor Data Acquisition and Preprocessing
### Wavelet Threshold De-Noising  
The AIS system obtains ship trajectory information by accessing GPS signals. Owing to the mutual interference and signal attenuation of maritime communication equipment, ship position information may also be affected by noise interference and data distortion.  
For a long time, people often choose Fourier transform to process signals. However, because the function constructed by Fourier transform is periodic sine wave and cosine wave, it can only be applied to those signals with periodic or approximate periodicity but the effect is not very good on those signals with non-periodic or strong local characteristics.  
The wavelet transform developed from Fourier transform can solve the above problems well. It not only retains many advantages of Fourier transform but also improves and develops on the original basis, so that it can process signals in time-frequency domain. The remarkable advantage of wavelet transform is that it can process the signal more subtly and show some characteristics of the signal better. It realizes the requirement of localization and multi-scale analysis of the signal in time-frequency domain. The signal de-noising method developed on the basis of wavelet shows good de-noising effect, which is the perfection and development of Fourier transform in the field of signal processing. Based on this, we choose the wavelet threshold de-noising method based on wavelet analysis theory. The advantage of this method is that the noise is almost completely suppressed and the characteristic peaks of the original signal are well preserved. Moreover, de-noising using soft threshold can minimize the maximum mean square error of the estimated signal, that is, the estimated signal after de-noising is the approximate optimal estimate of the original signal and the estimated signal is at least as smooth as the original signal without additional oscillation. In addition, the method is fast in calculation and has wide adaptability and is the most widely used one of many wavelet de-noising methods.  
The main theoretical basis of wavelet threshold de-noising is as follows. The wavelet transform has a strong decorrelation, which concentrates the energy of the signal in some small wavelet coefficients in the wavelet domain, while the energy of the noise is distributed in the entire wavelet domain. Therefore, after the wavelet decomposition, the amplitude of the wavelet coefficient of the signal is greater than the amplitude of the coefficient of the noise. Wavelet coefficients with larger amplitudes can be considered to be generally dominated by signals, while coefficients with smaller amplitudes are largely noise. Thus, the threshold can be used to preserve the signal coefficients, while reducing the noise to almost zero.
## 5 High Capacity 2 \(\times\) 2 OPS Node with Contention Resolution and Fiber Transmission
### Transmission Experimental Setup
The DSP blocks were similar to the ones described in Section 2.2, with the difference that, after normalization, the digital signal was filtered with a matched SRRC filter similar to the one used in the transmitter. Then, the signal was upsampled to four samples-per-symbol for timing recovery using the delay multiplication algorithm [46], which is appropriated for Nyquist pulse-shaped signals. After timing recovery performed at four samples-per-symbol, the signal was downsampled to two samples-per-symbol for subsequent processing.
## 2 Numerical Simulation
### Train-Sound Barrier-Flow Field Transient Calculation Model  
In this study, numerical simulations were conducted using the pressure-based solver in ANSYS Fluent software (Ansys 2021 R2) to capture the three-dimensional, viscous, incompressible, and unsteady turbulent airflow. In this transient calculation model, the ground, the surface of the sound barrier, and the train are treated as no-slip wall boundaries, implying no relative sliding between the fluid and these surfaces. The top and both sides of the computational domain are designated as symmetrical wall boundaries. The front side of the computational domain is set as the inlet boundary with a zero flow velocity, while the rear side is specified as the zero-pressure outlet boundary. The dimensions of the computational domain are 306 m in length, 60 m in width, and 30 m in height, as shown in Figure 5.  
The moving train model employed the overlapping mesh technique to simulate the dynamic interactions between a train and a sound barrier. This technique is well-suited for non-constant flow problems involving relative motions between multiple objects. It offers the advantage of independent mesh generation, thereby reducing the complexity of mesh generation and computational effort across the entire flow field region. The simulated high-speed train operates at a speed of 360 km/h, corresponding to a Mach number of 0.294. Typically, for fluids with Mach numbers below 0.3, incompressible fluid calculations are simplified, disregarding changes in air density. As a high-speed train traverses a sound barrier, it encounters a three-dimensional, viscous, incompressible, and unstable turbulent air field. Considering these factors, the numerical simulation employs the three-dimensional transient incompressible Navier-Stokes equation and the \(k\)-\(\omega\) turbulence model. The discrete equations were solved in a coupled format, with the pressure term in second-order format, the gradient term in least squares, and the other terms in second-order upwind format. To achieve computational convergence, a minimum residual of \(10^{-3}\) was ensured for each turbulence equation at each time step. The sound barrier in the model has a height of 3 m from the top plane of the track, a thickness of 0.175 m, and a length of 100 m.
