esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### Surface Velocities  
The TSX image pairs from 2009 and the TDX data during 2011-2016 were processed to obtain velocity fields using the SAR intensity offset tracking algorithm implemented in the GAMMA Remote Sensing software [23]. We precisely registered the master and slave data of the image pairs using a cross-correlation optimization technique [23; 24; 25] before implementing the tracking algorithm. The latter algorithm uses a normalized cross-correlation function based on a moving window and a predefined step size. Prominent structures or radar speckle in the moving window are identified in the amplitude image pairs to determine the displacement. A tracking window of \(128\times 128\) pixels (\(\sim\)116 m \(\times\) -237 m) in the range and azimuth directions and a step size of 30 \(\times\) 30 pixels (-27 m \(\times\) -55 m) proved to be suitable for most of the image pairs. However, to track fast flowing features especially at the glacier terminus, we used a tracking window of 320 \(\times\) 192 pixels (\(\sim\)290 m \(\times\) -355 m). A signal-to-noise ratio (SNR) threshold of 4.0 was taken as criterion to discard unreliable offset measurements. The velocity maps were subsequently geocoded using the TanDEM-X DEM acquired on 03/02/2012. A slant to ground range conversion was done by considering the local incidence angle computed from the DEM and orbital parameters of the respective master scene. In the last step, the displacement fields were scaled to daily velocities (m day\({}^{-1}\)).  
Errors in the surface velocity measurements can be caused by inaccurate coregistration and errors in the tracking algorithm itself. The net error is the aggregated sum of these error contributions. We determined the coregistration error using 350 velocity measurements on stable ground (rocks and moraines). We estimated the root-mean-square error (RMSE) from these points resulting in an RMSE of \(<\)0.20 m day\({}^{-1}\) in more than 90% of the image pairs. The error corresponding to the tracking algorithm (\(e_{t}\)) can be formulated as [13]  
\[e_{t}=\frac{C\triangle x}{z\triangle t}, \tag{1}\]  
where (C) is the uncertainty of tracking algorithm (pixels), (\(\triangle x\)) is the pixel resolution in ground range geometry, (\(z\)) is the oversampling factor accounting for aliasing [24] and (\(\triangle t\)) is the time duration between master and slave image acquisitions. We assumed an uncertainty value (\(C\)) of 0.4 pixels and used an oversampling factor (\(z\)) of 2. The ground range resolution was computed for each image pair from the depression angle and range resolution of each data set (Table 1). The aggregate error amounts to \(<\)0.25 m day\({}^{-1}\) for more than 90% of data pairs.
## 4 Performance Evaluation in a Capsule Prototype
### Results and Discussion  
The four boards are manufactured in circular PCBs each having a diameter of 16 mm as shown in Figure 9. The digital blocks in the FPGA are modeled and synthesized in the FPGA of the capsule prototype. The synthesis results of the proposed lossless compressor are summarized in Table 8.  
The measured current consumption of the capsule prototype is 23 mA and the capsule can run for 8.5 h with three 195 mAh button batteries.
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
###### Abstract  
Pneumothorax is a critical condition that requires rapid and accurate diagnosis from standard chest radiographs. Identifying and segmenting the location of the pneumothorax are essential for developing an effective treatment plan. nnUNet is a self-configuring, deep learning-based framework for medical image segmentation. Despite adjusting its parameters automatically through data-driven optimization strategies and offering robust feature extraction and segmentation capabilities across diverse datasets, our initial experiments revealed that nnUNet alone struggled to achieve consistently accurate segmentation for pneumothorax, particularly in challenging scenarios where subtle intensity variations and anatomical noise obscure the target regions. This study aims to enhance the accuracy and robustness of pneumothorax segmentation in low-contrast chest radiographs by integrating spatial prior information and attention mechanism into the nnUNet framework. In this study, we introduce the spatial prior contrast adapter (SPCA)-enhanced nnUNet by implementing two modules. First, we integrate an SPCA utilizing the MedSAM foundation model to incorporate spatial prior information of the lung region, effectively guiding the segmentation network to focus on anatomically relevant areas. In the meantime, a probabilistic atlas, which shows the probability of an area prone to pneumothorax, is generated based on the ground truth masks. Both the lung segmentation results and the probabilistic atlas are used as attention maps in nnUNet. Second, we combine the two attention maps as additional input into nnUNet and integrate an attention mechanism into standard nnUNet by using a convolutional block attention module (CBAM). We validate our method by experimenting on the dataset CANDID-PTX, a benchmark dataset representing 19,237 chest radiographs. By introducing spatial awareness and intensity adjustments, the model reduces false positives and improves the precision of boundary delineations, ultimately overcoming many of the limitations associated with low-contrast radiographs. Compared with standard nnUNet, SPCA-enhanced nnUNet achieves an average Dice coefficient of 0.81, which indicates an improvement of standard nnUNet by 15%. This study provides a novel approach toward enhancing the segmentation performance of pneumothorax with low contrast in chest X-ray radiographs.  
