esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model and Methods
### Bayesian Hierarchical Modelling Framework
#### 3.2.1 Likelihood Distribution  
The deterministic model (2) relates physical input parameters to measurable output quantities. Thus, for given input parameters \(\mathbf{\Theta}=(R_{\text{th}},\eta_{h},I_{f},V_{f})\),  
\[\mathcal{M}:\mathbf{\Theta}\mapsto\tilde{y}=\mathcal{M}(\mathbf{\Theta}) \tag{3}\]  
predicts a temperature change \(\tilde{y}=\Delta\tilde{T}_{0}\), where \(\mathcal{M}(\cdot)\) represents a forward operator. In practice, forward model predictions deviate from actually observed values \(y_{i}=\Delta T_{0i}\), \(i=1,\ldots,n\), due to measurement noise and randomness in the data-generating process in general, even if all input parameters \(\mathbf{\Theta}\) were perfectly known. Forward model predictions require precise knowledge about the inputs involved. However, model (2) is subject to uncertainties and variabilities through the inputs. In order to learn about these physically fixed, though actually unknown (\"uncertain\") forward model parameters from observed data, we consider a classical inversion problem setting. Bayesian methods have become a popular method for UQ in inverse problems (see, e.g., (45; 53)); this is also attributed to to their natural mechanism for regularization in typically ill-posed inverse problems (54).  
Reformulating (3) as an inverse model, the observed (measured) temperature swing of an LED system \(i\), \(i=1,\ldots,n\) is interpreted as  
\[y_{i} = \mathcal{M}(\mathbf{\Theta}_{i})+\varepsilon_{i},\] \[\Delta T_{0i} = \mathcal{M}(R_{\text{th}},\eta_{h,i},I_{f,i},V_{f,i})+ \varepsilon_{i} \tag{4}\] \[= R_{\text{th}_{i}}\cdot\eta_{h,i}\cdot I_{f,i}\cdot V_{f,i}+ \varepsilon_{i},\]  
where the residuals \(\varepsilon_{i}\) describe the discrepancy between forward model predictions and observed data. These residuals are commonly assumed to be additive, centred and independently distributed of \(\mathbf{\Theta}\). A rather simple choice in practice is to assume that  
\[\varepsilon_{i}\sim\mathcal{N}\Big{(}0,\sigma_{\varepsilon}^{2}\Big{)} \tag{5}\]  
with constant residual variance \(\sigma_{\varepsilon}^{2}\). Hence, the likelihood distribution is given by  
\[p(\mathbf{y}|\mathbf{\Theta})=\prod_{i=1}^{n}p(y_{i}|\mathbf{\Theta}_{i})=\prod_{i=1}^ {n}p_{\varepsilon}(y_{i}-\mathcal{M}(R_{\text{th}_{i}},\eta_{h,i},I_{f,i},V_{ f,i})|\sigma_{\varepsilon}^{2}) \tag{6}\]  
Figure 3: Workflow: (1) BHM framework (Section 3.2), (2) data-driven model for LED degradation (43), (3) impact of uncertainty in \(\Delta T_{0}\) on RUL prediction (Section 6.2).  
assuming conditional independence, i.e., the data are stochastically independent given \(\mathbf{\Theta}\). The likelihood distribution explains random uncertainty in the observed temperature swings given \(\mathbf{\Theta}\), which has been induced by measurement noise.
## 4 Simulation Results
### Second Step: Algorithm Evaluation  
The second step of the simulations aims to compare the efficiency of EBF and DBF and the quality of the constructed trees.  
Figure 7 displays the average number of messages required by EBF and DBF for the tree construction. We can observe that the larger the number of nodes, the more significant is the difference between the number of messages required by the two algorithms. In a scenario with 50 nodes, /linebreak EBF requires 10 messages per node on average, while DBF requires approximately 20 messages per node; therefore a reduction of 50% using the proposed strategy. In a scenario with 300 nodes, DBF requires 400 messages per node on average, while EBF requires 50; thus a reduction of 87.5%. This means that as the number of nodes increases, the proportion of paternity offers less advantageous than \(\alpha\) also increases. Comparing the scenarios with 50 and 300 nodes, the number of messages increases five-times for EBF and 20-times for DBF. Besides reducing communication overhead, EBF is also a more scalable solution.  
