esa-sceva/satcom-chunk-collection huggingface dataset:
# Paleomagnetic-Geodynamic Mapping of the Transition Zone from Ocean to the Continent: A Review
## 2 Tectono-Paleomagnetic Mapping and Its Role in Geological-Geophysical Integration
[18; 19] have applied the integrated paleomagnetic-radiometric and magnetic analyses for the tectonic-structural reconstructions in the Sea of Galilee area. As a result, the first magnetic-paleomagnetic-radiometric map (scheme) for this area was developed. The combined magnetic-paleomagnetic analysis enabled the discovery of the Kiama zone of inverse polarity in the easternmost Mediterranean [31]. This research triggered the development of the first paleomagnetic map of the easternmost Mediterranean based on the integrated interpretation of different geophysical fields and comprehensive analysis of the surrounding sedimentary and volcanogenic structures [22; 32; 33; 53]. Paleomagnetic-geodynamic mapping [22; 31] of the easternmost Mediterranean, located between the boundaries of Laurasia and Gondwana, where the Eurasian, Aegean-Anatolian, Nubian, Sinai, and Arabian plates converge, made it possible to clarify and substantiate the location and nature of their crust. For the first time, ophiolite exposures in the distal regions of the foreland adjacent to the southern Tethys zone were identified and mapped. A new tectonic map of the region has been compiled, where the Mesozoic Terrane Belt (MTB) with pre-collision, collision and post-collision traps have been recognized and contoured [22]. The specific complexity of the easternmost Mediterranean follows, first of all, from convergence here collision and spreading processes. A wide range of applied analytical, search and survey methods give a reason to believe that this area can become a reference for mapping techniques for transition zones from the ocean to the continent and geologically complex regions. It makes it possible to test a combination of paleomagnetic-radiometric studies and structural and tectonic analyses.
## 3 Results  
According to the initial results, by not considering the shear deformation effect in the beam theory, the forces and moments required to create a particular deformation in a single strut could be predicted by 15-20% higher for \(r/l\) as large as 0.15. The complete results are presented in Appendix E.  
The transformed elastic modulus, Poisson's ratio, and yields stress relationships for the six geometries are presented in Tables 2-4. For simplifying the equations, the terms\(S=\frac{AE_{s}}{l}\), \(T=\frac{\frac{2E_{s}l}{\Delta G_{s}l}+\frac{1}{3}}{\frac{E_{s}l}{E_{s}l}+\frac{1} {E_{s}l}}\), \(U=\frac{\frac{2E_{s}l}{\Delta G_{s}l}+\frac{1}{3}}{\frac{E_{s}l}{E_{s}l}+\frac{ 2}{E_{s}l}}\), \(V=\frac{1}{\frac{E_{s}l}{E_{s}l}+\frac{2}{E_{s}l}}\), and \(W=\frac{G_{s}l}{l}\) and have been used in the noted tables for the relationships based on the Timoshenko beam theory. For four of the geometries (BCC [31], rhombicuboctahedron [30], truncated cube [32], and truncated octahedron [39]), the original Euler-Bernoulli relationships have been presented in Tables 2 and 3 as well. In the case of hexagonal packing, the original Euler-Bernoulli relationships [35] for elastic modulus and Poisson's ratio have been improved and adjusted (the description on how and why can be found in Appendix C), and the improved relationships are presented in Tables 2 and 3. For the case of diamond unit cell, the original relationships for elastic modulus and Poisson's ratio have been conserved but stated as a function of \(r/l\) rather than of relative density, \(\mu\). As for the yield stress (Table 4), the analytical Euler-Bernoulli relationships presented in our other paper [40] have been presented. In order to compare the already-existing [26; 30; 31; 32; 35; 39] or improved Euler-Bernoulli analytical relationships with the newly transformed Timoshenko analytical relationships, the results of the analytical relationships for both the Euler-Bernoulli and Timoshenko beam theory have been compared with their numerical and experimental counterparts in Figures 6-8. The relative density relationships for the six geometries are presented in Table 5.  
For all the geometries, the newly transformed Timoshenko relationships have exceptionally good agreement with the numerical results for all the mechanical properties: elastic modulus, Poisson's ratio, and yield stress (Figures 6-8). As for the previously obtained formulas obtained in the literature or newly adjusted Euler-Bernoulli formulas, the maximum difference between Euler-Bernoulli analytical elastic modulus and corresponding numerical values for BCC, diamond, hexagonal packing, rhombicuboctahedron, truncated cube, and truncated octahedron (at relative density of \(\mu=0.5\)) are, respectively, 21.34%, 57.71%, 20.21%, 14.52%, 14.98%, and 45.54%. However, the corresponding differences between the Timoshenko analytical relationships and the numerical values for the noted geometries are, respectively, 1.13%, 2.21%, 8.29%, 2.97%, 0.43%, and 3.15%.
