esa-sceva/satcom-chunk-collection huggingface dataset:
# Polymer Models of Chromatin Imaging Data in Single Cells
###### Abstract  
Recent super-resolution imaging technologies enable tracing chromatin conformation with nanometer-scale precision at the single-cell level. They revealed, for example, that human chromosomes fold into a complex three-dimensional structure within the cell nucleus that is essential to establish biological activities, such as the regulation of the genes. Yet, to decode from imaging data the molecular mechanisms that shape the structure of the genome, quantitative methods are required. In this review, we consider models of polymer physics of chromosome folding that we benchmark against multiplexed FISH data available in human loci in IMR90 fibroblast cells. By combining polymer theory, numerical simulations and machine learning strategies, the predictions of the models are validated at the single-cell level, showing that chromosome structure is controlled by the interplay of distinct physical processes, such as active loop-extrusion and thermodynamic phase-separation.  
chromosome architecture; multiplexed FISH imaging; polymer physics; machine learning; computer simulations +
Footnote †: journal:  
002221533  
molecular motors that stochastically bind to DNA and extrude a polymer loop in an out-of-equilibrium, active (e.g., ATP-dependent) physical process [18; 19; 20; 21; 22; 23; 24], and in the second, distal genomic sites are tethered together by interactions mediated, e.g., by diffusing cognate molecular particles, such as transcription factors, or by direct interactions produced, for example, by DNA-bound histone molecules [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40]. By taking as a case study a 2 Mb wide chromatin region in human IMR90 cells where super-resolution multiplexed FISH data are available [15], we combine those distinct physical mechanisms into polymer models that we investigate by massive computer simulations (see Methods). We show that both loop-extrusion and phase-separation significantly recapitulate microscopy data, hinting that both processes can reliably coexist to determine the structure of chromosomes at the single-cell level [28].
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 2 Materials and Methods
### TCP-NACK Linux Implementation  
We chose and modified the TCP/IP stack from Linux 2.6.32 source code for the TCP-NACK implementation based on TCP-Reno protocol, since it is an open access and open source operating system [17]. Specifically, we modified the files inside the \"include\" and \"net\" directories, which are distributed within the Linux kernel as showed in Figure 2.
## 4 Discussion  
### Decay Pattern of the Postseismic Displacements  
Using the InSAR time series, we selected four points on both sides of the fault traces (detailed description in Figure S2) to illustrate the decay pattern of postseismic transient deformation over time. The postseismic displacements from the InSAR observations increase over time at these selected sites, but rapidly decay in the early days (Figures 9 and 10). We then separately fitted the time series to estimate the optimum decay coefficient (\(\tau\)). In this paper, our results show that the decay coefficients are different at these four sites. Compared with the far-field region, the optimum decay coefficient (\(\tau\)) is smaller at the near-field points, that is, near-field transient postseismic deformation decays more rapidly. The optimal decay coefficients \(\tau\) for tracks 99 and 106 images were, respectively, 132 and 46 days. Compared to ascending data, descending orbit data have shorter sampling intervals and longer observation times in the early stages, resulting in a smaller decay coefficient. Similarly, for the Nepal event, [PERSON] et al. [30] estimated the optimal \(\tau=30\) days, while [PERSON] et al. [43] proposed an optimal decay coefficient close to 83 days by the similar method. This discrepancy might be due to the different time windows.  
We further estimated the residuals between the actual InSAR observations and the postseismic deformation predictions from the afterslip model. Our study found that the average RMS for tracks 99 and 106 images is less than 5 mm at the four selected sites, which suggests that early postseismic deformation is likely dominated by the downdip afterslip. However, a non-negligible discrepancy exists between the model predictions and observations at point A, which may be due to the unavoidable noise in the InSAR observations or other postseismic mechanisms, such as viscoelastic relaxation.  
Figure 9: The postseismic time series from the descending track at four selected sites. The blue points denote the InSAR time series without seasonal signals. The red circles represent the surface deformation simulated by the afterslip model, corresponding to the 12 snapshots in Figure 7. The pink curves show the postseismic time series fitted by the logarithmic equation.
# Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification
## 4 Experimental Results
### Effect of Fingers  
After studying the effect of different methods on gender-related features, we further explore the effect of each finger. We divide the testing fingerprints into 10 sets, each set of which corresponds to a specific finger that contains 660 fingerprint images. We apply the DDC-ResNet coupled with CNN (best-performance method above) to test the 10 sets, as show in Figure 5. The result indicates that for each specific finger, the right ring finger (R2) shows the highest accuracy, reaching 92.455%. For five pairs of fingers, ring fingers outperform other pairs, reaching an accuracy of 91.413%. For the overall hand, the right hand achieves a higher accuracy than the left hand, reaching 87.872%. To better understand the effect of fingers on gender-related features, more careful studies will be carried out in the following part.
# Structural Design and Kinematics Simulation of Hydraulic Biped Robot
## 4 Gait Model
### Walking Parameters  
In real situations, such as climbing stairs or avoiding obstacles, it is often necessary to directly specify the position of the feet.  
As shown in Figure 13, the position of a simple walking foot is represented by length and width of the step. For a normal person, the step length is generally within the range 500-800 mm, and the step width is within \(80\pm 35\) mm. The used data are listed in Table 1.  
Figure 12: A trajectory of centroids supporting the cycle.  
In Table 1, \(s_{x}\) represents the step length in the walking direction, \(s_{y}\) represents the step width in the lateral direction, superscript (\(n\)) represents the \(n\)-th step, and the position of foot at the \(n\)-th step can be expressed as \((p_{x}^{(n)},p_{y}^{(n)})\):  
\[\left[\begin{array}{c}p_{x}^{(n)}\\ p_{y}^{(n)}\end{array}\right]=\left[\begin{array}{c}p_{x}^{(n-1)}+s_{x}^{(n)} \\ p_{y}^{(n-1)}-(-1)^{n}s_{y}^{(n)}\end{array}\right] \tag{64}\]  
where, \(\left(p_{x}^{(0)},p_{y}^{(0)}\right)\) is the position of the first supporting foot, and Figure 13 shows the right foot. If the first supporting foot is the left foot, then \(-(-1)^{n}\) needs to be replaced with \(+(-1)^{n}\) in the formula.  
The foot position at \(n\)-th step is:  
\[\left[\begin{array}{c}\overline{x}^{(n)}\\ \overline{y}^{(n)}\end{array}\right]=\left[\begin{array}{c}\frac{\frac{s_{x} ^{(n+1)}}{2}}{\frac{(-1)^{n}s_{y}^{(n+1)}}{2}}\\ \end{array}\right] \tag{65}\]  
It is worth noting that the foot position at the \(n\)-th step is determined by the step length and step width of the \((n+1)\)-th step, which is necessary for proper adjustment of the foot position and walking movement. The velocity formula at the end point is:  
\[\left[\begin{array}{c}\overline{v}_{x}^{(n)}\\ \overline{v}_{y}^{(n)}\end{array}\right]=\left[\begin{array}{c}\frac{\omega (1+\cosh(\alpha t))}{\sinh(\alpha t)}\overline{x}^{(n)}\\ \frac{\omega(\cosh(\alpha t)-1)}{\sinh(\alpha t)}\overline{y}^{(n)}\end{array}\right] \tag{66}\]
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 2 Materials and Methods
### Quality Control of GNSS Observations  
Low-cost GNSS devices are of lower quality than high-class geodetic GNSS instruments, the latter being considerably more expensive. Low-cost GNSS instruments have poorer design and, consequently, a lower carrier-to-noise ratio (C/N\({}_{0}\)). To better understand and explain the quality of observations from low-cost GNSS receivers, simultaneous GNSS observations with both types of devices in open-sky (ST 1) and urban areas (ST 1A) were obtained. One simpleRTK2B V1 receiver was connected to a low-cost GNSS antenna (LC\(-\)1), and the other simpleRTK2B V1 receiver to a geodetic GNSS antenna (LC\(-\)2). GNSS data from geodetic GNSS devices (GD\(-\)1) were used as a reference. Observations were collected for 24 h under open-sky conditions, followed by 4 h under urban conditions.  
