esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overview of the Human Gut Microbiota
### Development and Maturation of the Gut Microbiota  
The composition of the gut microbiota in adulthood is the result of a complex developmental trajectory that begins at birth. Early-life microbial colonisation is increasingly recognised as a key determinant of immune and metabolic programming, both of which are closely linked to cardiovascular health [29]. During gestation, while the amniotic fluid contains trace amounts of bacteria, it seems insufficient to influence the colonisation process of the foetus [30].  
The initial composition of the neonatal gut microbiota is primarily determined by the mode of delivery. In vaginal delivery, the newborn is predominantly colonised by _Lactobacillus_ spp., _Sneathia_ spp., and _Prevotella_ spp. through contact with the mother's vaginal and faecal microbiota. Conversely, infants delivered by caesarean section tend to be colonised by microorganisms present on the mother's skin, on surfaces of the hospital environment, and possibly in the mother's respiratory tract. As a result, the initial colonisation is predominantly by bacteria belonging to the genera _Corynebacterium_ spp., _Staphylococcus_ spp., and _Propioniibacterium_ spp. [30; 31]. Other important factors include the introduction of either breastfeeding or formula feeding. Breastfeeding promotes the proliferation of bacteria from the _Bifidobacterium_ spp., which are capable of degrading particular oligosaccharides in breast milk and are known to have several benefits, including the modulation of the immune system and the production of vitamins [32]. Conversely, formula feeding tends to stimulate a more diverse microbiota, with a higher prevalence of opportunistic pathogenic bacteria belonging to the genera _Enterococcus_ spp., _Bacteroides_ spp., and _Clostridium_ spp. [32; 33].  
The introduction and diversification of food is an important dietary event in childhood that causes profound shifts in the gut microbiota. This period is characterised by the diversification of the gut microbiota and by the dominance of bacterial species belonging to the phyla _Bacteroidota_ and _Bacilotta_[33; 34; 35; 36].  
The gut microbiota undergoes significant development until approximately three years of age, as this is the stage when food introduction is completed and weaning from breast or formula milk occurs. The microbial community evolves from a simple community with low richness and diversity to one that progressively acquires the composition of the adult gut microbiota [37]. Understanding how early microbial exposures shape the long-term composition and function of the gut microbiota provides valuable context for designing effective microbiota-targeted interventions aimed at preventing or mitigating atherosclerosis later in life [29; 38].
## 5 Experimental Results  
The proposed F-SBL method was applied to experimental UWA OFDM signals to further verify the performance in real underwater conditions. The signals were acquired in an at-sea experiment performed in the Western Sea of Korea from 19 to 20 August 2018. The UWA OFDM system with the parameters in Table 1 was used. A total of 17 frames were transmitted throughout the experimental period. Each frame was composed of 54 OFDM symbols that carried OPSK modulated symbols encoded via a turbo-code (with a code rate of 1/3). The communication range was between 2500 and 3500 m, and the measured sea depth was about 30 m. The underwater CIR estimate corresponding to each frame is shown in Figure 6. We can clearly see that the channel had time-varying characteristics and multi-paths with long delays.  
Figure 5: BLER performance versus SNR for 16 QAM modulation.  
Figure 7 shows the coded BER performance according to the frame index. It can be observed from the experimental results that the proposed F-SBL and F-TMSBL outperformed the conventional schemes. F-TMSBL yielded the largest error-free reception number, achieving the best BLER. F-SBL exhibited lower-coded BER than the conventional SBL method even with fewer iterations. The experimental results confirm the effectiveness of the proposed method for real underwater channels.
## 4 Conclusions  
The process of removal of sulfur compounds from diesel fuels by solutions of chlorides (CdCl\({}_{2}\), CuCl\({}_{2}\), CoCL\({}_{2}\), MnCl\({}_{2}\), CrCl\({}_{3}\)) and metal trifluoroacetates (Co, Mn, and Cu) in dimethylformamide and dimethyl sulfoxide has been studied. It was shown that extraction of sulfur-containing compounds from a diesel fuel with residual sulfur content 0.18% proceeds under mild conditions, at a temperature of 25 \({}^{\circ}\)C and atmospheric pressure for 30 min. The residual SCC content after 2-step extraction is less than 90%.  