Figure 8 shows that the runtime of EBF is significantly lower than the runtime of DBF. In a scenario with 300 nodes, the percentage reduction in runtime and the number of messages is the same. Comparing the scenarios with 50 and 300 nodes, the runtime of EBF increases by about 37.5%, while the runtime of DBF increases by about 434%.  
Figure 8: Runtime.  
Figure 7: Average messages per node.  
Figure 9 shows that the reductions in the number of messages and runtime have little impact on the quality of the tree constructed by EBF. The average distance from nodes to the sink is nearly the same for both algorithms. In topologies formed by 300 nodes, the difference does not exceed 7%. Figure 10 confirms that EBF ensures the quality of the constructed tree. As well as the average distance to the sink, the average number of hops to the sink is approximately the same. In none of the evaluated scenarios does the difference between the paths constructed by EBF and DBF reach one hop.  
The DBF algorithm is not scalable, because there is a potential for an exponential explosion in the number of transmitted messages with the number of nodes [13]. Moreover, as the number of transmitted messages increases, there is also an increase in the proportion of such messages that do not carry route offers with an advantage larger than \(\alpha\) over the previously-accepted route. Consequently, the number of messages discarded by the EBF algorithm also increases. Therefore, the increase in the number of messages for the EBF algorithm when the number of nodes increases is almost linear. However, the increase in the number of discarded messages impacts the quality of the constructed tree, and that is the reason why the differences in the path length and in the hop count also increase with the number of nodes.  
Figure 10: Average hops to the sink.  
Figure 9: Average distance to the sink.
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### Land-Atmosphere Fluxes
#### 3.2.2. CH\({}_{4}\) Natural Methane Fluxes  
Recent trends in atmospheric CH\({}_{4}\) are not well understood and attribution using surface networks is underdetermined [157]. The relatively short CH\({}_{4}\) atmospheric lifetime of 9.8 years [158] and the combination of anthropogenic and natural sources make CH\({}_{4}\) a suitable species for spatial and temporal analysis using EO. Near-surface CH\({}_{4}\) sensitivity provided by the GOSAT satellite has been available since 2009 and provides retrievals using the light-path proxy approach from shortwave infrared radiances. To improve attribution and aid model development, retrievals of methane isotopologues, such as 13 CH\({}_{4}\), could be used in the future. These retrievals are currently limited to stratospheric retrievals using the Atmospheric Chemistry Experiment (ACE) satellite [159] and remain a challenge for tropospheric instruments.  
Wetlands are the most dominant source of CH\({}_{4}\), contributing about 30% to the total flux [160]. Large uncertainties have been identified in both the spatial and temporal distribution of wetland CH\({}_{4}\) fluxes provided by land surface models [161]. Major impacts can be seen when simulating long-term CH\({}_{4}\) emissions taking into account different vegetation classes [162]. Ref. [163] used GOSAT column CH\({}_{4}\) (XCH\({}_{4}\)) to evaluate wetland emissions from the Joint UK Land Environment Simulator (JULES, [164]). The detailed spatial and temporal resolution of GOSAT data permitted evaluation of the model performance over specific wetlands, which provided insight into some of the causes of model uncertainty. Bias in the modelled fluxes highlighted a failure to capture large scale, river fed, wetlands in the Pantanal region during high rainfall years. This is most likely caused by a lack of river routing in the model or by a misrepresentation of methanogenesis in high flood waters. In 2009/2010, the Parana River region experienced a flood event that caused a spike in CH\({}_{4}\) concentration retrieved from GOSAT; this was not reproduced in JULES, highlighting the need to include representation of over-bank inundation in the model.  
