esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Supervised Learning across the Spectrum
## 6 Experiments and Results
### Robustness to Cloud Cover  
We analyze S4's ability to tackle the challenge of cloud cover during inference on optical images. We start by dividing the optical SITS in the PASTIS test set into different groups based on the amount of cloudy pixels they contain. We obtain a cloud mask for every image in the PASTIS test set using the S2 Cloudless algorithm [69]. We compute the cloud cover ratio as the number of clouded pixels to total pixels in the SITS. After grouping every SITS by cloud cover ratio, we compute the mIoU. Figure 9 reports the mIoU gain of S4 over the CaCo baseline for 100% labels (the scenario when our model has the highest relative improvement). The mIoU improvement is the mean improvement over CaCo baseline. The results illustrate that our approach provides greater gains in the presence of clouds. The radar data can help guide the model to make better predictions on partially clouded data, since the radar images provide insights into how the model can \"see through the clouds\". The improvement drops for SITS where the cloud cover ratio is greater than 25%, where many images are mostly or even fully occluded by clouds (and contain little useful information).  
Figure 8: Radar to optical reconstruction of S4 (optical ground truth, radar input, optical reconstruction). Red boxes indicate similar features between reconstruction and output modality images.  
Figure 7: Optical to radar reconstruction of S4 (optical input, radar ground truth, radar reconstruction). Red boxes indicate similar features between reconstruction and output modality images.
# Enhancing 3D Lung Infection Segmentation with 2D U-Shaped Deep Learning Variants
## 3 Material and Methods
### Segmentation with 2D Architectures
#### 3.3.1 UNet  
UNet [9] is a widely recognized and extensively used deep learning architecture based on Convolutional Neural Networks (CNNs) for medical image segmentation. Specifically, in the context of lung segmentation, the UNet architecture, which combines CNN encoder and decoder techniques, has been employed to achieve rapid and precise segmentation of lung structures [14; 51; 52].  
The key strength of the UNet architecture lies in its ability to extract features at multiple levels of resolution and reconstruct the output segmentation map with the required dimensions, thanks to its symmetrical encoder-decoder structure. The architecture of UNet comprises two main routes: the contraction path (also known as the encoder or analytic path) and the expansion path (also known as the decoder or synthesis path) [53].  
The contraction path resembles a standard convolutional network and extracts classification information from the input image. On the other hand, the expansion path consists of up-convolutions and concatenations with features from the contraction path. This enables the propagation of features from the early layers, preserving fine input details, to the deeper layers that aggregate high-level information. This is particularly important as deep layers tend to lose small image details due to the presence of intermediate pooling layers [54; 55]. The skip connections between the encoder and decoder routes in the UNet architecture facilitate the integration of low-level and high-level features, promoting the accurate delin  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Before CLAHE Applied** & **After CLAHE Applied** \\ \hline \hline \end{tabular}
\end{table}
Table 4: CT-scan comparison before and after CLAHE application [14].  
eation of lung structures [9]. Please refer to Figure 1, which is adapted from [9], for a visual representation of the UNet architecture.  
In our implementation, the UNet model comprises a total of 8.5 million parameters. It consists of a total of 63 layers, including layers with parameters and layers without parameters. Specifically, there are 35 layers with parameters only. For a more detailed scheme of UNet, please refer to [9].
