esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Deep Learning Models
#### 3.3.2 Alternative Approaches
Most of the studies are based on private, local datasets related to the local population, each having its own limitations due to small dataset size and variability, the use of different 2D vs. 3D approaches, the inclusion of lesion-only slices or complete slices, and differing validation procedures. Despite these many differences, an attempt at raw quantitative comparison across different model categories has been made to highlight the main trends. Only studies reporting mean or median Dice scores (assuming a negligible deviation from a Gaussian distribution) have been considered. For the main approach categories, a comprehensive Dice score has been estimated by averaging scores using the sample size of the test set as a weight, prioritising studies with larger and more inclusive test sets. The main results are summarised in Table 2.  
Early unsupervised methods achieved a mean Dice score of \(0.75\pm 0.01\), surpassing traditional supervised methods, which achieved \(0.70\pm 0.03\). However, the introduction of deep learning methods further improved segmentation performance, achieving a higher overall Dice score of \(0.79\pm 0.03\) for U-Net-based architectures, and an even higher score for alternative deep learning approaches, with a mean Dice score of \(0.83\pm 0.04\). These results suggest the potential for further advancements in model architectures and training strategies. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Early Unsupervised** & **Traditional Supervised** & **U-Net and Derived** & **Alternative DL** \\ \hline \(0.75\pm 0.01\) & \(0.70\pm 0.03\) & \(0.79\pm 0.03\) & \(0.83\pm 0.04\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean DSC and standard error across the main approach categories.
# DPMF: Decentralized Probabilistic Matrix Factorization for Privacy-Preserving Recommendation
## 3 The Proposed Model
### Decentralized PMF
```
Input: implicit feedback (\(R\)), explicit feedback (\(M\)), weight matrix for implicit feedback (\(W\)), learning rate (\(\theta\)), user adjacency matrix (\(E\)), neighbors of user \(i\) (\(\mathcal{N}^{d}(i)\)), regularization parameter (\(\lambda_{U}\), \(\lambda_{V}\)\(\lambda_{Z}\), \(\lambda_{Q}\), \(\lambda\)), number of iterations (\(T\)) Output: latent factor factor vectors \(U_{i}\), \(vp_{j}^{i}\), \(va_{j}^{i}\), \(zp_{k}^{i}\), \(zq_{k}^{i}\)
1 for\(i=1\) to \(m\), \(j=1\) to \(n\), and \(k=1\) to \(n\)do
2 Initialize \(U_{i}\), \(vp_{j}^{i}\), \(va_{j}^{i}\), \(zp_{j}^{i}\), \(zq_{j}^{i}\), \(Q_{k}^{i}\)
3 end for
4 for\(t=1\) to \(T\)do
5 Derive the implicit feedback matrix \(M\) and the explicit feedback matrix \(R\) from the observed data;
6 Shuffle training data;
7 foreach user-item pair \((i,j)\) in \(R\), \(P\), and \(N\)do
8 Compute gradients according to (15)-(18) Update \(U_{i}\) by \(U_{i}\gets U_{i}-\theta\frac{\partial C}{\partial U_{i}}\)
9 if\(I_{ij}^{R}=1\)then
10 for\(k\) that satisfies \(I_{ik}^{R}=1\)do
11 Update \(Q_{k}\) by \(Q_{k}\gets Q_{k}-\theta\frac{\partial C}{\partial Q_{k}}\)
12 end for
13 Update \(vp_{j}^{i}\) by \(vp_{j}^{i}\gets vp_{j}^{i}-\theta\frac{\partial C}{\partial V_{j}^{i}} \frac{\partial V_{j}^{i}}{\partial vp_{j}^{i}}\)
14 Update \(va_{j}^{i}\) by \(va_{j}^{i}\gets vq_{j}^{i}-\theta\frac{\partial C}{\partial V_{j}^{i}} \frac{\partial V_{j}^{i}}{\partial vq_{j}^{i}}\)
15 end for
16 else
17 Update \(zp_{j}^{i}\) by \(zp_{j}^{i}\gets zp_{j}^{i}-\theta\frac{\partial C}{\partial Z_{j}^{i}} \frac{\partial Z_{j}^{i}}{\partial vp_{j}^{i}}\)
18 Update \(zq_{j}^{i}\) by \(zq_{j}^{i}\gets zq_{j}^{i}-\theta\frac{\partial C}{\partial Z_{j}^{i}} \frac{\partial Z_{j}^{i}}{\partial zq_{j}^{i}}\)
19 end for
20 foruser \(i^{\prime}\) in \(\mathcal{N}^{d}(i)\)do
21 ifReceive \(\frac{\partial C}{\partial vp_{j}^{i}}\) from user \(i\)then
22\(vp_{j}^{i^{\prime}}\gets vp_{j}^{i^{\prime}}-\theta E_{ii^{\prime}} \frac{\partial C}{\partial vp_{j}^{i}}\)
23 end if
24 ifReceive \(\frac{\partial C}{\partial zp_{j}^{i}}\) from user \(i\)then
25\(zp_{j}^{i^{\prime}}\gets zp_{j}^{i^{\prime}}-\theta E_{ii^{\prime}} \frac{\partial C}{\partial zp_{j}^{i}}\)