Figure 14 shows the model failure to transport surface moisture, via river runoff, from the rainfall region in Southern Brazil down the Parana River towards Paraguay and Argentina. The lack of river routing within the model causes a spatial bias in wetland area in JULES when compared to the GRACE satellite water storage anomaly, resulting in misrepresentation of modelled CH\({}_{4}\) fluxes.
## 2 Methods
### Profile Retrieval
#### 2.5.1 Molecular Profile  
We retrieve the molecular density profiles from the publicly available European Centre for Medium-range Weather Forecasts (ECMWF) ERA5 reanalysis [57], provided with a horizontal resolution of \(0.25^{\circ}\times 0.25^{\circ}\), on 37 pressure levels, a temporal resolution of one hour, with a latency of five days. Geopotential heights \(\Phi\) are converted to altitudes above ground \(Z\) using the prescription of List [58]:  
\[Z=\frac{R_{\phi}\Phi}{\left(g_{\phi}R_{\phi}/g_{0}\right)-\Phi}\;, \tag{42}\]  
Figure 15: Two examples of a rebinned signal \(\mathcal{S}\). On the left side, the range from \(\sim\)20 km to \(\sim\)35 km could be recovered for analysis, compared to the unbinned signal. On the right side, the details of a signal with a complicated structure of clouds are shown.  
where \(R_{\phi}\) and \(g_{\phi}\) are the Earth's radius and local gravity at latitude \(\phi\), respectively. An updated version of both can be obtained, for example, from the WGS84 reference ellipsoid [59].  
The density profiles \(n(h)\), normalized to the US standard density of air at Sea level \(n_{s}\)[60], are fitted using Equation (12) of Fruck et al. [9] and transformed into the equivalent molecular RCS expectation (see Equation (16) of Fruck et al. [9]):  
\[F(h)=\ \ln\left(\frac{n(h)}{n_{s}}\right)-\frac{16\pi}{3\cos\theta}\ \beta_{0}\int_{h_{\rm LIDAR}}^{h}\frac{n(h^{\prime})}{n_{s}}\ \mathrm{d}h^{\prime}-\ln\left(\frac{\langle P(h_{\rm LIDAR})\rangle}{ \langle T(h_{\rm LIDAR})\rangle}\cdot\frac{T_{s}}{P_{s}}\right)\ \ \, \tag{43}\]  
where \(\theta\) is the LIDAR's zenith pointing angle (\(90^{\circ}-\)_elevation_), \(\beta_{0}\) the Rayleigh backscatter coefficient at standard Rayleigh backscatter coefficient at \(n_{0}\)[9; 32], \(h_{\rm LIDAR}\) the altitude of the LIDAR, \(T_{s}\) and \(P_{s}\) the US standard atmosphere Sea level temperature and pressure [60], and \(\langle P(h_{\rm LIDAR})\rangle\) and \(\langle T(h_{\rm LIDAR})\rangle\) their measured averages at the LIDAR site.  
The so-called molecular fits are then used to fit a given part of the LIDAR RCS to \(F(h)\).