## 3 Application examples of CAD for brain diseases  
### Intracranial aneurysm  
One of the purposes of the medical checks for brain is to detect asymptomatic unruptured aneurysms using the MR angiography (MRA) [1]. Ruptures of intracranial aneurysms cause subarachnoid hemorrhage (SAH), which kills more than 10,000 persons each year in North America [64]. People who have a family history of aneurysms or who have polycystic kidneys, coarctation of the aorta, or collagen vascular disease, could be put at an increased risk for aneurysms. In those people, it is very important to detect unruptured aneurysms as early as possible and to treat or follow up the aneurysms. However, it is difficult and time consuming for radiologists to detect small aneurysms, because of their overlap with adjacent vessels or unusual locations in maximum intensity projection (MIP) images of MRA. Therefore, a number of CAD systems have been developed for assisting radiologists in detection of intracranial aneurysms [25,26,41,65,66]. Table 1 shows a comparison of methods and results of CAD systems for detection of intracranial aneurysms using MRA. [PERSON] _et al._[66] proposed a CAD system for detecting intracranial aneurysms in MRA images based on estimation of the fuzzy degrees for each aneurysm candidate, which denoted whether a candidate is an aneurysm. [PERSON] _et al._[41] developed a curvature-based display system that shows volume-rendered images with overlaid curvature indices determined from MRA images. However, neuroradiologists should identify aneurysm candidates by using color-coded curvatures corresponding to the degree of aneurysm, because the system did not automatically detect intracranial aneurysms. [PERSON] _et al._[65] developed an automated detection system for aneurysms based on a gradient concentrate filter, which was applied to segmented vessels for enhancement of aneurysm. [PERSON] _et al._ developed a CAD system for detection of intracranial aneurysms using MRA images [25,26]. The selective enhancement filters for dots [40], which can enhance dot-like objects, were applied to isotropic 3-D MRA images. Useful image features related to small protrusions were extracted from the shape-based difference (SBD) regions obtained by the SBD image technique. A protrusion of a SBD region could be a small aneurysm, but a thin or very small region of a SBD region could be a false positive. Figure 11 shows the illustrations of images produced in each step of the SBD image technique. We tested the computerized scheme on 53 cases with 61 aneurysms and 62 non-aneurysm cases based on a leave-one-out-by-patient test method. As a result, the CAD system achieved a high sensitivity of 97% with 3.8 false positives per patient by use of the SBDI technique.
## 3 Results and Discussion
### Relative Humidity and Bulk Density  
The DiMaL samples obtained from the VMP process were analyzed for relative humidity and bulk density, and the results are shown in Table 5.  
Humidity increased as the percentage of leather in the composite rose, which was expected. This phenomenon was observed in the sample composed of HD leather. However, although HD and P leathers were similar and the former could accommodate a higher loading percentage, the density of the latter was higher. The density of the Talyn composite sample was particularly relevant, resembling polypropylene, the thermoplastic currently used in spirit bottle caps (see Figure 10). The resin, used as a binder, is the main contributor to the compound's mass. Nevertheless, the choice of this resin was motivated by two aspects that ultimately proved decisive: its high level of transparency and the fact that, according to its manufacturer, it contains 20% natural oils.  
As observed among the collected cork samples (Figure 10) from various wineries within the Jerez wines region (Cadiz, southern Spain), despite design changes over more than 100 years, the size and shape of the cork has remained unchanged over time. Although the use of internal reinforcements in the housing to improve cork-plastic adhesion seems prevalent, there was only one case where this was not used (bottom sample in red). Similarly, these reinforcements were not used in the samples manufactured in this study, yet the adhesion was successful. In reference [38], tensile tests were performed to verify the adhesive strength of the bond, and the results showed that in most samples, the cork broke before detaching. Additionally, a study of the actual adhesive consumption in these types of crown-cork joints was conducted in [25].
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 3 Study of the Deformation and Instability Modes of the Slope After Excavation and Backfilling
### Contact Surface Analysis  
To investigate the sliding trend between the soil and rock layers at the contact surface, Figure 7 presents the shear displacement contour plot. The shear stress at the contact surface is distributed within the magnitude range of \(\times 10^{-2}\) to \(\times 10^{-5}\). The central part of the contact surface, with a relatively greater depth of the soil and rock layers, is more influenced by gravity, resulting in the maximum shear stress. As the soil and rock layers at both ends thin, the shear stress gradually decreases towards the ends, and the overall shear stress increases with the depth of the contact surface.  
Figure 6: Stress response contour plot with contact surface: (**a**) maximum shear strain increment; (**b**) maximum principal stress; (**c**) maximum shear stress.  
Figure 7b shows the shear displacement at the contact surface, following a similar pattern: the displacement is greatest at the center and gradually decreases towards the ends. The maximum shear displacement occurs at the shear surface ID1 near the exposed section, showing a reduction in overall shear displacement with increasing depth. This is due to the increased shear and normal stiffness in the deeper sections of the soil and rock mass [34]. Figure 7c illustrates the shear surface sliding diagram, where all five contact surfaces are shown in purple, indicating no sliding. This suggests that under gravitational forces, the soil and rock layers between the contact surfaces remain relatively stable, making interlayer sliding unlikely.