## 2 Materials and Methods
### Peritumoral Tract Sampling and RNA Sequencing  
One of seven patients enrolled at the primary enrollment center underwent additional study collection of tumor-associated samples intraoperatively for grouped analysis using RNA sequencing (RNAseq). Samples were chosen using preoperative MRI with overlaid tractography combined with stereotactic intraoperative navigation system (BrainLab, Munich, Germany). A total of 13 samples were collected and classified as either normal peritumoral brain tissue (\(n=2\)), peritumoral tract-associated tissue (\(n=6\)), or tumor tissue (\(n=5\)). RNAseq was performed using Illumina HiSeq2500 (GeneWiz/Azenta Life Sciences, South Plainfield, NJ, USA). The 150-nucleotide sequence reads were aligned to the hg38 build of the human genome using gsnap [52]. The genomic locations of genes/exons defined in Refseq were extracted from the refGene.txt ([[http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/refGene.txt.gz](http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/refGene.txt.gz)]([http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/refGene.txt.gz](http://hgdownload.cse.ucsc.edu/goldenPath/hg38/database/refGene.txt.gz)), accessed on 15 January 2019). Read summarization at the gene level was performed with in-house scripts using reads with a mapping quality of 20 or greater. After discarding genes with fewer than 5 reads-per-million in at least 2 samples, we retained 15,562 genes for analysis. Clustering of samples based on expression profiles was performed with the hclust routine of R (Posit Software, Boston, MA, USA). This analysis showed that the samples acquired from the tumor areas that correspond to tracts cluster separately from the bulk tumor or control samples. Gene expression was analyzed in R with the limma package [53; 54], applying voom precision weights to account for the mean-variance dependency observed in the standardized read counts [55]. \(p\)-values were adjusted with the Benjamini-Hochberg method to control the false discovery rate and account for multiple hypothesis testing [56]. A 2-fold difference was imposed as minimal threshold for differential expression. Functional classification was carried out using gene set enrichment analysis (GSEA) with the Gene Ontology annotations at [[http://pantherdb.org](http://pantherdb.org)]([http://pantherdb.org](http://pantherdb.org)), accessed on 15 January 2019 [57].
# Recent Trends on UV filters
## 3 Results
### Usage Frequency of UV Filters in Sunscreens for Adults and Children
More recently, in 2019, the European Commission amended the entry regarding the nano form of TiO\({}_{2}\) as a UV filter in Annex VI [30; 34; 35]. Reducing particle size from micro to nano particles decreases the appearance of white residue after skin application, which consequently improves the attractiveness of the product [36]. Regarding the analyzed children's sunscreens, the most used organic filters are essentially the same as those used in adult photoprotective formulations (Figure 2). The inorganic filter TiO\({}_{2}\) (non-nano form) is much more used in children's sunscreens, increasing from 6.07% to 35.4% when compared to the photoprotectors used for adult formations. A lower number of UV filters was used in sunscreens for children (21 UV filters) compared to the adults' sunscreens (24 UV filters). Isoamyl \(p\)-methoxycinnamate, tris-biphenyl  
Figure 1: Usage frequency of the UV filters in sunscreens for adults. (BMDBM: butyl methoxy-dibenzoylmethane, OC: octocrylene, Tinosorb S\({}^{\circledgtrgtr}\): bis-ethylhexyloxyphenyl methoxyphenyl trirazine, EHT: ethylhexyl1 triazone, EHS: ethylhexyl salicylate, DHHB: diethylamino hydroxybenzoyl1 hexyl benzoate, EHMC: ethylhexyl methoxycinnamate, DBT: diethylhexyl1 butamido triazone, DTS: dometrizole trisiloxane, PBSA: phenylbenzimidazole sulfonic acid, Tinosorb M\({}^{\circledgtr}\): methylene bis-benzotriazoyl tetramethylbutylphenol, TDSA: terephthalalyidene dicamphor sulfonic acid, MBC: 4-methylbenzylidene camphor, PS15: polysilicone-15, IMC: isoamyl \(p\)-methoxycinnamate, DPDT: disodium phenyl dibenzimidazole tetrasulfonate, TBPT: tris-biphenyl triazine, Triasorb\({}^{\circledgtrgtr}\): phenylene bis-diphenyltriazine.) triazine, and phenylene bis-diphenyl triazine were the UV filters included in the adults' UV-protective formulations but not in children's sunscreens. The photosensibilization potential of isoamyl \(p\)-methoxycinnamate has been previously reported, which may account for the observed difference.