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results
### Comparison of Algorithms Under the LineMOD Dataset
In contrast, the point cloud extraction module proposed in this work enhances the focus on local point cloud information during pixel-level point cloud extraction, leading to richer features and better performance on weak-textured objects. In summary, the proposed method achieves a favorable balance between pose estimation accuracy and computational efficiency, outperforming most baseline methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RGB Methods**} & \multicolumn{3}{c}{**RGB-D Methods**} \\ \hline \multicolumn{3}{c}{**PoseCNN [11]**} & \multirow{2}{*}{**HRPose [49]**} & \multirow{2}{*}{**RNNPose [27]**} & \multirow{2}{*}{**FS6D [26]**} & \multirow{2}{*}{**DenseFusion [13]**} & \multirow{2}{*}{**MaskedFusion [35]**} & \multirow{2}{*}{**DFTr [42]**} & \multirow{2}{*}{**Ours**} \\  & & +ICP & & & & & & \\ \hline ape & 77 & 61.2 & 85.62 & 78 & 92.3 & 92.2 & **98.2** & 96.8 \\ bench vi. & 97.5 & 95.5 & **100** & 88.5 & 93.2 & 98.4 & 99.4 & 97 \\ camera & 93.5 & 84.9 & 98.43 & 91 & 94.4 & 98.0 & **99.1** & 98.9 \\ can & 96.5 & 93.6 & **99.51** & 89.5 & 93.1 & 97.4 & 97.4 & 98.8 \\ cat & 82.1 & 86.0 & 96.41 & 97.5 & 96.5 & 97.8 & **99.8** & 99.3 \\ driller & 95 & 96.2 & **99.5** & 92 & 87.0 & 95.6 & 99.0 & 99.1 \\ duck & 77.7 & 68.0 & 89.67 & 75.5 & 92.3 & 94.0 & **98.7** & 97.7 \\ eggbox \(\star\) & 97.1 & 99.0 & **100** & 99.5 & 99.8 & 99.6 & **100** & **100** \\ glue \(\star\) & 99.4 & 97.0 & 97.3 & 99.5 & **100** & **100** & **100** & 99.7 \\ hole & 52.8 & 78.1 & 97.15 & 96 & 92.1 & 97.3 & **99.3** & 98.7 \\ iron & 98.3 & 95.5 & **100** & 87.5 & 97.0 & 97.1 & 98.9 & 98 \\ lamp & 97.5 & 96.6 & **100** & 97.0 & 95.3 & 99.0 & 99.6 & 98.8 \\ phone & 87.7 & 86.7 & 98.68 & 97.5 & 92.8 & 98.8 & **99.8** & 97.2 \\
**MEAN** & 88.6 & 87.6 & 97.1 & 91.5 & 94.3 & 97.3 & **99.2** & 98.5 \\ \hline \hline \end{tabular}  
* **Notes:** Bold numbers represent the highest scores among the methods, and objects marked with an asterisk (\(\star\)) are symmetric.  
\end{table} 
Table 3: Evaluation results of different algorithms using the LineMOD dataset.
# Optimisation of Energy Transfer in Reluctance Coil Guns: Application to Soccer Ball Launchers
## 5 Results and Discussion
### Experimental Results  
In order to validate the simulation results, experiments have been done using a two-coil reluctance coil gun corresponding to the optimal configuration (as explained in Section 5.1). This launcher is shown in Figure 29. It is part of our new RoboCup robot presented in Figure 30.  
A custom four-channel coil gun driver has been designed but is not in the scope of this paper. It includes four MOSFET for capacitor commutation with a 160 A current peak under 450 V on each coil. A safety system for dissipating energy stored in the capacitors when the system is switched off or stopped has been implemented in this driver, justifying the aluminium ventilated power heat sink that can be seen on the right board of Figure 31. For triggering the coils in a very accurate time sequence  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Triggering Instant (ms)** & 13.5 & 14.5 & 15.5 & 16.5 & 17.5 \\ \hline
**Optimized Ball Speed (m-s\({}^{-1}\))** & 13.3 & 14.6 & 16.4 & 14.3 & 13.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Impact of a variation of the fourth coil triggering instant in the case of a four-coil EML.  
Figure 30: RoboCup robot for testing the optimized coil gun.  
Figure 29: 2 coils optimized reluctance coil gun.  
(and for controlling DC motors and low level sensors), a micro-controller board has been designed and can be seen on the left side of Figure 31. In our test corresponding to the chosen optimal case, the second coil has been triggered exactly 10 ms after the first one.  