# Intelligent Reception of Frequency Hopping Signals Based on CVDP
## 1 Introduction
* The CVDP network combines the inductive bias property of CNN networks with the global inductive modeling capability of VIT networks [28] to solve the problem of poor convergence of VIT networks on small data sets and to improve the stability and robustness of VIT networks. A dual multi-head self-attention mechanism is proposed for extracting the time-frequency features of different low-dimensional subspaces to improve the network performance. Meanwhile, the idea of _Probsparse_ self-attention [29] is introduced to calculate only the self-attention of key points to reduce the computational complexity. * Extensive simulation experiments have been conducted to evaluate the performance of the CVDP network in various scenarios, including multi-tone interference, single-band interference, multi-band interference, swept frequency interference, mixed interference, and channel fading, encompassing both frequency-flat Rayleigh fading and frequency-selective Rayleigh fading. The simulation results indicate that the CVDP network exhibits a high level of generalization performance across all of these environments. Moreover, the intelligent receiver system, which incorporates the CVDP network, performs remarkably close to an ideal receiver when dealing with an unknown frequency hopping sequence.
## 5 Discussion  
### Temporal Variation of the Tree Cover in the Loess Plateau  
Tree covered area and tree cover values both increased in the Loess Plateau from 2000 to 2014 (Figure 4). Since the Grain for Green Project (GGP) launched in the Loess Plateau in 1999, a great number of trees were planted annually [41]. Furthermore, this project also helped protect natural vegetation [13]. Primarily due to the land-use policy implemented by the Chinese government, forest area increased in the Loess Plateau after 2000 [42]. Although vegetation is closely related to climate, it is a relatively short time period to evaluate any potential climate-influenced expansion of forested areas [15]. The GGP also enhanced forest coverage (older forest and newly forested land) [43]. The tree cover trend increased nearly 40-fold in the Loess Plateau, which was attributed to the GGP [11]. It is said that the GGP overemphasis on tree and shrub planting in environmentally fragile areas and failure to consider climate, soil, hydrology, and landscape factors are questionable [13]. The GGP dominantly resulted in the tree-covered area and tree cover value increased in the Loess Plateau.  
The tree covered area tended to increase from 2000 to 2014, but with some fluctuations (Figure 3). It experienced a rapid increase at first, and decreased, and slightly increased again, which may have been caused by the different stages of GGP implementation. At the beginning of the GPP, many trees were planted, and the tree covered area continued to increase, peaking in 2003. However, after 2003, it decreased and reached the minimum in 2006, which may have been due several reasons. First, trees cultivated in an inappropriate environment place may not grow well or even die. For example, many trees planted in the typical steppe zone or desert steppe zone were termed'small-aged trees' characterized by much smaller sizes than what would be expected in another environment [44]. The failure of afforestation in desert steppe or desert zones also demonstrated this size discrepancy. Second, planting inappropriate tree species may also cause other problems [45]. For instance, cash trees (e.g., apricot and apple trees) did not always grow well due to water scarcity and unfavorable climate in the Loess Plateau [46; 47]. Other detrimental conditions in the Loess Plateau may also lead to trees death. In arid and semiarid areas, soil moisture is generally deficient in planted forests due to low rates in annual precipitation, and has led to large-scale of plantations mortality during drought years [48]. This decrease in forested area was regarded as the second stage of the project. Even though the three reasons above have led to a decline in tree cover, as the project expanded, the tree covered area increased, and the number of trees planted annually decreased [41]. Thus, the tree covered area did not change drastically on an annual basis after 2006, tending to be stable. In 2014, it was greater than that in 2000 at the beginning of this project.
# A Deadbeat Current and Flux Vector Control for IPMSM Drive with High Dynamic Performance
## 5 Evaluation Results
### Parameter Sensitivity Analysis for Proposed DB-CFVC  
In this section, the robustness of the proposed DB-CFVC scheme in regard to motor parameter variation is evaluated via experiments on the HIL device (Figure 5(b)). The examinations are conducted under the scenario of simultaneous resistance and inductance mismatches during operation. These tests are used to thoroughly investigate the robustness of the proposed DB-CFVC to variations of the winding resistance and \(d\)-axis inductance and the effectiveness of using the estimated \(q\)-axis inductance, \(\hat{L}_{\text{q}}\).  