## 2 Methods
### Covariance Pooling Module  
Prior research has suggested that second-order statistical measures offer enhanced discriminative power over first-order approaches for image feature extraction [32; 33; 41]. The detailed structure of second-order pooling block can be shown in Figure 3. The MPN-COV model [41] differs from conventional approaches by not being restricted to first-order statistical features during training. Instead, it employs global covariance aggregation and matrix power normalization to capture inter-channel dependencies and model the distributional structure of features. This enables MPN-COV to significantly outperform other classical convolutional networks in terms of performance. Inspired by this, this study further integrates covariance aggregation techniques to calculate the second-order statistical features of BCHI.
To overcome some of these limitations, increasing the number of microsatellite _loci_ analyzed is necessary, but it can be time-consuming due to the number of PCR reactions required for an individual bee, optimizing multiplex PCR conditions, and assembling genotypes from multiple _loci_. Therefore, the use of a single hypervariable genetic marker instead of standard microsatellite markers could be convenient for genetic studies on honeybees, alleviating the amount of genotyping effort, and one such candidate could be the complementary sex-determining gene (_Csd_). The _Csd_ gene is responsible for sex determination in honeybees [15]. Bees that are heterozygous for _Csd_ develop into females, while bees that are hemizygous or homozygous for _Csd_ develop into males (drones). Generally, drones are haploid as they emerge from unfertil eggs. Diploid individuals can also develop into males when they are homozygous for _Csd_; however, they are eliminated by their nestmates at an early stage of development [16]. Due to the absence of fitness in homozygotes and the greater likelihood of common alleles forming homozygotes compared to rare ones, _Csd_ undergoes robust over-dominant and negative frequency-dependent selection. This combination of selective pressures, referred to as balancing selection, results in a significant level of intraspecific polymorphism [17]. The number of _Csd_ alleles present in the worldwide honeybee population is difficult to estimate, as _Csd_ alleles are unevenly distributed, and there is minimal overlap between local honeybee populations [18; 19]. To date, several hundred _Csd_ alleles have been deposited in public databases [20]. The _Csd_ gene encodes an SR-type protein that initiatesthe sex-determination cascade, resulting in the sex-specific splicing of _feminizer (fem)_ and _doublesex (dsx)_ transcripts [21; 22; 23; 24; 25; 26]. Exons 6-8 of the _Csd_ gene encode the potential-specifying domain, consisting of the hypervariable region (HVR) flanked by arginine/serine- and proline-rich domains. The HVR domain includes asparagine/tyrosine repeats varying in number across alleles, while the adjacent proline-rich domain contains TTCCTG/A repeats. These domains are separated by a conserved sequence (ATTAAT) containing a _Vspl_ (_Asel_) restriction enzyme recognition site (Figure 1). Similar to microsatellite _loci_, the diversity of the HVR is likely maintained by mutations occurring as a result of unequal crossing-over and/or polymerase slippage during replication. However, the mutation rate of the _Csd_ gene is estimated to be 2.4 times faster than that of microsatellite _loci_[19]. The aim of this work was to explore the possibility of replacing or supplementing the standard sets of microsatellite markers used in honeybee research with the analysis of _Csd_ diversity as a method for establishing patrilines and assessing their diversity within honeybee colonies.
# Polycrystalline Formamidinium Lead Bromide X-ray Detectors
## 2 Materials and Methods  
Materials. FABr (98%) was purchased from Ossila Ltd, Sheffield, UK. PbBr\({}_{2}\) (\(>\)99%), dimethylformamide (DMF, \(>\)99.8%) and gamma-butylrolactone (GBL, \(>\)99.9%) were purchased from Merck Life Science, Gillingham, UK.  
FAPbBr\({}_{3}\) polycrystals. FAPbBr\({}_{3}\) polycrystals were grown using the inverse temperature crystallization method (ITC). Specifically, FABr (0.01 mol, 1.25 g) and PbBr\({}_{2}\) (0.01 mol, 3.67 g) were added into 10 mL of DMF-GBL (1:1 \(v/v\)) within a glass flask. The temperature was increased gradually from room temperature 20 \({}^{\circ}\)C to 80 \({}^{\circ}\)C over the course of 24 h.  
Pellet fabrication. FAPbBr\({}_{3}\) polycrystals were ground by hand to achieve a uniform FAPbBr\({}_{3}\) powder. A hydraulic press was then applied to make 0.3 g of the powder into a 1 cm diameter pellet with different pressures ranging from 12 MPa to 124 MPa.  
SEM. The microstructure of the FAPbBr\({}_{3}\) pellets was analyzed by imaging the fracture surface using scanning electron microscopy (SEM). A JEOL JSM-7100F Field Emisson SEM was operated at 15 kV, and a ThermoFisher Scientific Verios G4 Field Emission SEM was operated at 5 kV and 0.10 nA to collect secondary electron images.  
AFM. AFM measurements were carried out using a Bruker Innova AFM.  
