esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Nutrient Dynamics across the Sediment-Water Interface
#### 4.1.2 Mechanisms Driving Po4\({}^{3-}\) Concentrations and Fluxes  
In our study, higher PO4\({}^{3-}\) concentrations at L1 and T1 were attributable to WWTP effluents, while the higher concentration at W2 was related to the tributary inflow (Figure 3). However, contributions from PO4\({}^{3-}\) released from sediments were less which was reflected in uptake flux and low regeneration rates (Figure 5). In addition to the PO4\({}^{3-}\) flux determined by the equilibrium concentration, abundant biofilms at T1 and W2 likely reduced the interaction between PO4\({}^{3-}\) and sediment adsorption sites, thereby reducing regeneration from sediments [52; 95].  
We observed that PO4\({}^{3-}\) concentrations were negatively correlated with DO (\(r^{2}=-0.321\), \(p<0.001\)). This finding was associated with the highest mean PO4\({}^{3-}\) concentration in winter when the lowest mean DO concentration occurred (Figure 3b). Also, the reduced PO4\({}^{3-}\) absorption by weaker bioactivity in winter contributed to increased PO4\({}^{3-}\) concentrations [10; 46; 96]. DO conditions affect electron receptors in sediments and under reducing conditions the formation of soluble substances (e.g., Fe(OH)\({}_{2}\)) reduces the adsorption sites resulting in increased water-specific PO4\({}^{3-}\) concentrations [39; 41]. However, the highest mean DO concentration in autumn did not lead to the lowest PO4\({}^{3-}\) concentration and this could be due to increased seasonal precipitation carrying PO4\({}^{3-}\) from urban impervious surfaces into rivers [97].  
There was a positive correlation between PO4\({}^{3-}\) and water pH in the River Tonghui (\(r^{2}=0.257\), \(p=0.040\)). These study reaches were alkaline (pH = 7.5-8.9), causing OH\({}^{-}\) and PO4\({}^{3-}\) to compete for adsorption sites on the sediment surface and exchange with PO4\({}^{3-}\), consequently elevating PO4\({}^{3-}\) concentration in the water [98]. However, for the other two study rivers, positive correlations were not observed as sediment particle size seemed to also affect the P-dynamics. For example, both the sediments at L2 and W2 contained a high proportion of fine particles (Table S9) which were easy to resuspend and could cause PO4\({}^{3-}\) to re-enter the overlying water [99].  
In the 0-3 min exposure, PO4\({}^{3-}\) concentrations in water across the study sites were both higher/lower than the equilibrium concentration resulting in both uptake/regeneration fluxes. In the 3-10 min exposure, decreased DO facilitated releases of PO4\({}^{3-}\) from sediments and regeneration fluxes or decreased uptake fluxes were observed. Significant seasonal variations in DO and PO4\({}^{3-}\) concentrations in water were recorded, but again there were no significant seasonal differences in the PO4\({}^{3-}\) flux, which may be attributed to the small PO4\({}^{3-}\) flux rate found at lower concentrations when compared with results from the other nutrients.
## 4 Experimental Results and Analysis
### Two-Dimensional Image
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Images** & **This Paper** & **Gaussian Random** & **Random Symmetric** & **Random Sparse** & **Bipartite Graph** \\ \hline Lena & 28.96 & 23.95 & 25.10 & 24.98 & 25.75 \\ Baboon & 27.48 & 23.81 & 24.13 & 24.00 & 24.94 \\ Barbara & 30.53 & 26.74 & 27.22 & 26.93 & 28.00 \\ Cameraman & 27.00 & 23.11 & 22.92 & 23.18 & 24.27 \\ Goldhill & 31.64 & 26.56 & 27.46 & 27.11 & 28.48 \\ Peppers & 30.39 & 26.14 & 26.67 & 26.10 & 26.98 \\ Bone1 & 33.49 & 31.34 & 31.36 & 31.18 & 30.59 \\ Bone2 & 34.08 & 29.13 & 30.77 & 29.88 & 31.80 \\ Aerial image1 & 25.66 & 21.13 & 20.52 & 20.27 & 22.67 \\ Aerial image2 & 29.85 & 24.93 & 25.29 & 25.81 & 26.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recovery PSNRs of various images. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Images** & **This Paper** & **Gaussian Random** & **Random Symmetric** & **Random Sparse** & **Bipartite Graph** \\ \hline Lena & 0.842 & 0.723 & 0.757 & 0.731 & 0793 \\ Baboon & 0.835 & 0.719 & 0.723 & 0.723 & 0.763 \\ Barbara & 0.859 & 0.782 & 0.784 & 0.796 & 0.831 \\ Cameraman & 0.772 & 0.605 & 0.644 & 0.616 & 0.709 \\ Goldhill & 0.907 & 0.796 & 0.841 & 0.826 & 0.868 \\ Peppers & 0.870 & 0.765 & 0.808 & 0.793 & 0.823 \\ Bone1 & 0.935 & 0.889 & 0.894 & 0.897 & 0.897 \\ Bone2 & 0.910 & 0.830 & 0.871 & 0.850 & 0.885 \\ Aerial image1 & 0.859 & 0.721 & 0.760 & 0.741 & 0.811 \\ Aerial image2 & 0.840 & 0.638 & 0.712 & 0.693 & 0. Figure 8: Reconstructions of Aerial image2. (**a**) Gaussian random measurement matrix, (**b**) our proposed measurement matrix, (**c**) random binary measurement matrix, (**d**) random symmetric measurement matrix, and (**e**) bipartite graph measurement matrix. Figure 10: Details of reconstructed Bone2: (**a**) Gaussian random measurement matrix, (**b**) our proposed measurement matrix, (**c**) random binary measurement matrix, (**d**) random symmetric measurement matrix, and (**e**) bipartite graph measurement matrix. Figure 6: Reconstructions of Lena: (**a**) Gaussian random measurement matrix, (**b**) our proposed measurement matrix, (**c**) random binary measurement matrix, (**d**) random symmetric measurement matrix, and (**e**) bipartite graph measurement matrix. Figure 7: Reconstructions of Bone2: (**a**) Gaussian random measurement matrix, (**b**) our proposed measurement matrix, (**c**) random binary measurement matrix, (**d**) random symmetric measurement matrix, and (**e**) bipartite graph measurement matrix. Figure 9: Details of reconstructed Lena: (**a**) Gaussian random measurement matrix, (**b**) our proposed measurement matrix, (**c**) random binary measurement matrix, (**d**) random symmetric measurement matrix, and (**e**) bipartite graph measurement matrix.
# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Latency Reduction
#### 4.3.2 X-ray  
Unlike the previously described Angio benchmark, the X-ray benchmark fully displays the plausibility of using the CBPC2 compression method, as it achieved significantly better results than its simpler counterpart. This is depicted on Figure 7. For Wi-Fi and LTE, CBPC2 achieved better results for 9 and 15 percentage points, respectively, than CBPC. For 3G, that margin is smaller but can still be considered significant, as relatively low speeds  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{5}{c}{**Data Size [MB]**} \\ \cline{2-5}  & **Original** & **CBPC2** & **CBPC** & **ZIP** \\ \hline Angio & 1.43 & 0.52 & 0.55 & 0.91 \\ X-ray & 11.88 & 2.66 & 2.95 & 5.03 \\ MR-1 & 95.82 & 24.23 & 26.48 & 43.62 \\ CT & 76.81 & 35.87 & 38.07 & 41.56 \\ MR-2 & 486.83 & 173.41 & 201.52 & 237.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Raw sizes of the original and compressed data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Latency [s]**} \\ \cline{3-6}  & & **Angio** & **X-ray** & **MR-1** & **CT** & **MR-2** \\ \hline \multirow{4}{*}{3G} & Original & 28.41 & 146.57 & 1108.39 & 879.66 & 6197.09 \\  & CBPC2 & 12.01 & 41.97 & 286.48 & 415.87 & 2095.19 \\  & CBPC & 12.94 & 45.06 & 312.58 & 443.33 & 2242.86 \\  & ZIP & 20.15 & 68.61 & 506.64 & 483.02 & 2861.06 \\  & Original & 3.28 & 10.46 & 58.29 & 43.58 & 532.27 \\  & CBPC2 & 1.41 & 3.27 & 14.78 & 21.41 & 156.95 \\  & CBPC & 1.41 & 4.76 & 18.00 & 23.57 & 211.59 \\  & ZIP & 2.17 & 5.84 & 30.32 & 26.73 & 334.01 \\  & Original & 1.06 & 5.94 & 36.24 & 28.58 & 224.94 \\  & CBPC2 & 0.49 & 1.46 & 8.89 & 14.75 & 82.40 \\  & CBPC & 0.55 & 2.04 & 12.73 & 17.17 & 84.78 \\  & ZIP & 0.71 & 3.87 & 18.77 & 19.87 & 101.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Raw latencies in miliseconds.  
of the 3G network could use each increase in latency reduction. When comparing our custom compression methods with ZIP compression, using CBPC and CBPC2 led to better results regarding latency reductions. The margin was the smallest on the LTE network and larger on Wi-Fi and 3G networks. For this benchmark, latency reduction varies greatly for ZIP compression, ranging from 65% for a Wi-Fi network to 47% for a 3G network. The largest margin is achieved on the Wi-Fi network as the CBPC2 compression method reduces latency to just 25%, relative to raw image transfer, and for 40 percentage points with respect to ZIP compression.