X-ray; pneumothorax segmentation; nnUNet; MedSAM +
Footnote †: journal: Applied Sciences
# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
## 3 Example
### Data Collection and Dataset Generation
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{**
\begin{tabular}{} \end{tabular}**} \\ \hline
1 & Normal & The time series is symmetrical, the vibration amplitude is relatively steady, and the frequency response is concentrated in the mid-frequency band. \\ \hline
2 & Seismic & The time series shows sparse long period features. Additionally, the frequency response is concentrated in the low frequency band. \\ \hline
3 & Missing & Most/all of the time series is missing or meaningless values, and the frequency response is correspondingly zero or meaningless disorder. \\ \hline
4 & Minor & The time series appears sawtooth-shaped, and the vibration amplitude is very small in the time domain. \\ \hline
5 & Bias & Relative to the pattern Normal, the time history is Bias towards one side. \\ \hline
6 & Drift & The time series is nonstationary, with random drift, and the frequency response is concentrated in the low frequency band. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of each data pattern. It is worth noting that the data labeling in this study does not consider the quantitative values of the vibration acceleration, but only the visual features of the T-F images, so labeling the seismic data mainly relies on the following two aspects: firstly, the seismic information released by the China Earthquake Networks Center is utilized, but due to the seismic propagation, the seismic response of bridges lags behind the occurrence of earthquakes by several seconds; secondly, the seismic response of long-span bridges exhibits long-period characteristic [42]. Next, seismic events No. 1 and No. 2 are used for neural network training, and seismic events No. 3 and No. 4 are for testing. The number of samples per class in the balanced training set is set to 1000, and 200 samples from each class are selected as the validation set. Figure 7 shows the distribution of anomalies for seismic events No. 1 and No. 2. The starting time of seismic No.1 was around 11:17:30, while in seismic No. 2, it was around 21:57:15, followed by an aftershock at around 22:12. Note that the vibration acceleration data did not concurrently exhibit the seismic response characteristics, even some of the channels' data were never labeled as seismic responses from start to end. Some channels with persistent sensor malfunctions failed to record the earthquake, resulting in mostly Missing or Minor data spreading throughout the entire time span. Meanwhile, the pattern Bias was scattered in both temporal and spatial dimensions, which may interfere with the seismic detection.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 4 Simulation Framework
### Simulation Setup and Configuration  
The standard dumbbell topology with two routers and six nodes (N1-N6) was utilized for all scenarios (see Figure 4). Simulation parameters common to all scenarios (S1-S4) are compiled in Table 1, with an asterisk indicating that detailed descriptions of each simulation group are provided preceding individual experiment descriptions.  
TCP sources were situated in nodes N1-N3, while nodes N4-N6 functioned as data recipients. All TCP flows utilized 1000-byte packets. The total number of TCP flows, link delays, and buffer sizes varied for each scenario group. The routers employed the drop-tail policy. Simulations were conducted for 100 s, except for the first scenario.  