The interaction of organic solvents with metal salts and sulfur-containing compounds of the diesel fraction was considered within the framework of HSAB concept. It was shown that the most complete extraction of SCC was carried out by the DMF-Mn(TFA)\({}_{2}\), MnCl\({}_{2}\), and CrCl\({}_{3}\) systems, which is due to the hard base-hard acid interaction.  
The PMR method has shown by the example of DMF and cobalt chloride, that the addition of a metal salt to the coordinating polar solvent results in the coordination of the metal cation through the oxygen atom of the DMF carbonyl group, which leads to a redistribution of the electron density in the solvent molecule and enhancement of properties of the acceptor center localized on the nitrogen atom.  
Thus, the work shows that extraction systems based on coordinating organic solvents and metal salts may be well suited for the removal of sulfur compounds from diesel fuels.  
N.S.K., T.S.S., O.S.A., E.I.M. and V.I.S. conceived of and designed the experiments; T.S.S., E.I.M. performed the experiments, O.S.A. and V.I.S. analyzed the data; N.S.K. wrote the paper.  
This research was funded by Ministry of Education and Science of the Russian Federation grant No. RFMEF157817X0225.  
The work is performed with financial support of the Ministry of Education and Science of the Russian Federation. Grant agreement No. RFMEFI57817X0225.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metal Salts** & \begin{tabular}{c} **Concentration of the Metal** \\ **Salt in DMSO, mol/dm3** \\ \end{tabular} & \
\begin{tabular}{c} **S\({}_{\text{tot}}\) \%** \\ \end{tabular} &
\begin{tabular}{c} **Degree of Extraction of** \\ **Sulfur Compounds, \%** \\ \end{tabular} \\ \hline CdCl\({}_{2}\) & 0.12 & 0.10 & 33.3 \\ CoCl\({}_{2}\) & 0.50 & 0.06 & 66.6 \\ CuCl\({}_{2}\) & 1.76 & 0.06 & 66.6 \\ MnCl\({}_{2}\) & 2.50 & 0.04 & 76.6 \\ CrCl\({}_{3}\) & 2.75 & 0.03 & 78.8 \\ Co(TFA) & 0.11 & 0.05 & 70.0 \\ Mn(TFA)\({}_{2}\) & 0.11 & 0.04 & 78.3 \\ Cu(TFA)\({}_{2}\) & 0.11 & 0.06 & 68.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extraction of sulfur compounds from the fraction boiling in the temperature range of 200–350 \({}^{\circ}\)C with saturated solutions of chlorides and metal trifluoroacetates in DMSO. The total sulfur content (S\({}_{\text{tot}}\)) in the initial sample is 0.2%. S\({}_{\text{tot}}\) content in the initial sample after treatment with DMSO is 0.18%. The extraction time is 30 min. The temperature is 25 \({}^{\circ}\)C.
# Robust Parking Block Segmentation from a Surveillance Camera Perspective
## 3 Dataset
### Dataset Statistics  
Table 1 shows the control figures for each of the produced image sets.  
Figure 3: Parking lot examples from satellite perspective: (**a**,**b**) were both excluded from our dataset given the poor demarcation in the former and poor lighting conditions in the latter. In contrast, (**c**) is a great example that was included in our dataset.  
Figure 4 shows the general statistics of the input and mask coverage ratio. We can see the annotated class is clearly unbalanced, with only 24.5% corresponding to parking blocks. To overcome this challenge we must select a metric that compensates this phenomenon. In the next section we will see how the IoU metric has been used efficiently in the past for similar cases.  
It is also worth mentioning that the parking lot instances have a mean of 15.7 polygons, a maximum of 332 and a minimum of 0. The standard deviation on the number of polygons per parking lot is 26.31.