# Evaluating Deep Learning Techniques for Natural Language Inference
## 5 Experimental Results
### Results on SICK  
The best DAM implementation demonstrated 81.10% accuracy on the development set and 83.37% on the test set. It was trained with a hidden layer size of 300, a batch size of 16, a learning rate of 0.01, a dropout rate of 0.2, and a weight decay of 10\({}^{-5}\) for 34 epochs. Adding the intra-sentence attention did not improve performance, as our best implementation achieved 83.15% accuracy on the test set.  
The best test set performance of the ESIM model was 80.81%, a slight drop in performance compared with DAM. We trained this implementation with a hidden state size of 300, a batch size of 16, the Adam optimizer with a learning rate of 0.0004, and a dropout rate of 0.5 for 15 epochs.  
BERT-base's best performance on the test set was 87.42%, a large improvement from the previous models. It was trained with a batch size of 32 and a learning rate of 3 \(\times\) 10\({}^{-5}\) for 640 steps, with the first 140 of those being warmup steps. Our best BERT-large implementation achieved 89.85% accuracy on the test set. We trained with a batch size of 16 and a learning rate of 2 \(\times\) 10\({}^{-5}\) for 1200 steps with 140 warmup steps.  
With RoBERTa-base, the best test set performance we achieved was 89.59%, a 2% jump from BERT-base. We trained the model with a batch size of 64 and a learning rate of 2 \(\times\) 10\({}^{-5}\) for 420 steps, with the first 70 steps as warmup steps. With RoBERTa-large, our best implementation demonstrated 91.50% accuracy on the test set, and it was trained with a batch size of 16, a learning rate of 10\({}^{-5}\), and 140 warmup steps for a total of 900 steps (Figure 2).  
Our best implementation of ALBERT-base achieved 89.06% accuracy on the test set. We trained the model with a batch size of 32, a learning rate of 2 \(\times\) 10\({}^{-5}\), and 140 warmup steps for a total of 720 steps. For ALBERT-large, our best implementation demonstrated 89.85% accuracy, which is the same as BERT-large, and was trained with a batch size of 16, a learning rate of 2 \(\times\) 10\({}^{-5}\), and 140 warmup steps for a total of 1200 steps.  
Figure 1: (**a**) Validation accuracy graph and (**b**) training loss graph of best model (RoBERTa-Large) for SNLI.
## 3 Engineering Example
### Project Profile
#### 3.1.1 Distribution in Soil Layers and Calculation Parameters  
According to the Geotechnical Investigation (Detailed Survey) Report on Water intake and Drainage Structures during the Preliminary Design Stage of the First Phase of Sanmen Nuclear Power Plant, the quaternary system is widely distributed in the coastal plain and terrace, with different thickness mainly consisting of marine deposits and eluvium. The maximum thickness shown by this investigation is 43.6 m. The soil layer in the tunnel excavation area is divided into flowing soil, silt, clay, silty clay, silty clay mixed with gravel, moderately weathered rock, andesite basalt, etc. The engineering geological section is displayed in Figure 8, and the parameters of the soil layer and concrete are shown in Table 1. Equivalent linear parameters of the soil mass are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Soil Layer** & **Density (kg/m\({}^{3}\))** & **Static Modulus of Elasticity (E/Pa)** & **Static Poisson’s Ratio (\(\mu\))** & **Dynamic Modulus of Elasticity (E\({}_{d}\)/Pa)** & **Dynamic Poisson’s Ratio (\(\mu_{d}\))** \\ \hline
1 & Concrete & 2500 & 3.25 \(\times\)\(10^{10}\) & 0.20 & 4.225 \(\times\)\(10^{10}\) & 0.20 \\
2 & Silt & 1610 & 1.00 \(\times\)\(10^{7}\) & 0.30 & 1.95 \(\times\)\(10^{7}\) & 0.45 \\
3 & Muddy clay & 1750 & 1.30 \(\times\)\(10^{7}\) & 0.30 & 2.91 \(\times\)\(10^{7}\) & 0.45 \\
4 & Clay & 1830 & 2.07 \(\times\)\(10^{7}\) & 0.32 & 1.68 \(\times\)\(10^{8}\) & 0.45 \\
5 & Silty clay & 1950 & 2.61 \(\times\)\(10^{7}\) & 0.30 & 1.49 \(\times\)\(10^{8}\) & 0.45 \\
6 & Moderately & 2550 & 2.46 \(\times\)\(10^{7}\) & 0.24 & 4.60 \(\times\)\(10^{10}\) & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Lining and soil layer parameters.  