To conduct a comprehensive comparison of the results, various metrics were collected. These included the overall throughput of the bottleneck link, the mean queue length, the standard deviation of the queue length, and the drop ratio at router A. The drop ratio refers to the proportion of dropped packets in relation to the total number of packets transmitted, and it is represented as a percentage. Additionally, the mean throughput for each individual flow was computed. To assess the fairness of link sharing among competing flows, [PERSON]'s index was calculated:  
\[JI=\frac{(\sum_{i=1}^{n}x_{i})^{2}}{n*\sum_{i=1}^{n}\overline{x}_{i}^{2}}, \tag{4}\]  
where \(x_{i}\) is the \(i\)th flow's throughput.
## 4 Results and Discussions
### The Effect of the Radiation to Conduction Number  
Figure 10 shows the isothermals (on the left) and streamlines (on the right) for \(Ra=10^{5}\), \(Kr=10\), \(\varepsilon=0.6\), \(G=1.2\), \(\phi=0.5\) and \(Pr=0.71\), with \(Nr=10^{-1}\), \(10^{0}\), \(10^{1}\), \(10^{2}\) and \(10^{3}\). For \(Nr=10^{-1}\), the isothermals in the cavity are almost horizontally distributed, but have an intense vertical distribution near the hot interface. With the increase in \(Nr\), the horizontally distributed isothermals become vertically distributed, which indicates that the transition from natural convection to conduction and surface radiation takes place. For \(Nr=10^{3}\), the isothermals are all concentrated on the solid walls. In addition, the maximum velocity decreases as \(Nr\) increases. This indicates that the air flow becomes weaker with increasing \(Nr\), and natural convection is restrained by surface radiation. It can be seen from the streamlines that for \(Nr=1\), two vortexes appear in the middle-upper and middle-lower part of the cavity, and the streamlines in the cavity are distributed non-uniformly, which means the air flow is extremely strong. For \(Nr=10^{2}\), the two vortexes vanished, which indicates the air flow is weakened, and the heat transfer is dominated by surface radiation.  
Figure 11 shows the evolution of the average convective, radiative and total Nusselt numbers of the hot interface as a function of \(Nr\). It can be seen that \(\overline{N}u_{\rm rad}\) increases with increasing \(Nr\), but \(\overline{N}u_{\rm conv}\) decreases with increasing \(Nr\). This indicates that surface radiation among the solid walls is enhanced by an increase in \(Nr\), which leads to a more uniform temperature distribution in the cavity. Therefore, the natural convection of the air is weakened due to the decrease of the temperature difference in the cavity. In addition, at low \(Nr\) (from \(10^{-1}\) to \(10^{0}\)) and at high \(Nr\) (from \(Nr=10^{2}\) to \(10^{3}\)), \(\overline{N}u_{\rm rad}\) and \(\overline{N}u_{\rm conv}\) had little change compared to the \(Nr\) from \(10^{0}\) to \(10^{2}\). This means that the effect of \(Nr\) on the total heat transfer reaches a peak for \(Nr\) from \(10^{0}\) to \(10^{2}\), and, in the other range, the effect has a limit. In addition, the increase in \(\overline{N}u_{\rm t}\) with increasing \(Nr\) indicates that the total heat transfer in the cavity is strengthened and surface radiation plays a leading role at high \(Nr\).  
The emissivity of the porous char layer and the radiation to conduction number have a significant effect on total heat transfer when taking surface radiation into consideration. In order to keep excellent thermal protection performance at high temperature, the designer should take effective measures to make the emissivity lower.  
Figure 9: Variations of the average Nusselt number as a function of \(\varepsilon\).  
Figure 10: _Cont._
# How to Make Sense of Team Sport Data:
## 4 Research Aspects
### Definitions
#### 4.1.3 Group Behavior and Movement  
Analysis of movement patterns or, more general, of behavior of individuals can nowadays be combined with simultaneous recordings of diverse parameters. 3D accelerometer data (behavior and energy use), remote sensing data (e.g., weather, habitat) and/or data about interactions with other tagged individuals can be assessed additionally to traditional tracking methods/data (reviewed by [PERSON] et al. [61]). The increasing amount of data about crucial impacts on individual behavior can then be used to explain or predict behavioral responses of an individual on the different levels of behavior (see the works of [PERSON] [62], [PERSON] [63] and [PERSON] [64]. The investigation of behavior becomes even more complex when (social) interactions of individuals are added like in, for example, team sports or collective behavior in animals.  