## 3 Design of Beam Steering and Adjustment Structure
### Analysis of Beam Adjustment Capability
#### 3.2.1 Piston Error Correction Simulation  
The Power UMAC programmable multi-axis motion controller from Delta Tau Data System Inc. is adopted as the system controller module. In the control software system, each piece of piezoelectric ceramic corresponds to three commands: displacement in the \(Z\)-axis, rotation around the \(X\)-axis, and rotation around the \(Y\)-axis. The \(Z\)-axis displacement command is used to adjust the piston error; the \(X\)-axis and \(Y\)-axis rotation commands are used to adjust the tilt error.  
When there is no piston error in the subaperture, the light path is \(abcd\), as shown by the solid lines in Figure 12. When there is a piston error \(p\) in the subaperture, the light path is \(a^{\prime}bcd\), as shown by the dotted lines in Figure 12, and the optical path difference is \(aa^{\prime}\). Corresponding adjustments are made by the \(FSM\) to compensate for the piston error \(p\) and ensure a constant optical path length. The piston motion of the \(FSM\) is driven by piezoelectric ceramics, and the compensation is set as \(p^{\prime}\). As can be seen from the figure, regardless of the value of \(p^{\prime}\), there is still \(bcd=b^{\prime}c^{\prime}d^{\prime}\). Therefore, the piston error \(p\) of the subaperture can be compensated for when the motion of \(p^{\prime}\) enables \(ab=a^{\prime}b^{\prime}\) or \(aa^{\prime}=bb^{\prime}\). In order to realize \(ab=a^{\prime}b^{\prime}\), there shall be \(p^{\prime}=p\times\cos 45^{\circ}\), i.e., when the compensation is \(p^{\prime}=p\times\cos 45^{\circ}\), there is \(abcd=a^{\prime}b^{\prime}c^{\prime}d^{\prime}\). Therefore, when there is a piston error \(p\) in the subaperture, the \(Z\)-axis motion \(p^{\prime}\) that the piezoelectric ceramics can compensate for in the piston error is:  
\[p^{\prime}=\frac{\sqrt{2}}{2}p \tag{6}\]  
With the piston error \(p^{\prime}=\lambda/2\) image as the correction object, according to Formula (1), the piezoelectric ceramic motion for correcting the piston error is:  
\[p^{\prime}=\frac{\sqrt{2}}{2}p=\frac{\sqrt{2}}{2}\times\frac{\lambda}{2}=0.35\lambda \tag{7}\]  
The piston error correction commissioning is carried out by gradually adjusting the \(Z\)-axis displacement of the piezoelectric ceramics from \(p^{\prime}=0\) to \(p^{\prime}=\lambda\), and the corrected image is shown in Figure 13.  
Figure 12: Comparison of optical paths when 0 piston error and p piston error.  
Figure 13: Simulated imaging of piston error correction.  
It can be seen from Figure 11 that the imaging effect is equivalent to the ideal situation when \(p^{\prime}=2\lambda/5\).
## 3 Results and Discussions  
### Atterberg Limits  
The results of Atterberg limits tests are given in Table 1.  
Regardless of the percentages of XG in the soil, the plastic and liquid limits for the XG-treated soil were higher than pure soil. Significant increases in LL (64.7 in 2% XG additive-treated soil versus 38 in pure soil) and slight gains in PL were seen. A considerable increase (about two times) in the plasticity index occurred when adding 2% XG. This arises from the gel type and hydrophilic nature of XG, which results in absorbing more water and increases the plasticity index of soil. Additionally, the concentration of XG affects the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Soil Reference** & **Liquid Limit, LL (\%)** & **Plastic Limit, PL (\%)** & **Plasticity Index, PI (\%)** \\ \hline Pure soil & 38.0 & 26.9 & 11.1 \\
0.5\% XG & 59.6 & 28.5 & 31.1 \\
1\% XG & 62.2 & 29.6 & 32.6 \\
2\% XG & 64.7 & 30.7 & 34.0 \\ \hline \end{tabular}
\end{table}
Table 1: Results of Atterberg limits tests.  