For a GNSS geodetic receiver, the C/N\({}_{0}\) can vary from 35-45 dBHz, and this value can be less than 30 dBHz for signals received at low elevation angles, while a high-quality antenna can reach up to 50 dBHz. The values of C/N\({}_{0}\) were determined for different elevation angles for LC\(-\)1, LC\(-\)2, and GD\(-\)1. Firstly, additional analyses were performed to show the variation of C/N\({}_{0}\) as a function of the elevation angle for LC\(-\)1. Secondly, the average value of C/N\({}_{0}\) was estimated on a basis of an additional 24 h set of GNSS observations obtained with a sampling rate of 1 Hz.  
Low-cost GNSS antennas are more sensitive to multipath error compared to geodetic antennas, which can affect the quality of position determination, especially in urban areas [2]. Considering that the observations were performed at the same time and place, the differences in multipath presence between LC\(-\)1 and LC\(-\)2 are due to the different antennas used, while the differences with GD\(-\)1 are due to both the receiver and the antenna. The RMSE value of the code multipath (CMC) for which the differences are more visible was obtained for both cases and all types of GNSS instruments used.  
Figure 2: Measuring stations.  
The ambiguity fix ratio is another factor analyzed and compared between LC\(-\)1 and LC\(-\)2. In urban conditions, the multipath and other factors (cycle slips, loss of lock) may degrade the percentage of fix solutions, which results in positioning with low quality. For the open-sky and urban conditions, the percentage of fix ambiguities is shown for 24 h and 4 h, respectively. In addition, the determined coordinates and their variation for horizontal and vertical components are also presented graphically.  
Figure 3: Location of measuring stations: (**a**) LC\(-\)1, LC\(-\)2, and GD\(-\)1 in ST 1; (**b**) LC\(-\)1, LC\(-\)2, and GD\(-\)1 in ST 1A; (**c**) LC\(-\)1 in ST 2; (**d**) LC\(-\)1 in ST 3; and (**e**) LC\(-\)1 in ST 4.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 4 Information Bottleneck Polar Decoders
### Information Bottleneck Successive Cancellation List Decoder
Moreover, at any level \(j\), every decoding table at an even stage \(i\) requires \(|\mathcal{T}|^{2}\cdot Q_{t}\) bits of memory, while each decoding table at an odd stage \(i\) requires \(2\cdot|\mathcal{T}|^{2}\cdot Q_{t}\) bits of memory, where \(Q_{t}=\log_{2}|\mathcal{T}|\) is the number of bits required to represent a cluster index \(t_{i,j}\). If the LLR values in the translation tables are stored using a resolution of \(Q_{LLR}\) bits, the \(N\) translation tables will consume a memory of \(N\cdot|\mathcal{T}|\cdot Q_{LLR}\) bits.