Figure 8: Geological stratigraphy of water-intake shield tunnel.  
Figure 7: Lining segments of shield tunnels: (**a**) segments of shield tunnels, (**b**) splicing method of staggered joints, and (**c**) straight bolt connection.
## 4 Network Model  
In the previous section some of the potential benefits of SCF relaying were detailed. We argued that when delaying message deliveries does not affect the perceivable user experience, then forwarding paths that employ the store-carry and forward relaying scheme can be realized with great potential for improving system performance in cellular networks. To quantify the potential gains of the proposed scheme we focus in the sequel in the case of a single cell and differ the multicell/multiaccess performance gains as part of a future work.  
Figure 3: SCF relaying in a multiaccess scenario.  
Without loss of generality we consider a one dimensional realization of a single cell of radius \(R\) meters. Note that for the mathematical formulation of this networking problem that we derive in the sequel, the one dimensional realization assumption is not a necessity; it is merely used for ease of interpretation. For the case of uplink, we assume that there are \(\mathcal{M}=\{1,\ldots,M\}\) active users, uniformly distributed across the cell diameter, each with a single message of size \(F\) (bits) to communicate to the BS. Further, we consider \(\mathcal{N}=\{1,\ldots,N\}\) vehicular relays in each direction of a bidirectional road of length \(H=2R\). For simplicity we assume that all users are static while vehicles travel on the stretch of road with velocity \(v_{i},i\in\mathcal{N}\). Future vehicle positions are estimated using [PERSON]'s car-following model. Note that any other mobility model can be used to capture future vehicle positions. In fact, a highly accurate mobility model will strengthen the robustness of the forwarding decisions being made. Finally we assume that all vehicles have enough buffer capacity to store all messages forwarded to them and that a VR can transmit to, and receive from, a single other VR at an instance in time.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 6 Focusing of Secondary Mesons
### Quadrupole Multiplets  
An alternative to magnetic horns is quadrupole multiplets, forming an \"acceptance stage\" for the hadrons just downstream of the production target. Differently from a horn, a single quadrupole focuses the particles along one axis and defocuses them on the other: a net focusing effect in both transverse dimensions can be achieved with a combination of them (from two quadrupoles onward). Three quadrupoles in series (i.e., a quadrupole triplet) are a common choice as a focusing stage because they allow achieving similar focusing proprieties both in the vertical and horizontal plane. Using doublets or triplets, the produced secondaries are accepted and collimated. The particles are charge and momentum-selected by inserting bending dipoles and slits along the beamline, and re-focused towards the decay volume. Quadrupoles, dipoles and other standard accelerator magnets are referred to as _static focusing elements_ because they all run at constant currents (or are pulsed for long times, \(O(s)\)) for the entire duration of the experiment.  
In general, the quadrupole multiplets have a smaller acceptance than the horns, except for \(\mathcal{O}(100)\) GeV/c secondaries [124]. Still, they offer major advantages. There is no need to pulse them and, therefore, they can be used in combination with a \(\mathcal{O}(\mathrm{s})\)-long slow extraction. Standard large-aperture quadrupole magnets with gradients of the order of \(\sim\)10 T/m are significantly less expensive, simpler, and robust than horns. Even if horns are a must for long-baseline experiments, the flux requirements of short-baseline experiments can be fulfilled by well-designed static systems even in the low-energy regime [125]. They can operate in DC or, generally, low currents of the order of 1 kA, thus they do not produce a major thermal load. They are particularly suitable for monitored neutrino beams because they work in slow extraction operation mode and reduce the overall length of the beamline from the target to the decay volume. Such length determines the number of kaons whose lepton cannot be monitored because the parent decays before the tunnel instrumentation.