26 end if
27
28 end for
29
30 end for return\(U_{i}\), \(vp_{j}^{i}\), \(va_{j}^{i}\), \(zp_{k}^{i}\), \(zq_{k}^{i}\), \(Q_{k}^{i}\)
```  
**Algorithm 2**Decentralized PMF Algorithm
## 5 Methodology
### Multi-Objective Symbolic Regression  
Multi-objective symbolic regression (MOSR) enhances traditional SR by optimizing multiple criteria simultaneously, rather than just focusing on prediction error. This approach addresses the issue of overly complex expressions that may not generalize well, offering a better balance between accuracy, simplicity, and interpretability. This work used aMOSR algorithm [51; 52] built upon the Non-dominated Sorting Genetic Algorithm II (NSGA-II) [53], an selective multi-objective genetic algorithm where program ranking is based on non-dominance criteria and crowding distance metrics. Unlike single-objective approaches, which prioritize a single metric such as prediction accuracy, MOSR optimizes for both prediction accuracy and complexity simultaneously, ensuring that the resulting expressions are both accurate and interpretable. The algorithm sorts solutions based on Pareto optimality, where non-dominated solutions are assigned to the first Pareto front and subsequent fronts are populated with solutions that are less optimal but still offer useful trade-offs. The crowding distance metric promotes diversity in the population, encouraging a more thorough exploration of the solution space. To handle constants within expressions, the algorithm includes a local optimization procedure. This process uses a single-neuron neural network where the expression itself acts as the activation function, and constants are optimized using the Adam optimizer [54] with a binary cross-entropy loss function. This integration of genetic programming with gradient-based optimization enhances the search for more accurate and interpretable mathematical expressions.
## 3 Results on the Generation of Standard Hybrid Vector Beams
### Case with Input Circularly Polarized Vortex Beam  
Let us now analyze the case with \(\ell=2\) i.e., the input beam is \(\mathbf{V}_{in}=L{G_{0}^{2}}^{\mathbf{A}}\mathbf{e}_{\mathbf{R}}\). This input state was obtained by illuminating with LCP a static q-plate of \(q=1\) (Thorlabs WPV10L-633) inserted in the  
Figure 3: Results for the QW q-plate illuminated with an RCP Gaussian beam (\(\ell=0\)). (**a**) Results in the near field; (**b**) results in the far field. Simulated and experimental polarization maps, with right and left-handed ellipses depicted in magenta and cyan, and linear polarization in green. Experimental images captured for different orientations of the analyzer, indicated as white arrows (NA indicates no analyzer).  
PSG. According to Equation (12) this input RCP vortex beam now provides an output beam given by \(\mathbf{V}_{out}=\frac{1}{\sqrt{2}}\Big{(}LG_{0}^{2}\mathbf{e_{R}}-iLG_{0}^{1} \mathbf{e_{L}}\Big{)}\). This hybrid vector beam is the superposition of two vortex beams of charges \(\ell_{R}=2\) and \(\ell_{L}=1\).  
Figure 4 shows the corresponding numerical and experimental results. As the fractional order is once again \(m=1/2\), the NF shows the same polarization pattern as in the previous case with \(\ell=0\) (Figure 3a). However, an important difference in the NF captures is the phase singularity that now appears in both RCP and LCP components. The FF images more clearly reveal the different vortex content in the RCP and LCP components, observed in the different diameter of the focused beam in these components. Unlike in the previous case, now the Gouy phase polarization rotation is \(\frac{1}{2}\Delta\psi_{G}=\frac{\pi}{4}\), and consequently, the FF shows a \(45^{\circ}\) counterclockwise polarization rotation with respect to the NF. Again, this is clearly seen in the \(90^{\circ}\) clockwise rotation of the FF intensity patterns compared to the NF patterns obtained for each linear analyzer. The experimental and simulated polarization maps also evidence this polarization rotation. This rotation is exact at the L-line that separates the inner and outer regions with opposite handedness.