Device fabrication. FAPbBr\({}_{3}\) X-ray detectors were fabricated from a pressed pellet, with a [Au (90 nm)/FAPbBr\({}_{3}\) pellet (2 mm)/Au (90 nm)] vertically-stacked structure. The Au electrodes were deposited using thermal evaporation.  
Photoluminescence (PL) of FAPbBr\({}_{3}\) pellets. PL was recorded using a continuous-wave green laser of 552 nm, with an optical power of 30 mW at room temperature. Optical spectra were acquired using a QE6500 spectrometer (Ocean Insight, Duiven, The Netherlands).  
XRD. X-ray diffraction (XRD) patterns were collected using a Malvern Panalytical Empryean III X-ray diffractometer. The instrument was equipped with Cu K\({}_{\alpha}\) radiation (\(\lambda\) = 0.154 nm) and operated at 45 kV and 40 mA. The simulated diffraction pattern was obtained using the MDI Jade software package and atomic parameters from previously published results [20].  
X-ray and I-V measurements. Both dark current and X-ray photocurrent measurements were carried out using a Keithley 2400 source-meter (Tektronix UK Ltd, Bracknell, UK), with a programmable scan rate and settling time. For X-ray photocurrent and sensitivity measurements the device was irradiated with X-rays from an Amptek MiniX2 X-ray (Amptek, Bedford, USA [21]) source with an Au anode and an acceleration voltage of 40 kVp (see Supplementary Information). The sample was placed 45 mm away from the X-ray source. For the switched X-ray photocurrents, a mechanical tungsten X-ray chopper was used with a switching time of 5 ms.
However, approximately 23% of all land on Earth is covered with permafrost or seasonally frozen soil, where freeze-thaw cycling in seasonally frozen soil significantly affects the behavior of the soil and decreases its shear strength [26; 27; 28]. Studies have shown that freeze-thaw cycles reduce the strength of unreinforced soils, but there is no consensus on the effect of freeze-thaw cycles on the mechanical properties of rubber fiber-reinforced expansive clay [29; 30; 31; 32; 33; 34]. Under dynamic loads, freeze-thaw cycles change the dynamic shear modulus of the soil and decrease the dynamic strength [35; 36]. Although the characteristics of soils subjected to freeze-thaw cycles have been extensively studied, the impact of freeze-thaw cycles on rubber-reinforced soil has received relatively less attention. The improvement of expansive soil has been a hot issue in geotechnical engineering. Expansive soil is a specialized clay rich in hydrophilic minerals such as montmorillonite, which is widely distributed worldwide. Expansive soil is considered problematic because it swells in volume when it absorbs water and shrinks and cracks when it dries. It is estimated that the cost of maintaining expansive soil already exceeds the combined economic losses caused by natural disasters worldwide [37]. Therefore, a feasible solution is needed to address the negative impacts of expansive soil on building structures. This study aims to enhance expansive clay performance in freeze-thaw cycles and to find reliable methods for recycling large volumes of waste tires. Triaxial tests were performed on rubber fiber-reinforced expansive clay exposed to freeze-thaw cycles. To evaluate the effects of rubber fiber on the mechanical properties of expansive clay under freeze-thaw cycles, the triaxial test results of rubber fiber-reinforced expansive clay and unreinforced expansive clay were compared. An expansive clay stress-strain calculation model under freeze-thaw cycles was developed based on the test results.
## 3 Experimental Facility and Numerical Validation
### Numerical Calculation Method
The height of the first-layer mesh from the vane surface remains \(1\times 10^{-6}\) m with mesh quantity variation. The relationship of global node quantities with the deviations in total pressure loss coefficient is described in Figure 9. It is found that the total pressure loss coefficient at the design point gradually decreases with the increase in grid node numbers. When the number of grids in the calculation domain reaches \(2.5\times 10^{5}\), the numerical simulation results are essentially unchanged with the increase in the number of grids. For 2.0 \(\times\) 10\({}^{5}\) and \(2.5\times 10^{5}\) nodes, the deviation in total pressure loss coefficient is 0.06%, which is less than 0.1%. It is concluded that  
Figure 8: Calculation domain of cascade. \begin{table}
\begin{tabular}{c c} \hline \hline
**Boundary Conditions** & **Setting** \\ \hline Inlet & Total temperature, total pressure, and \\ velocity & \\ Outlet & Static pressure \\ Wall & Nonslip and adiabatic wall \\ Turbulence model & SST(\(k\)-\(\omega\)) \\ Fluid & Ideal gas \\ Periodicity & Translational periodicity \\ Initialization & Inlet parameters \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Boundary condition setting for numerical calculation. 2.5 \(\times\) 10\({}^{5}\) grid nodes are capable for predicting the aerodynamic performance of supersonic compressor cascades.