# A Methodology to Model the Rain and Fog Effect on the Performance of Automotive LiDAR Sensors
## 4 Modeling of the Rain and Fog Effect in the Virtual LiDAR Sensor
### Marshall-Palmer Distribution  
The Marshall-Palmer DSD is commonly used in the literature for characterizing rain, and it can be written as:  
\[N(D)=N_{0}\cdot e^{-\Lambda D}, \tag{2}\]  
where \(N_{0}=8000\,\mathrm{m}^{-3}\) mm\({}^{-1}\), \(\Lambda=4.1\ r_{rate}{}^{-0.21}\)mm\({}^{-1}\), \(r_{rate}\) is the rain rate in mm/h, and \(D\) is the drop diameter in mm [24].  
Figure 3: Exemplary scan pattern of Cube 1. \(\pm 36^{\circ}\) horizontal and \(\pm 15^{\circ}\) vertical FoV, 50 scan lines, 0.4\({}^{\circ}\) horizontal angle spacing, frame rate 5.4 Hz, maximum detection range 250 m, and minimum detection range 1.5 m.  
We use the inverse transform sampling method [23] to randomly generate raindrops based on the Marshall-Palmer distribution. Equation (2) becomes:  
\[D=-\frac{1}{\Lambda}\cdot\ln(1-u), \tag{3}\]  
where \(u\) is a random variable with the uniform distribution \(\mathcal{U}(0,1)\)[23]. The terminal velocity \(v_{t}\) of the raindrops in m/s can be written as:  
\[v_{t}(D)=3.78\,D^{0.67}, \tag{4}\]  
where \(D\) is the raindrop diameter in mm [27].
# Study on Molybdenum-Rhenium Alloy Ultrasonic Resonance Temperature Sensor
###### Abstract  
Compared to traditional temperature measurement methods, ultrasonic temperature measurement technology based on the principle of resonance offers advantages such as shorter section lengths, higher signal amplitude, and reduced signal attenuation. First, the type of sensor-sensitive element was determined, with a resonant design chosen to improve measurement performance; using magnetostrictive and resonant temperature measurement principles, the length, diameter, and resonator dimensions of the waveguide rod were designed, and a molybdenum-thenium alloy (Mo-5%Re) material suitable for high-temperature environments was selected; COMSOL finite element simulation was used to simulate the propagation characteristics of acoustic signals in the waveguide rod, observing the distribution of sound pressure and energy attenuation, verifying the applicability of the model in high-temperature testing environments. Second, a resonant temperature sensor consistent with the simulation parameters was prepared using a molybdenum-thenium alloy waveguide rod, and an ultrasonic resonant temperature-sensing system suitable for high-temperature environments up to 1800 \({}^{\circ}\)C was constructed using the molybdenum-thenium alloy waveguide rod. The experiment used a tungsten-thenium calibration furnace to perform static calibration of the sensor. The temperature range was set from room temperature to 1800 \({}^{\circ}\)C, with the temperature increased by 100 \({}^{\circ}\)C at a time, and it was maintained at each temperature point for 5 to 10 min to ensure thermal stability. This was conducted to verify the performance of the sensor and obtain the functional relationship between temperature and resonance frequency. Experimental results show that during the heating process, the average resonance frequency of the sensor decreased from 341.8 kHz to 310.37 kHz, with an average sensitivity of 17.66 Hz/\({}^{\circ}\)C. During the cooling process, the frequency increased from 309 kHz to 341.8 kHz, with an average sensitivity of 18.43 Hz/\({}^{\circ}\)C. After cooling to room temperature, the sensor's resonant frequency returned to its initial value of 341.8 kHz, demonstrating excellent repeatability and thermal stability. This provides a reliable technical foundation for its application in actual high-temperature environments.  
ultrasonic guided waves; resonance; molybdenum-thenium alloy; temperature sensor [EMAIL_ADDRESS] 2025156965
# Rhythm-Based Attention Analysis: A Comprehensive Model for Music Hierarchy
## 4 Experiments
### Experimental Design and Dataset
#### 4.1.2 Baseline Models  
We selected several baseline models to compare phrase segmentation results on the ESFC dataset. These baselines include both rule-based and machine learning approaches. The rule-based models we considered are as follows:  
* **Rule-Combined Model:** This model integrates various musical rules, including \(\Delta\)IOI (change in inter-onset interval), Meter Finder (beat detection), and Pause (rest position detection). It was first proposed by [PERSON] et al. (2018) in [45]. By combining multiple rhythmic and prosodic features to determine phrase boundaries, this approach offers simplicity, efficiency, and ease of implementation and demonstrates stable performance in musical phrase segmentation tasks.