# Railway Lines across the Alps: Analysis of Their Usage through a New Railway Link Cost Function
## 1 Introduction  
Most railway lines crossing the Alps were built in the nineteen century and are often steep and tortuous. Almost all these lines are operated in double traction. Some of them are operated in triple traction, for example: the Brenner line on the Austrian side, and the Frejus line on both the Italian and the French side. This greatly increases not only monetary costs, but also travel times. On the other hand, several improvement projects have been developed: two new base tunnels have already been opened (the Gotthard and the Lotschberg ones), but several other base tunnels and new lines are currently under construction or planned.  
In this paper, a new cost function for railway links has been considered, then, after a deep analysis of the geometry and operation of each line crossing the Alps, the importance of each Alpine railway pass is evaluated.  
The minimum cost paths between the most important rail terminals of Italy, from one side, and the most important rail terminals of Central-Southern Europe and of Central-Eastern Europe, from the other side, have been calculated. Which Alpine pass is crossed by each minimum cost path has been determined. Minimum cost routes have been calculated by means of the Dijkstra algorithm. Two scenarios of study have been taken into account, namely: a 'current scenario', which involves only railway lines currently in operation, and a 'project scenario', which involves also railway lines under construction or planned. In order to do so, the railway network of a large part of Europe has been modelled. It comprises northern Italy, large part of France, Germany, Belgium, the Netherlands,Luxembourg, Austria, Switzerland, Czech Republic, western parts of Slovakia and Hungary, Slovenia, northern Croatia, and northern Serbia.  
The analysis of the usage of Alpine passes, presently and after the opening of the new base tunnels and lines, currently under construction and planned, has been composed of the following main phases:  
1. Development of a new monetary cost function for freight railway links;
2. Analysis of the geometry and operation characteristics of each line crossing the Alps;
3. Construction of the railway network model of a large part of Europe;
4. Calculation of the minimum cost paths, based on travel times, as well as monetary and generalized costs, between pairs of Italian and European railway terminals, which require crossing the Alps.
# Principles and Application of Polyimide Fiber Bragg Gratings for Surface Strain Measurement
## 2 Research on the Strain Transfer Model
### Analysis of Strain Transfer Influential Factors  
The diameter of polyimide fiber is around 130 \(\upmu\)m, and the elastic modulus is 72 GPa [24; 25]. These two parameters can be regarded as constants. According to Equation (11), the only decisive factors in strain transfer were the shear modulus of the bonding layer, the thickness of bonding layer, the bonding length, and the fiber width. When the bonding length and width of fiber are confirmed, the average strain transfer ratio of bare polyimide FBG, as shown in Figure 9, increases as the shear modulus increases.  
Figure 8: Average strain transfer ratios of different models.  
Figure 7: Strain transfer ratio along the gauge length of FBGs of different models.  
As shown in Figure 10, when the bonding length and width of fiber are fixed, the average strain transfer rate decreases as the thickness of the adhesive layer increases.  
As shown in Figure 11, when the shear modulus of adhesive layer and the thickness of the bonding layer between fiber and substrate were fixed, the average strain transfer rate increased as bonding length and width increased.  
As shown in Figure 12, the thickness of the upper adhesive layer of the fiber had little influence on the average strain transfer rate. It is almost negligible.  
Figure 11: The average strain transfer rate changed with the length of bonded.  
Figure 9: The average strain transfer ratio changed with the shear modulus.
## 2 Materials and Methods  
### Site Descriptions  
This study was conducted using EC flux data from an agricultural site in the midwestern part of the United States, near Champaign, Illinois (40.0062, 88.2904 ). The site (US-Bo1) is part of the research network in the global energy and water cycle experiment described by [PERSON] [28] as well as the international network for terrestrial carbon fluxes described by [PERSON] _et al._[13]. This site has been operational since 1996, which aims to obtain long-term information about energy, water, and CO\({}_{2}\) fluxes and their environmental controls for improving soil-vegetative-atmosphere transfer models. The field was managed as continuous no-till with alternating years of corn and soybean crops (corn in the odd years and soybean in the even years). Soil type is silt loam consisting of three soil series (Dana, Flanagan, and Drummer). The micrometeorological tower placement in the field provides a fetch over a continuous crop with 300 m to the west, 700 m to the east, 500 m to the south, and 200 m to the north. For the corn and soybean crops, plant heights are typically 3.0 m and 0.9 m with the peak leaf area index (LAI) of 5.5 and 5, respectively. Climate of the region is temperate continental with the prevailing winds during the summertime from the south. A detailed description of the site can be found in [PERSON] [29] and [PERSON] _et al._[30].  