Afterberg limits. As shown in Table 1, soil treated with 0.5% XG had lower PL and LL compared to 1% and 2% XG-treated soil. Higher soil plasticity as a result of the XG additive can be a practical implication as it is expected to reduce soil hydraulic conductivity [23; 24].
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 4 Proposed Work
### Hybrid Beamforming for Multi-User mmWave Networks
#### 4.3.1 Multi-User Beamforming Strategies for mmWave Networks
The developed HB is demonstrated to be effective just like the ideal entirely digital beamformer in regard to the highest minimum user rating. We also introduce a novel time division concept hybrid enabled by mmWave transmission regime beamformers developed to excel in both single-cell and doubled; the same number of cells is still being used up to double the amount of RF chains. Current research focuses on developing hybrid beamformers that can maintain both the quality of service and physical layer security for users.
## 2 Understanding WebAssembly
### WebAssembly Binary Format  
Wasm's binary format embodies a compact low-level representation of Wasm code structured to allow rapid decoding and execution [11]. Engineered as the machine-readable blueprint of Wasm programs, this format is crucial for Wasm runtimes when they aim for efficient execution, as highlighted in Figure 3. At its core, the binary format is segmented into multiple sections, each catering to a distinct aspect of the Wasm module. First, the \"Version\" section explicitly determines the version of the format, such as version 1. Diving deeper, the \"Type Section\" encapsulates function signatures used within the module, defining both parameters and return values to ensure smooth interaction with other modules and the runtime. To maintain extensibility, the \"Import Section\" enumerates external functionalities and global variables imported from distinct modules. Following suit, the \"Function Section\" categorizes the module's functions, correlating them with respective function signatures from the \"Type Section\". To foster efficient indirect function calls and dynamic dispatch, the \"Table Section\" stipulates table types, sizes, and element types. Meanwhile, the \"Memory Section\" delineates the linear addressable memory parameters of the module. The \"Global Section\" holds the global variables, defining their characteristics and initial values. Ensuring accessibility to other modules, the \"Export Section\" enlists the module's exportable entities. If initialization is necessitated upon module load, the \"Start Section\" pinpoints the relevant start function. For modules leveraging tables, the \"Element Section\"  
Figure 2: WebAssembly data flow architecture.  
lays out the initialization criteria. The crux of the module's functionality is enshrined in the \"Code Section\", which comprises the compiled code of all functions. To establish a foundation of initial data, the \"Data Section\" manages memory initialization specifics. Lastly, the \"Custom Sections\" offer an avenue for integrating optional data pertinent to the module's broader context.
## 2 Materials and Methods
### Characterizations  
The optical properties of the synthesized complex were studied by ultraviolet-visible (UV-Vis) spectroscopy (V-670, JASCO spectrophotometer, Japan) at room temperature. To identify the various phases of solid material, X-ray powder diffraction (XRD, Rigaku, Cu K\(\alpha\), \(\lambda\) = 1.54178 A, Tokyo, Japan) was applied in the Bragg angle range of \(\sim\)10\({}^{\circ}\) and \(\sim\)80\({}^{\circ}\). The nature of the various chemical bonds and functional groups were investigated by Fourier transform infrared (FTIR, Nicolet, IR300 Thermo Fisher Scientific, Waltham, MA, USA) spectra and elemental analysis (EA Thermal analyzer, Thermo Fisher Scientific, Waltham, MA, USA). Field emission scanning electron microscopy (FESEM, Hitachi 4800, Tokyo, Japan) was used for morphology, and energy-dispersive X-ray (EDX) spectroscopy coupled with FESEM was applied to determine the elemental analysis. Cyclic voltammetry (CV) was used to find out the oxidation states and redox behavior of the Cu (II) complex by using WPG 100 Potentiostat/Galvanostat (WonATech, Seoul, Korea). In the three-electrode system, the CV experiments were executed in 0.1 M tetrabutyl ammonium hexafluorophosphate (TBAPF\({}_{6}\)) as supporting electrolytes an acetonitrile solution with ferrocene as an internal reference. The metal complex deposited on glassy carbon (GCE) was used as a working electrode, and a saturated calomel reference electrode (SCE) and a platinum wire as a counter electrode at \(\sim\)100 mV/s scan rate during the experiment.