* **Supervised Rule-based Model:** This hybrid model combines rule-based features with supervised machine learning methods and was first introduced by [PERSON] et al. (2020) in [46]. Specifically, the model extracts a set of features related to musical rhythm and meter and employs a multi-layer deep learning classifier (referred to as the Ripper classifier) along with a random forest classifier to perform supervised training. Through this process, the model effectively learns characteristic patterns associated with musical phrase segmentation. Due to the incorporation of supervised learning, this model shows significant improvements in generalization and performance compared to purely rule-based methods.
* **Unsupervised Model:** This model is an unsupervised method based on ensemble temporal prediction errors, proposed by [PERSON] et al. (2022) in [47]. Unlike supervised approaches, the ensemble temporal prediction model does not rely on labeled data.  
Instead, it detects phrase boundaries by analyzing changes in prediction errors over time within the music sequence. The main advantage of this model is its independence from annotated data and its adaptability to various musical styles, making it suitable for applications with limited or unlabeled data resources.  
The above baseline models represent a wide spectrum of approaches, from simple rule combinations to supervised and unsupervised learning methods, providing a solid and comprehensive reference for comparison. This enables deeper analysis and validation of the proposed model's performance in the musical phrase segmentation task.
# Improved Short-Term Clock Prediction Method for Real-Time Positioning
## 2 Clock Prediction Methods
### Stability of IGS On-Board Clock Corrections  
Although cesium clocks have a better long-term stability than rubidium clocks, the lower short-term noise and price makes the latter more suitable for short-term prediction. The Overlapping Allan DEVIation (OADEV), the most common measure of time-domain frequency stability, is used to characterize the stability of three types of GPS on-board clock [17]. In terms of phase data \(x_{i}\), the Overlapping Allan VARIance (OAVAR) \(\sigma_{y}^{2}\) can be estimated from a set of \(N\) time measurements as  
\[\sigma_{y}^{2}(\tau)=\frac{1}{2(N-2m)\tau^{2}}\sum_{j=1}^{N-2m}[x_{i+2m}-2x_{i +m}+x_{i}]^{2} \tag{1}\]  
where \(\tau~{}=~{}m\tau_{0}\) is the averaging time interval, m is the averaging factor and \(\tau_{0}\) is the basic interval.  
Figure 1 shows OADEV for 30 days IGS clock corrections detrended by removing a second-order polynomial. The stability of the block IIF satellites differs from the previous blocks. The block IIF shows the lowest OADEV of three types of GPS constellation. The OADEV of the block IIF rubidium AFS and cesium AFF varies as \(1/\sqrt{\tau}\) up to a few thousand s, consistent with white phase noise. The Block IIR clocks have a very similar performance as the Block IIR-M clocks up to \(10^{4}\) s. The decrease of them is closer to \(1/\tau\), which corresponds to white frequency noise. However, it should be noted that G13, launched in 1997, has been shown to differ from others. This may be due to the instability caused by the aging of the atomic clocks. The non-power-law behavior displayed in rubidium AFS at averaging intervals round \(10^{4}\) is caused by the 12-h periodic signal [9]. Besides, it can be concluded that the OADEV of GPS constellation varies between \(10^{-11}\) and \(10^{-14}\) s up to \(10^{4}\) s averaging interval. With these short-term features, we can better perform the following analysis of prediction.
## 3 Topology Optimization Design of U-Frame of Space Electro-Optical Tracking and Pointing Turntable
### The Establishment of the Initial Model
#### 3.2.2 Boundary Condition Analysis  
The configurations of spaceborne turntables and two-dimensional turntable systems are defined by their connections to the satellite. Connection to the satellite is achieved through the pitch shaft system, flange, and azimuth locking mechanism. Given these conditions, a fixed support constraint is applied at the juncture where the U-shaped frame meets the locking mechanism. This constraint limits movement in all six degrees of freedom.  
Additionally, given that the inner wall of the azimuth shaft hole of the U-shaped frame creates a contact pair with the outer surface of the bearing, displacement constraints are applied to the inner wall of the azimuth shaft hole. Axial rotational freedom is permitted, whereas the remaining five degrees of freedom are constrained.