To independently evaluate the transferability of the developed model, the crop site (US-Ro1; 44.7143, 93.0898 ) located at the University of Minnesota's Rosemount Research and Outreach Center, approximately 25 km south of St. Paul, Minnesota, was also used. Waukegan silt loam is the main soil type with high organic carbon content (about 2.6%) on the surface layer and varying thickness between 0.3 m and 2.0 m with coarse outwash sand and gravel. This field has the same temperate continental climate with a regional growing season that usually starts in late May and ends in October. In contrast to US-Bo1, this site was a conventional-till management corn/soybean annual rotation field (corn in the odd years and soybean in the even years). Apart from this, both sites are rain-fed farming systems. Further information can be obtained from [PERSON] [10] and [PERSON] _et al._[31].
# Programmable Data Plane Applications in 5G and Beyond Architectures: A Systematic Review
## 4 Findings and Discussion
### Obtained Results Discussion
#### 4.4.6 Other Uses  
In [100], the proposed data placement approach is shown to achieve improved load balancing and below 30% path lengths when compared to alternative methods. While the data retrieval scheme introduced in [101] achieves a reduction of 50.21% in lookup paths and 92.75% in false positives compared to other state-of-the-art implementations. On the other hand, the publish-subscribe scheme proposed in [104] is found to result in more homogeneous traffic and lower state replication latency, although no latency measurements are included. The service function chaining (SFC) scheme introduced in [98; 99] can classify and create a service sequence for traffic flows, which results in an increase of only 1% in flow completion time (FTC), a reasonable impact for 5G architectures. In [97], experimental results show that their proposed framework can reduce handover time by up to 18% and 25% for two and three sequences, respectively. According to the findings reported in [65], the average reduction in handover delay achieved by the proposed solution was 2.11 times compared to a host-based approach. Finally, the beamforming method presented in [103], which employs programmable devices, can calculate angle approximations within acceptable empirical error distributions for moderate user movement speeds of less than 90 km/h and control cycle times less than 100 ms. It is worth noting that the resource consumption of the method depends solely on the size of the TCAM table used in the programmable device.
## 8 System Implementation Experimental Studies
### Energy Consumption Case Study
This result emphasizes the flexibility of the submatrix size selection that is given by the proprietary QC-RL-LDPC design framework. It can be inferred that the QC-RL-LDPC system exhibits higher energy efficiency compared to fixed-rate LDPC in high-SNR scenarios. This is due to its reduced transmission rate and encoding time. Additionally, it surpasses LDPC in low-SNR scenarios (below approximately 0 dB) since it can still effectively correct errors while utilizing more parity bit chunks. It should be especially desired to use rate-adaptive coding in situations where the channel SNR is not known in advance. It then improves not only the error correction possibilities, but also the enerzy efficiency. Figure 12: Transmitter energy consumption per information bit—codes of information block length 144b. Figure 13: Transmitter energy consumption per information bit—codes of information block length 256b.
## 2 Materials and Methods
### AHL Extraction from Marinobacter Supernatants  
Methods of extraction were based on those described by [PERSON] et al. [45]. Individual _Marinobacter_ strains (29 in total) were inoculated in 10 mL of marine broth and incubated for 72 h. Cells were pelleted by centrifugation at 8000 rpm for 15 min, with subsequent decanting of the supernatant into acid-washed glass culture tubes. AHLs were extracted from acidified supernatant by adding 5-25 mL of dichloromethane or ethyl acetate and placing culture tubes in an orbital shaker for 1-2 h to allow for phase mixing. The organic phase removed by either rotary evaporation or dried using nitrogen gas, was reconstituted in 0.5 mL acetonitrile and transferred to glass vials. Extracts were stored at \(-20\)\({}^{\circ}\)C until assayed. Negative controls were produced by performing the extraction procedure on sterile MB and LB media.