Ball speed measurements have been done using a high speed camera on 20 successive tests. Average measured ball velocity is equal to \(V_{Ball}=15.5\) m\(\cdot\)s\({}^{-1}\). This is consistent with the theoretical value (16 m\(\cdot\)s\({}^{-1}\)). Error is only 3.1% and dispersion is low (\(\sigma=0.2\) m\(\cdot\)s\({}^{-1}\)). These results show that the simulation model used in this paper is accurate, despite many strongly non-linear effects, and that the structure of a reluctance coil gun can be optimized very efficiently without changing the amount of copper used and the size of the actuator.
## 3 Results  
### Development of the Assay  
We aimed to develop a multiplex PCR-based NGS panel to identify mutations in CTCs relevant for targeted therapies in metastatic breast cancer, considering that the genomic DNA of single CTCs was amplified using an Msel restriction-based method. We included hotspot regions in the _PIK3 CA_, _ESR1_, _AKT1_, and _ERBB2_ genes frequently mutated in breast cancers. In the _PIK3 CA_ gene, hotspot regions within the exons 5, 10, and 21 were covered. In the _ESR1_ gene, the hotspot regions in exons 5, 7, and 8 were analyzed. In the _AKT1_ gene, we investigated the hotspot regions in exon 3 and in the _ERBB2_ gene our assay covers the hotspot regions in exons 8, 18, 19, and 20. For each amplicon, a panel of primers was tested and the most specific primer combination was chosen for the multiplex PCR (Table 2, Figure S1). Primer concentrations in the first PCR were titrated resulting in an equal ratio for each fragment of \(7.0\%\pm 1.4\%\) to \(10.2\%\pm 0.8\%\) after sequencing (Figure S2). In total, we achieved an on target rate of \(95.3\%\pm 0.9\%\) of all reads.  
Next, a group of CTCs of different quality determined by Ampli1 quality control was sequenced and the obtained coverage was compared. For cells with highest amplified DNA quality showing 4 bands in the quality control PCR a mean coverage of \(10.2\pm 1.3\) fragments per cell was observed (Figure 1). The coverage correlated with the quality determined with the quality control PCR. Cells displaying 3 or 4 bands in the quality control PCR had a significantly increased coverage compared to those with less than three bands (\(p<0.0001\) determined by two-tailed \(t\)-test). Thus, we focused on cells displaying at least 3 bands in the quality control PCR for further analysis to yield reliable results.
## 3 Results
### Identifying the Optimal Treatment Time  
In order to identify the optimal treatment time of indirect HOS exposure to CAP, in this study we performed a cell viability analysis, using three PAM-RPMI doses (with T\({}_{\text{exp}}\) of 30, 60 and 90 s). The treatment times analyzed were 5, 10, 20 and 30 min, for all experimental variants also control groups were used. Cell viability was assessed at 2 h post-treatment.  
PAM-RPMI treatment exhibited cytotoxic effects for all experimental variants, these ranged from mild to moderate and strong depending on the combination of exposure time and treatment time (Figure 8).  
After treating the cells with PAM-RPMI 30 s, the viability of HOS cells experienced a slight decrease, as follows: 92.22% for 5 min T\({}_{\text{tr}}\), 88.19% for 10 min T\({}_{\text{tr}}\), 87.09% for 20 min T\({}_{\text{tr}}\) and 78.35% for 30 min T\({}_{\text{tr}}\). For the PAM-RPMI 60 s treatment, the trend of decreasing viability was the same, reaching values of 89.83% for 5 min T\({}_{\text{tr}}\), 84.22% for 10 min T\({}_{\text{tr}}\), 87.29% for 20 min T\({}_{\text{tr}}\) and 75.78% for 30 min T\({}_{\text{tr}}\). The last experimental variant evaluated in this study, PAM-RPMI 90 s treatment, was most influenced by the treatment time, the HOS cell viability values being 87.55% for 5 min T\({}_{\text{tr}}\), 77.85% for 10 min T\({}_{\text{tr}}\), 49.32% for 20 min T\({}_{\text{tr}}\) and 27.70% for 30 min T\({}_{\text{tr}}\).  