## 3 Results
### Artificial Intelligence (Al)-Supported Analysis  
#### 3.2.1 AI-Supported Measurement of Epidermal Thickness and Stratum  
Corneum Thickness  
The reduction in the ET in the course of therapy measured by the AI-model of the SP was significant (\(p\) = 0.006), as well as that of the SCT (\(p\) < 0.001); for the exact values of the AI-supported measurements, see Table 2. The difference in the thicknesses measured by the AI-model between the SP and CoA was calculated at each time point and compared between visits to assess for the convergence of the SP toward the CoA. This difference significantly decreased over the course of treatment for ET as well as SCT (ET: \(p\) = 0.004; SCT: \(p\) = 0.001) (see Figure 7 for illustration). The AI-supported measurements of the ET and SCT in the SP and CoA showed a high correlation with subjectively scored measurements (see Figure 8). The Pearson coefficient for the ET (SP and CL) was 0.97, and the Spearman coefficient was 0.91. Regarding the SCT (SP and CoA), the Pearson coefficient was 0.84, and the Spearman coefficient was 0.81. For a better comparison of the variation between the subjective and AI model-calculated thicknesses, we also applied a linear regression. When both methods had perfect accordance, the intercept was 0 and the slope was 1. For the ET, the intercept was 14.3 (95% confidence interval (CI): 5.7-23.0), and the coefficient was 0.79 (95% CI: 0.73-0.84). The median percentage difference in the AI-calculated ET from the subjectively scored value was 13.4%. The intercept for the SCT, respectively, was 12.9 (95% CI: 8.4-17.4), with a coefficient of 0.53 (95% CI: 0.45-0.61). The median percentage difference between the subjective and AI model measurements was 30.8%. Figure 6: (**a**) Manual measurement of epidermal thickness/acanthosis (in \(\upmu\)m) in the SP (orange) and CoA (blue) before (V0) and under therapy (V1 after 4 weeks, V2 after 12 weeks). (**b**) Manual measurement of stratum corneum/hyperkeratosis (in \(\upmu\)m) in the SP (orange) and CoA (blue) before (V0) and under therapy (V1 after 4 weeks, V2 after 12 weeks). SP = study plaque, CoA = control area, healthy control area in similar localization, \(\upmu\)m = micrometer. Thick horizontal line in the boxplots = median.
## 4 Data and Methods  
### Investigation Area and Data  
The basin area \"Goldene Aue\" near Nordhausen is located in the transition zone between the Federal State of Thuringia and Saxony-Anhalt in the Federal Republic of Germany (Figure 3). It is margined by the mountain ranges \"Harz\" in the North, \"Windleite\" in the South and \"Kyffhauser\" in the Southeast. These ridges are characterized by forested areas. The interjacent basin area is characterized by intense agricultural usage. This vast heterogeneity and structural complexity qualify the \"Goldene Aue\" as an optimal test site for the experimental image reconstruction.  
The data basis used in this study comprises two types of input data. On the one hand, multi-spectral satellite imagery from the sensors Landsat and SPOT 4 is employed. On the other hand, SAR satellite imagery from the sensors TerraSAR-X (TSX), ERS and PALSAR (ALOS) is used. The preprocessing of the multi-spectral imagery was restricted to the resampling of the SPOT scene to the Landsat resolution of 30 m and the respective coregistration of the images. Table 1 gives an overview of the properties of the multi-spectral imagery used in this study. For the sake of complexity reduction, all SAR data are processed to a resolution of 30 m. The advantages of this approach are the comparability with the Landsat image in terms of spatial resolution and the exclusion of spatial resolution as a variable of the resulting cloud fill quality. Table 2 gives an overview of the SAR data and related processing parameters applied to them.  
Figure 3: The testing area “Goldene Aue” in Germany.
## 5 Conclusions  
The present investigation in female football players demonstrated significant post-match reductions in IPF, RFD\({}_{150}\) and RFD\({}_{250}\) during the isometric quadriceps and hamstring muscle strength assessments. However, these measures were fully restored 48 h post-match. Still, some RFD-based H:Q ratios were still disturbed 48 h after the match, with values > 1.15 for H:Q RFD\({}_{150}\). This suggests that some parameters of players' neuromuscular function, particularly the ability to generate force rapidly, remain impaired 48 h post-match due to the differing recovery rates of the quadriceps and hamstring muscles in returning to baseline levels. Furthermore, players who executed the highest number of high-intensity actions during the match exhibited the smallest post-match-to-baseline differences. Thissuggests that those who engage in more high-speed movements may be better adapted to the physical demands of competition.  