## 3 Results
### Signal from M. Extensor Digitorum Communuis
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Gait**} & \multirow{2}{*}{**Signal Variation**} & \multirow{2}{*}{**Amplitude [mV]**} & \multirow{2}{*}{**RMS [mV]**} & \multirow{2}{*}{**MF [Hz]**} & \multirow{2}{*}{**SNR [dB]**} \\ \cline{3-6}  & & & & & \\ \hline Walk & Raw & 142.5 (110.5; 160.0) a  & 28.8 (24.9; 33.8) b  & 72.4 (58.7; 84.8) a  & 13.3 (12.3; 14.0) a  \\  & Low-pass 10 Hz & 34.2 (29.3; 39.8) b  & 21.7 (19.2; 24.5) b  & 5.6 (5.0; 6.1) b  & 12.5 (11.0; 13.2) b  \\  & Bandpass 40–450 Hz & 117.6 (96.3; 143.0) c  & 23.4 (18.0; 28.4) b  & 109.1 (100.4; 123.3) c  & 16.6 (14.7; 17.9) c  \\  & Bandpass 7–200 Hz & 114.2 (91.9; 146.3) c  & 26.2 (21.3; 30.7) c  & 60.9 (50.2; 73.5) a  & 13.3 (12.1; 13.9) a  \\ \cline{2-6}  & \(p\)-value & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ \hline Trot & Raw & 1040.0 (841.0; 1227.0) a  & 191.7 (154.4; 236.8) a  & 62.6 (53.7; 73.1) a  & 13.9 (11.8; 15.1) a  \\  & Low-pass 10 Hz & 312.1 (230.5; 354.0) b  & 144.0 (118.2; 179.7) b  & 7.0 (5.9; 8.0) b  & 12.6 (10.8; 14.9) b  \\  & Bandpass 40–450 Hz & 844.0 (653.8; 1026.0) c  & 156.3 (116.0; 199.1) c  & 89.0 (79.3; 100.7) c  & 16.9 (146; 18.5) c  \\  & Bandpass 7–200 Hz & 924.3 (738.6; 1116.0) d  & 186.7 (149 ; 227.3) d  & 58.2 (51.5; 71.7) d  & 14.2 (12.2; 15.7) d  \\ \cline{2-6}  & \(p\)-value & & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ \hline \end{tabular}
\end{table} 
Table 3: Median and ranges (lower quartile; upper quartile) signal-feature data (amplitude, root mean square (RMS), median frequency (MF), signal-to-noise ratio (SNR)) compared between signal variants (low-pass filtered with a 10 Hz cut-off frequency, bandpass filtered with a 40–450 Hz cut-off frequency, bandpass filtered with a 40–450 Hz cut-off frequency, bandpass filtered with a 7–200 Hz cut-off frequency) for signals obtained from the m. extensor digitorum communis during walking and trotting. For the m. extensor digitorum communis, mean signal loss decreased, and mean residual signal increased across low-pass filtering at 10 Hz, bandpass filtering at 40-450 Hz, and bandpass filtering at 7-200 Hz in both gaits, as illustrated in Figure 3A,E. During both gaits, signal loss was highest after low-pass filtering at 10 Hz, lower after bandpass filtering at 40-450 Hz, and lowest after bandpass filtering at 7-200 Hz, as shown in Table 4.
# Imprint of Mesoscale Eddies on Air-Sea Interaction in the Tropical Atlantic Ocean
## 2 Materials and Methods
### Altimetry Data and Mesoscale Eddy Tracking  
In the present study, mesoscale eddies were detected and tracked from daily absolute dynamic topography (ADT) maps, which is a multimission satellite altimetry product, optimally interpolated onto a \(0.25\lx@math@degree\times 0.25\lx@math@degree\) longitude/latitude grid [36]. This product is freely distributed by the Copernicus Marine Environment Monitoring Service and available on [[https://marine.copernicus.eu/](https://marine.copernicus.eu/)]([https://marine.copernicus.eu/](https://marine.copernicus.eu/)) (accessed on 15 February 2023).  
The mesoscale eddies were detected using the eddy detection algorithm by [2; 3], which defines the center of an eddy as a local maximum for AEs or a local minimum for CEs in the ADT maps, and the edge as the outermost closed ADT contours surrounding the center. After an eddy is detected, its properties such as amplitude, radius and EKE are derived. Eddy amplitude is defined as the absolute value of the ADT difference between the eddy center and the averaged ADT along the eddy edge. The radius of an eddy corresponds to the radius of an equivalent circular vortex having the same area, delimited by the eddy edge, while the EKE of an eddy is the average eddy kinetic energy over the considered eddy.  
The eddy trajectories were reconstructed using the eddy tracking algorithm by [8]. To ensure robust results, we only considered eddies that lasted for a minimum of 30 days and had a median amplitude and radius greater than 1 cm and 30 km respectively, all along their trajectory. This approach is consistent with previous studies in TAO such as [1; 13].