## 4 Results and Discussion
### Broadband Dielectric Spectroscopy  
It is evident from the results (Figure 13) that the synthesized material (A) shows the stability of the real part of complex permittivity and its value is lower in comparison with commonly used insulation systems (B, C). This fact is very important for potential application in the industry. The lower value of real part of complex permittivity, which represents relative permittivity of insulation could cause lower local overloading if the delamination occurs. This fact can be easily illustrated by using of Maxwell-Wagner model for double layer insulator. In the case of presence e.g., air-filled delamination, the maximal field strength in the cavity is lower if the surrounding material has also lower permittivity.  
If the attention is focused on the dielectric processes it is very difficult to precisely define the individual polarizations which are visible in the imaginary part of the complex permittivity, because there is a combination of thermoplastic reinforcement with a thermosetting resin. However, these polarizations are probably caused due to the local motion of small molecular units, side chains and aromatic substitution groups, which contribute slightly to the dielectric losses [63; 64] or due to the Brownian motion of the chain segments [65] in the case of higher temperatures. In the case of a loss factor, it is visible that the surfaces of investigated materials overlap to a large extent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **min \(E_{p}\) (kV/mm)** & **max tan \(\delta\) (-)** & **min \(\rho_{v}\) (\(\Omega\cdot\)m)** \\ \hline A—Nanostructured micaless microcomposite & 53.2 & 0.00452 & \(4.84\times 10^{14}\) \\ B—PET reinforced mica composite [28] & 50 & 0.015 & \(1\times 10^{13}\) \\ C—Glass reinforced mica composite [28] & 35 & 0.015 & \(1\times 10^{13}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum/maximum values of key dielectric parameters of investigated composites.  
Figure 12: Normal probability plots of investigated parameters of nanostructured micaless microcomposite.
# Sensitivity Analysis of Numerical Coherency Model for Rock Sites
## 3 Sensitivity Study
### Depth of Numerical Model  
In numerically deriving the coherency function, the depth of the model may influence the surface variability. However, the effect of the depth of the numerical model has not yet been investigated. To assess its impact on plane-wave coherency, a sensitivity analysis was conducted for depths ranging from 50 m to 200 m. Figure 8 presents the results of the sensitivity analysis with respect to the analysis depth for CV = 20 and CLh = 20. The results indicate that when the analysis depth exceeds 100 m, the calculated coherency converges and that further increases in depth have a marginal influence on the calculated response. This suggests that when the physical properties and spatial variability of the site remain constant, the analysis results converge once the depth exceeds 100 m. However, this result applies only to the Pinyon Flat site. In practice, borehole depth and \(V_{\mathrm{s}}\) profile data vary significantly across sites. Therefore, a sensitivity analysis on the depth of the model should be conducted when developing a numerical coherency model.
## 3 Additional Damping Controller Design
### Filter  
The filter is used to extract the sub-synchronous signal based on the oscillating component of the feedback signal. As the core element in this process, it allows specific  
Figure 2: System impedance modeling.  
sub-synchronous frequency components of the signal to pass through while greatly attenuating other non-essential frequency components [22]. Through this process, the filtering link can ensure that the control system accurately identifies and processes the SSO, provides accurate and reliable signal input for the subsequent gain and phase compensation links, optimizes the interaction between the DFIG and the power grid, and improves the stability and performance of the system. In this study, the filtering link in the SDC includes two parts: a band-stop filter and a band-pass filter. The combined transfer function H\({}_{\text{F}}\)(s) is  
\[\begin{split} H_{\text{F}}(s)&=H_{\text{P}}(s) \cdot H_{\text{S}}(s)\\ H_{\text{P}}(s)&=\frac{\frac{\lambda_{\text{P}}^{ \text{L}}}{\lambda_{\text{P}}^{\text{L}}+\left(\frac{s}{\omega_{\text{P}}} \right)^{2}}}{1+\frac{2\zeta_{\text{S}}}{\omega_{\text{S}}}+\left(\frac{s}{ \omega_{\text{S}}}\right)^{2}}\\ H_{\text{S}}(s)&=\frac{1+\left(\frac{\lambda_{ \text{S}}}{\omega_{\text{S}}}\right)}{1+\frac{2\zeta_{\text{S}}}{\omega_{ \text{S}}}+\left(\frac{s}{\omega_{\text{S}}}\right)^{2}}\end{split} \tag{5}\]  
where H\({}_{\text{S}}\)(s) is the transfer function of the band-stop filter and H\({}_{\text{P}}\)(s) is the transfer function of the band-pass filter.