## 4 Tensor Decomposition-Based Algorithm of Generating Multiple Virtual Sources
To generate multiple virtual sources simultaneously, the input signal of each virtual is manipulated according to the aforementioned steps (1) to (4), respectively, and the signals of all virtual sources are automatically mixed in the outputs of \(N^{\prime\prime}\) filters. The algorithm has the following features:  
1. The algorithm requires \(N^{\prime\prime}\) convolvers, which are equivalent to \(N^{\prime\prime}\) finite impulse response (FIR) filters; therefore, its efficiency for generating a single virtual source is low. However, the filters are independent of source distances and directions. Virtual sources at different distances and directions can share a common set of filters. In other words, the number of filters is fixed and independent from the number of virtual sources. For generating multiple virtual sources simultaneously, the efficiency of the proposed algorithm is improved in comparison with the conventional algorithm when the number of virtual sources is larger than \(N^{\prime\prime}\). 2. For application to dynamic VAD, when the user turns their head or the position of the virtual source changes (such as the case of a moving virtual source), it is only required to update the weights \(c_{n^{\prime}}\) rather than update the filters or convolvers, avoiding the possibly audible artifacts caused by updating the filters in the conventional algorithm. 3. Weight \(c_{n^{\prime}}\) is related to the source distance and directions by two sets of coefficients, \(u_{dd^{\prime}}^{d}\) and \(u_{mm^{\prime}}^{m}\), respectively, as shown in Equation (14). The source distance and direction can be controlled independently by changing these two sets of coefficients, respectively. 4. HRIRs/HRTFs are continuous functions of the source distance and direction. Practical measurement or calculation usually yields data at discrete distances and directions with certain resolutions. Dynamic VAD requires that the data match the auditory resolution, which can be obtained by spatially interpolating to the measured/calculated data. Spatial interpolation can be implemented online in a dynamic VAD, which increases the computational cost. Spatial interpolation can also be implemented offline, which requires more resources for data storage. Tensor decomposition results in a compact representation of HRIRs/HRTFs, which saves resources for data storage required for offline implementation of spatial interpolation.
## 4 Experimental Results
### Ablation Experiment
#### 4.2.2 Ablation Experiment for Variational Attention Module  
In order to analyze the effectiveness of introducing the variational attention module, this subsection conducts ablation experiments on the reconstruction branch network, and takes the multi-scale feature reconstruction network of the weighted segmentation network, including three branches as the baseline model.  
As can be seen from Table 5, simply adding the self-attention module does not significantly improve the anti-spoofing performance of the model. The area under the AUC curve only increases by 2.4% (compared with 0.9028). However, adding the variational structure can greatly enhance the model's performance, and the equal error rate decreases by 37% (compared with 0.1865). This may be because the changes in the key information of the input samples are within a certain range rather than a fixed value, and the variational structure can separate the bona fide samples from spoof samples by introducing the data distribution of bona fide samples. In addition, this subsection also finds that by combining residual connections and the self-attention module, long-distance dependencies can be established, and the continuous features between the structural contours of bona fide samples can be well utilized.
## 5 Simulation and Experimental Results
### Point Target Simulation
Imaging results of (**e**) RDA, (**f**) AR-RDA, (**g**) TR-RDA, and (**h**) BPA when \(N_{m}=150\). Imaging results of (**i**) RDA, (**j**) AR-RDA, (**k**) TR-RDA, and (**l**) BPA when \(N_{m}=150\). imaging results in Figure 9: the smaller the ISLR, the better the focusing effect of point target \(B\). For peak sidelobe ratio (PSLR) and impulse response width (IRW), BPA outperforms the other two algorithms, and the results obtained by AR-RDA are the poorest. Table 4 shows the time cost of the three algorithms at different \(N_{m}\), which was measured on an Intel(r) CoreTM i5-13500H CPU with a main frequency of 2.6 GHz (Intel Corporation, Santa Clara, CA, USA). It can be observed that as \(N_{m}\) increases, the time cost of BPA increases at a noticeably faster rate compared to the other two algorithms. This indicates that BPA requires higher computational resources. According to the simulation results, the following conclusions can be inferred: The algorithm proposed in this paper outperforms azimuth resampling in terms of accuracy. Additionally, it achieves comparable or even superior imaging effects compared to BPA, all while requiring less computational complexity.