Figure 7: Viability of human osteosarcoma cells (HOS) treated with PAM-PBS and PAM-RPMI. Values are means \(\pm\) SEM (\(n\) = 3). Statistical significance was determined by two-way ANOVA followed by Sidak’s post hoc multiple comparison test (ns—nonsignificant, ****\(p\) < 0.0001).  
When evaluating the impact of the different treatment times on HOS cell viability it can be observed that an increase in the duration in which the cells are in contact with PAM-RPMI has as a consequence a stronger impact on cell viability, resulting in a direct correlation between the treatment time and its cytotoxicity. According to the Tukey multiple comparison tests, it can be observed that for the PAM-RPMI 30 s and PAM-RPMI 60 s the only statistically significant difference regarding the decrease in cell viability is between the samples treated with PAM-RPMI for 30 min and those treated for 5 min, while, in the case of PAM-RPMI 90 s, there is a statistically significant decrease in cell viability for all experimental variants, with the exception of the T\({}_{\text{tr}}\)'s of 5 and 10 min, between which the differences are statistically not significant. According to the results previously presented, the treatment time of 30 min had the strongest cytotoxic effect, regardless of the exposure time used.
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 3 Materials and Method
### Baseline  
To comprehensively evaluate the performance of the proposed method, comparisons were conducted with several classical and state-of-the-art baseline models, including logistic regression [53], XGBoost [54], TabNet [55], TabTransformer [56], VIME [57], and BERT [58]. Logistic regression is a well-established linear model suitable for linearly separable data. It is widely used due to its simplicity and computational efficiency. XGBoost, a gradient-boosted decision tree algorithm, is capable of capturing nonlinear feature interactions and exhibits strong resistance to overfitting, making it a popular choice for structured data modeling. TabNet is a deep learning-based model that combines attention mechanisms with decision tree-inspired feature selection. It enables automatic identification of informative features and is particularly effective in modeling sparse structured data. TabTransformer leverages the transformer architecture to model tabular data and is capable of capturing long-range dependencies among features, making it well suited for datasets with complex feature interactions. VIME is an SSL framework specifically designed for structured data. It effectively learns latent representations by maximizing the similarity of representations under various perturbations, and performs well in scenarios with limited labels and high missing rates. Although originally designed for natural language processing, BERT has recently been adapted for structured data tasks due to its powerful contextual representation learning capabilities, which are advantageous in feature representation and downstream prediction tasks.
## Appendix A Nonlinearity Control Theory and Parameter Analysis in Third-Order Systems
### Multi-Input Feedback Linearization Theorem  
The nonlinear system \(\overset{\bullet}{\mathbf{x}}=f(\mathbf{x})+\sum\limits_{i=1}^{m}g_{i}( \mathbf{x})u_{i}=f(\mathbf{x})+G(\mathbf{x})\mathbf{u},\mathbf{x}\in\mathbf{ R}^{n}\) is locally feedback linearizable, i.e., locally transformable in V\({}_{0}\), a neighborhood of the origin contained in U\({}_{0}\), into a linear controllable system in the Brunovsky controller form by means of:  
1. a nonsingular state feedback: \[\mathbf{u}=K(\mathbf{x})+\beta(\mathbf{x})v,K(0)=0,\] where \(K(\mathbf{x})\) is a smooth function from V\({}_{0}\) into \(\mathbf{R}^{n}\), \(\beta(\mathbf{x})\) is an m \(\times\) m matrix with smooth entries, nonsingular in V\({}_{0}\);
2. a local diffeomorphism in V\({}_{0}\): \(z=T(\mathbf{x}),T(0)=0\), if, and only if, in U\({}_{0}\): 1. \(\Theta_{l}=\mathrm{Span}\{ad_{f}^{i}g_{i}:1\leq i\leq m,0\leq j\leq l\},0\leq l \leq n-2\), is involutive and of constant rank. 2. \(\mathrm{Rank}\Theta_{n-1}=n\), \(\mathrm{Rank}\ \mathrm{G}_{n-1}=n\).