As a practical application, the findings of this study suggest that a minimum recovery period of 48 h is necessary before engaging in high-intensity training after a competitive match, as neuromuscular function, particularly RFD variables, remains impaired immediately post-match but returns to baseline after two days. However, individual recovery rates vary, indicating the importance of personalised strength assessments to tailor post-match recovery and subsequent training volume and intensity. Given that quadriceps strength showed a greater post-match decline than hamstrings, targeted recovery strategies such as neuromuscular activation exercises, well-monitored quadriceps muscles training and adequate nutritional and hydration protocols should be emphasised to accelerate muscle function restoration. Additionally, monitoring RFD-based hamstring-to-quadriceps (H:Q) ratios could help to identify players at higher risk of injury if neuromuscular fatigue persists beyond 48 h.  
Conceptualisation, A.M.-I., J.L.L.E. and V.M.-P.; methodology, A.M.-I. and J.D.C.; formal analysis, A.M.-I., J.D.C. and C.L.-Q.; investigation, S.H.-S. and C.L.-Q.; data curation, A.M.-I.; writing--original draft preparation, A.M.-I. and V.M.-P.; writing--review and editing, J.L.L.E., J.D.C., C.L.-Q. and S.H.-S.; supervision, V.M.-P. and J.L.L.E. All authors have read and agreed to the published version of the manuscript.  
[PERSON] contribution was financed by a grant from the Ministry of Universities of Spain (grant number: FPU21/04536).  
The study was conducted in accordance with the Declaration of Helsinki and approved in March 2021 by the University's Ethics Review Committee (code: DCD.J.E.01.20).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors would like to thank all athletes and clubs involved in the study for their contribution.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:
## 4 _P. gingivalis_ and Systemic Diseases
### Other Diseases
#### 4.5.2 Biology and Immunology  
The oral cavity can be colonized by over 700 different species of microbes, including bacteria, fungi, viruses, and protozoa. Together, they form a complex biological system also known as the oral microbiome. More niches can be distinguished in the oral cavity, each with its own unique microbial composition. The oral microbiome is dynamic and can be influenced by several factors, which can lead to dysbiosis. _P. gingivalis_ has a variety of virulence factors that aid in the invasion or destruction of host tissue and evasion of the host immune response and has been associated with various systemic diseases [84].  
Several studies have explored the biological mechanisms of _P. gingivalis_, identifying it as a key pathogen in chronic periodontitis. The products of _P. gingivalis_, such as vesicles, may be key to understanding the mechanisms of communication and dissemination of the bacterium and its systemic implications [85].  
In a study by [PERSON] et al., the authors demonstrated the association between viruses and bacteria in PD, suggesting that both could contribute to the development of systemic diseases, but the interaction between _P. gingivalis_ and viruses warrants further investigation, as it may offer new insights into potential treatment strategies for patients [86].  
In addition, [PERSON] et al. also recognized _P. gingivalis_ as an important contributor to PD, and a comprehensive understanding of its virulence factors could provide a potential antigenic target for a periodontal vaccine [87]. According to [PERSON] and [PERSON], some periodontal bacteria could contribute to the maintenance, latency, and reactivation of HIV-1. Therefore, periodontal disease could accelerate the progression of acquired immunodeficiency syndrome (AIDS) in infected individuals [88]. Again, the prevention of periodontal disease emerges as a critical factor in slowing the progression of systemic diseases and improving the overall outcomes of these diseases [89].
# Adsorption of Metolachlor and Its Transformation Products, ESA and OXA, on Activated Carbons
## 3 Results  
### Characterization of Precursors and Activated Carbons  
#### 3.1.1 Composition of the Lignocellulosic Precursors  
It has been demonstrated that the microporous properties of the activated carbons depend not only on the experimental conditions of the carbonization and activation steps, but also preponderantly on the original nature and structure of the involved precursors. The lignocellulosic wastes used in this work had a high carbon content (ca. 45% and 50% for rape straw and sunflower seed, respectively) being appropriated to be used as precursors for obtaining activated carbons. The content of ashes was higher for the rape straw, which is related to the higher amount of P, K, S and Ca (Table 1); these differences are expected to show variations in the properties of the final activated carbons obtained from the precursors. Additionally, different percentages of cellulose, hemicellulose and lignin were obtained, with the lignin amount being the main difference between both compositions, with a four times higher value obtained for the rape straw.