The aggregation of individuals often leads to \"collective behavior [that] is defined as the behavior of aggregates whose interaction is 'affected by some sense that they constitute a group' but who do not have procedures for selecting or identifying leaders or members\" [65]. Flocks of birds and shoals of fish are the textbook example for collective behavior in animals. The aggregate of individuals moves as a whole structure based on the individual behavioral patterns that are again influenced by the fact that the individual behavior is affected by the aggregate. This interdependency of individual and group behavior causes that no (obvious) leader can be identified by the observer. Different mathematical models can be applied on collective behavior in animals (e.g., by [PERSON] [64]) and synergies that arise in team sports [66] might also apply to synergies arising in superorganisms like social insects. Although the 'final cause' (consequences of behavior, definitions by [PERSON] [63]) are very different between team sports (e.g., winning a match) and animals (e.g., passing genes to the next generation), certain structures of behavior might be shared across team sports and superorganisms [67]. The integration of different research fields might allow to apply principles revealed in animal superorganisms to team sports performance analysis and vice versa [67]. The analysis of movement patterns of individuals therefore will benefit from a close collaboration by researchers of different fields in which new movement analysis and visualization methodologies can arise [68] and help to understand collective behavior in the wild or/and under restricted conditions like team sports or lab experiments.
# Ventilation Systems and Their Impact on Nanoparticle Concentrations in Office Buildings
## 4 Discussion
### Strengths and Limitations of This Study  
A main strength of this study is that it provides continuous measurements of nanoparticles indoors in twelve office buildings representing both the heating and nonheating seasons. The buildings were chosen to represent different ventilation systems and various distances from busy streets. The measurement periods lasted for one week, which means that they include both weekdays and a weekend during both daytime and nighttime, presented separately in the study. Another strength of this study is that the ventilation flows in the buildings represent flows under both variable and constant conditions and show how the NP concentrations are affected by those.  
One of the limitations of the current study is that all buildings except one use filters of class F7. Consequently, there is very limited information on studying the filters' effect on NP filtration in real life situations. Another limitation of this study is that the chemical composition of the NPs was not studied because it was not possible to measure with the Scanning Mobility Particle Sizer Spectrometer (SMPS). This means that it is not possible to distinguish the sources of the NPs, which would have been a great advantage when it comes to understand the importance of ventilation. Therefore, this is an important issue that needs to be addressed in future studies and measurements.
## 4 Discussion  
The principal component analysis (PCA) conducted above revealed six major factor components affecting PV power and explaining up to 90% of the total variable variance. The most significant variables identified using the PCA technique were subsequently used in the proposed models.  
Moreover, based on the findings of the performance metrics acquired in Tables 7 and 8, it can be seen that non-linear models, particularly Bayesian regularized neural networks and random forest, obtained the best compromise between the predicted and observed values, with R\({}^{2}\) = 99.99% and R\({}^{2}\) = 99.53%, respectively, in the training phase and R\({}^{2}\) = 99.99% and R\({}^{2}\) = 97.33%, respectively, in the testing phase, while the lowest performance was achieved by linear models such as the elastic net algorithm with R\({}^{2}\) = 89.3% and RMSE = 0.69 kW. This is mainly because non-linear methods are better at including data dynamics and capturing non-linear correlations between variables.  
Finally, several plots have been presented above to enable a more accurate study of the models in terms of residuals. For instance, using residual versus observed values plots in Figure 5 showed that Bayesian regularized neural networks offer better prediction accuracy when compared with the other predictive models investigated in this study, since the residuals are symmetrically distributed around the x-axis (near to zero). In the same way, when examining the residual boxplots (see Figure 6), we can observe that Bayesian regularized neural networks have the fewest residuals, followed by random forest and support vector regression, unlike elastic net, which has considerably more widely distributed residuals.  