## 2 Antenna Designing and Operation Mechanism
### Design of Band Notch Elements
#### 2.2.3 Satellite Band Notch Using Mutual Coupling  
The X-Band for satellite communication is from 7.2 to 8.4 GHz as allocated by the federal commission communication (FCC). Reliable communication is not possible because of interference due to the co-existence between the UWB and X-band. The satellite band notch is required to be implemented. The satellite band notch is achieved by creating specific mutual coupling between WiMax and WLAN element. A notch at 8.9 GHz in the UWB characteristic is achieved by selecting the appropriate wavelength \(\lambda=3\) mm. Figure 5 shows the achieved notching characteristics using mutual coupling. The coupling of notches is presented in Figure 6. The figure shows that the combined effect of the L-shaped notching element with the U-shaped notching element over the entire frequency band shifted a bit along with the creation of a third band notch. However, with the addition of both notching elements, i.e., \(U_{u}\) and \(U_{u}\), a shift of 2% in the center frequency in the WLAN band notch toward the high frequency band is observed. Similarly, the center frequency for WiMax notch is shifted by 13% toward the lower frequency end.  
Figure 4: (**a**) Geometry of \(U_{u}\)-shaped slot; and (**b**) \(S_{11}\) parameter with different lengths of an inverted U-shaped slot (\(U_{u}=8\) mm, 9 mm) for WLAN band notching.
## 3 Results and Discussion
### Antimicrobial Analysis
The low percentage of terpenoids and relatively high content of carboxylic acids in the oils extracted from _Hydnora africana_ may suggest that the oil may not be considered as a true essential oil [25]. This may be the reason for the weak antioxidant effects shown by the oil against DPPH, ABTS and nitric oxide. However, the potential applications of the oil in the cosmetic industries, due to the presence of some terpenoids may be very useful. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Solvent**} & \multicolumn{8}{c}{**Inhibition Zone Diameter (mm)**} \\ \cline{2-10}  & **Af** & **An** & **Mg** & **Tr** & **Tt** & **Tm** & **Pa** & **Pc** & **Mc** \\ \hline Essential oil & NA & 2.5 \({}^{\text{a}}\) & NA & NA & NA & NA & NA & NA & NA \\ Positive control & 0.01 & 2.5 \({}^{\text{a}}\) & 0.02 & 2.5 & \textless{}0.01 & 0.02 & 0.01 & 0.02 & 0.01 \\ \hline \hline \end{tabular}  
* The fungi are denoted thus by: Af (_Aspergillus fumigatus_), An (_Aspergillus niger_), Mg (_Microsporium gypsum_), Tr (_Tribolphton rubrum_), Tr (_Tribolphton tonsurans_), Tm (_Tribolphton mucoides_), Pa (_Penicillium aurantrogenreum_), Pc (_Penicillium chrysogenum_) and Mc (_Microsporum c anis_). Data expressed as means \(\pm\) SD; \(n\) = 3; values along a row with differentsubscripts are significantly different (\(p\leq 0.05\)). NA indicates not active.  
\end{table} 
Table 4: Minimum inhibitory concentrations (MIC) of the \(H\). _africana_ essential oils against the tested opportunistic fungi.
# Rethinking Learnable Proposals for Graphical Object Detection in Scanned Document Images
## 2 Related Work
### Deep Learning-Based Approaches
One limitation of this method is its inability to handle tables with a large number of empty cells. [PERSON] et al. [34] proposed SEM (split, embed, and merge), which exploits the multimodality of tables, as they contain both visual and text features. First, a fine grid structure of the potential rows and columns of the table is obtained. Next, using a visual module and text module, the two modalities are fused; the fused features are used to predict the table structure. [PERSON] et al. [35] proposed CascadeTab-Net, which tries to solve the table detection and table structure recognition together. Unlike most other detection networks that treat the problem of table detection and structure representation separately, CascadeTab-Net uses a single-shot method for both. They utilized the Cascade Mask R-CNN [36] detection head. More recently, CDeC-Net [37] utilized the dual-backbone paradigm introduced by CBNetv1 [38]. It has high-resolution dual ResNeXt101 backbones with deformable convolutions. The backbone is paired with a Cascade Mask R-CNN detector head. It allows the network to detect objects at different resolutions and achieve high accuracy, even at higher IoU thresholds. As our work focuses on graphical page object detection, to this end, Table 1 shows a comparison of the different approaches we investigate in our work with prior work in the domain. The table lists the key characteristics of each approach along with its limitations and advantages that are drawn from the experiments we carry out for each approach and its corresponding methods. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Methods** & **Advantages** & **Limitations** & **Results** \\ \hline Dual & Faster R-CNN, & Ability to capture different features with the use & Features extracted by the parallel backbones are not normalized distinct & Parallel backbones do not lead to better performance in document images \\ \cline{2-4} Backbone Architectures & Swin-S Cascade Mask R-CNN & & & \\ \cline{2-