## 7 Results
### Performance on the Zipf's Tail
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Language**} & \multicolumn{4}{c}{**Percentage of All Errors**} & \multicolumn{2}{c}{**\% of Training Dataset**} \\ \cline{2-6}  & **Unseen** & **[1, 100]** & **[101, 10,000]** & **[1, 100]** & **[101, 10,000]** \\ \hline Croatian & 31.42 \(\pm\) 1.65 & 54.23 \(\pm\) 1.27 & 12.67 \(\pm\) 1.45 & 11.79 & 51.02 \\ Czech & 20.42 \(\pm\) 0.24 & 51.07 \(\pm\) 1.31 & 26.43 \(\pm\) 0.80 & 10.62 & 54.54 \\ French & 13.55 \(\pm\) 0.88 & 42.29 \(\pm\) 3.26 & 28.53 \(\pm\) 1.66 & 2.06 & 37.53 \\ \hline Hungarian & 26.78 \(\pm\) 0.33 & 49.96 \(\pm\) 0.59 & 19.67 \(\pm\) 0.69 & & \\ \(\times\)8 \#samples & 35.97 & 43.45 & 17.64 & 9.25 & 52.82 \\ \hline Irish & 46.51 \(\pm\) 6.04 & 35.37 \(\pm\) 0.65 & 13.64 \(\pm\) 3.17 & 23.53 & 45.03 \\ \hline Latvian & 25.94 \(\pm\) 0.95 & 49.87 \(\pm\) 0.16 & 21.35 \(\pm\) 0.77 & & \\ \(\times\)8 \#samples & 33.71 & 43.23 & 20.49 & 22.20 & 56.23 \\ \hline Lithuanian & 26.13 \(\pm\) 0.60 & 47.93 \(\pm\) 1.03 & 24.83 \(\pm\) 1.11 & & \\ \(\times\)8 \#samples & 40.28 & 39.60 & 19.55 & 16.41 & 63.61 \\ \hline Polish & 18.31 \(\pm\) 0.52 & 48.49 \(\pm\) 0.89 & 29.78 \(\pm\) 0.46 & 10.43 & 56.29 \\ Romanian & 16.06 \(\pm\) 0.29 & 43.46 \(\pm\) 0.96 & 27.83 \(\pm\) 1.08 & 6.83 & 48.39 \\ Slovak & 36.68 \(\pm\) 1.00 & 52.96 \(\pm\) 0.29 & 10.01 \(\pm\) 0.70 & 14.98 & 52.18 \\ Spanish & 13.22 \(\pm\) 0.24 & 39.29 \(\pm\) 1.58 & 28.74 \(\pm\) 0.86 & 2.19 & 36.81 \\ Turkish & 24.11 \(\pm\) 0.48 & 54.03 \(\pm\) 1.13 & 21.07 \(\pm\) 0.71 & 11.26 & 59.66 \\ \hline Vietnamese & 1.48 \(\pm\) 0.01 & 7.85 \(\pm\) 0.19 & 58.40 \(\pm\) 0.46 & & \\ \(\times\)8 \#samples & 3.37 & 10.69 & 55.43 & 0.96 & 26.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The distribution of diacritics restoration errors for the different frequencies of the words in the training dataset are shown in the first three columns. The intervals indicate the bounds of how many times the words in this group were encountered in the training dataset. The last two columns indicate the percentages of how much of the training dataset was constituted by these word groups.