Another important tool for comparing and analyzing the accuracy of regression models for different tolerance levels is the REC curve graphic (see Figure 7). The ideal model is located in the upper left corner, similar to the ROC curve. The better the model, the faster the curve approaches this point, which is the case for the Bayesian regularized neural networks model, followed by random forest.  
This study presented deep insight into comparing the performance of four statistical and machine learning techniques for hourly PV power forecasts, which will be useful to researchers and engineers working in the field of solar photovoltaic energy such as PV  
Figure 7: REC curves.  
integrated smart buildings, efficient energy management system, electric vehicle charging, and smart grids.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 3 Gaussian Process Regression for Time Series
### Kernel Functions for Time Series
#### 3.2.1 The Radial Basis Function Kernel  
The Radial Basis Function (RBF) kernel (a.k.a. the Gaussian kernel or the Squared Exponential kernel) is defined as:  
\[k_{\text{RBF}(s,\ell)}(t,t^{\prime})=s^{2}\exp\left(-\frac{(t-t^{\prime})^{2} }{2\ell^{2}}\right) \tag{10}\]  
In (10), \(k\) has two hyperparameters: the _scale factor_\(s\), which quantifies the deviation to the mean value \(m\) of a function \(f\) drawn from the corresponding Gaussian Process; and the _length scale_\(\ell\), which quantifies the strength of the correlation between the data points \(f(t)\) and \(f(t^{\prime})\) of two points in time at distance \(|t-t^{\prime}|\). Gaussian Process Regression with the RBF kernel corresponds to Bayesian linear regression with an infinite number of basis functions. Due to its nice properties (see, e.g., ([14], Sections 4.2 and 4.3)), RBF has become the default kernel function in practical applications.
## 4 Experimental Results and Discussion
### Performance Analysis on Multi-Date Satellite Images
The quality evaluation results of DSMs generated from multi-date satellite images are shown in Table 4. It can be seen that the proposed method has a high CP (over 60%) of the DSMs from the four sites, which is consistent with the visual quality results presented in Figure 9. For Site 4, the CP is lower for all three methods, due to the presence of too many vegetation areas in Site 4 and the large variation of vegetation areas in satellite images from different acquisition times. On the whole, the proposed method achieves more than 60% completeness for the DSM generated from multi-date satellite images and performs well. At the same time, the CP value of DSM generated by the multi-date S2P method can be as low as 50.49%, while that of the traditional BA method is as low as 52.71%. In terms of accuracy, the ME values of the proposed methods are all below 0.7 m. Nevertheless, the ME values of the multi-date S2P method can be as high as 0.99 m, and the traditional BA method is as high as 0.94 m. Based on Figures 9 and 10 and Table 4, the proposed method is more accurate in estimating the altitude of the building area and is significantly more robust than the multi-date S2P method and the traditional BA method. In addition, we compare the optimization performance of the proposed method with the traditional BA method, and the number of iterations and optimization time of the two methods are listed in Table 5. It can be seen that regardless of the number of iterations, the optimization time of the proposed method is less than that of the traditional BA method, which indicates that the proposed method can find the optimal solution faster and has  
Figure 10: The ground truth DSM and the generated DSMs of the multi-date S2P method [20], the traditional BA method [32], and the proposed method. better optimization performance. Therefore, the proposed method provides a possibility to solve the slow convergence problem of the traditional BA method.
## 4 Results  
This section aims to validate the effectiveness of the proposed BOTA-LSTM model by comparing it with a manually tuned temporal attention-LSTM (TA-LSTM) model and a Bayesian optimization-based LSTM (BO-LSTM) model. The prediction results of the BOTA-LSTM model for single and multiple rings are presented first. The effect of the temporal attention mechanism is studied by comparing the performance of the BOTA-LSTM model and the BO-LSTM model. After that, the impact of Bayesian hyperparameter optimization on the performance of the temporal attention-LSTM model is explored. Moreover, a quantitative comparison between two commonly used recurrent neural networks for shield tunneling, LSTM and GRU, is also presented to explain why LSTM is selected as the basic model for developing the multi-ring prediction model in this study.