In this case, it is assumed that the winding resistance is varied to 150% of the original one (i.e., \(R^{\prime}_{\text{s}}=1.5R^{0}_{\text{s}}\)), and the \(d\)-\(q\)-axis inductances are varied to 75% of the original ones simultaneously (i.e., \(L^{\prime}_{\text{d}}=0.75L^{0}_{\text{d}}\) and \(L^{\prime}_{\text{q}}=0.75L^{0}_{\text{s}}\)). Then, the IPMSM is driven at low and high speeds under heavy load (2.5 Nm). There are two test cases: without and with updating the value of the estimated \(q\)-axis inductance, \(\hat{L}_{\text{q}}\). The comparison between the two cases (i.e., without and with updating \(\hat{L}_{\text{q}}\)) is shown in Figure 7(a) (at base speed, 2300 rpm) and Figure 7(b) (at low speed, 100 rpm), where the left side of each figure shows the result without updating \(\hat{L}_{\text{q}}\).  
As can be seen in Figure 8a,b, the results reveal that only the \(q\)-axis inductance has an impact on the output quality of the IPMSM controlled by the proposed DB-CFVC method. In particular, the torque and flux ripple increase when \(q\)-axis inductance reduces (by 25% here). Moreover, the flux linkage estimation error increases due to variation of \(q\)-axis inductance; however, it seems to be insignificant and independent of the operating speed.  
Figure 8: Comparison of speed, torque, flux linkage, current of proposed DB-CFVC between \(\mathrm{w/o}\) and \(\mathrm{w/updated}\)\(\hat{L}_{\mathrm{q}}\) toward parameter mismatches scenario of \(R_{\mathrm{s}}^{\prime}=1.5R_{\mathrm{s}}^{0}\), \(L_{\mathrm{d}}^{\prime}=0.75L_{\mathrm{d}}^{0}\), and \(L_{\mathrm{q}}^{\prime}=0.75L_{\mathrm{d}}^{0}\) at (**a**) high speed (2300 rpm) and (**b**) low speed (100 rpm) under heavy load (2.5 Nm) (where \(\mathbf{\
abla_{\mathrm{act}}}\) is actual stator flux linkage) (HIL result).  
The results on the right side of Figure 8a,b show that the drive system improves as the estimated \(q\)-axis inductance variation is updated. This demonstrates the usefulness of the \(q\)-axis inductance estimation. It also confirms the robustness of the proposed DB-CFVC method to mismatch of resistance and \(d\)-axis inductance.
# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
## 3 Example  
The monitoring data of a long-span cable-stayed bridge, which consists of a northern-branch bridge and a southern-branch bridge, was used for method verification. Specifically, the acceleration data of the bridge's girders and piers, totaling 70 channels with a sampling frequency of 50 Hz, were employed. The sensor locations and sensing directions are shown in Figure 5.
# Multi-View Ground-Based Cloud Recognition by Transferring Deep Visual Information
## 1 Introduction
The cloud features used in the existing methods are not discriminative enough to sufficiently describe cloud images when presented with view shift, and therefore we propose an effective method named transfer deep local b  
Figure 1: (**a**) We present cloud images from two different views; (**b**) The performance of three competitive methods degrade when presented with view shift. feature representation. Concretely, we first train a CNN model, and we propose part summing maps (PSMs) based on all feature maps for one convolutional layer. Then we extract LBP in local regions from the PSMs, and each local region is represented as a histogram. Finally, in order to adapt view shift, we discover the maximum occurrence to make a stable representation. After cloud images are represented as feature vectors, we compute the similarity between feature vectors to classify ground-based cloud images. Classical distance metrics are predefined, such as the Euclidean distance [18], chi-square metric [13] and quadratic-chi metric [19]. Hence, we propose a learning-based method called weighted metric learning (WML) which aims to utilize sample pairs to learn a transformation matrix. In Figure 2, green and blue indicate two kinds of feature spaces. Two samples from both feature spaces comprise a sample pair. Here, the red lines denote similar pairs, while black lines denote dissimilar pairs. In practice, the number of cloud images in each category greatly differs. For example, there are many clear sky images as the clear sky appears frequently, while there are few images of altocumulus which has a low probability of occurrence. There exists an unbalance problem of sample pairs when we learn the transformation matrix. Hence, to avoid the learning process being dominated by sample pairs in which clouds appear frequently, and neglecting limited sample pairs in which clouds occur rarely, we propose a weighted strategy for metric learning. We assign a corresponding weight for sample pairs in each category. Thus, we assign a small weight to sample pairs that possess a large number (squares in Figure 2) and assign a large weight to sample pairs that possess a small number (circles in Figure 2). Finally, we utilize the nearest neighborhood classifier, where the distances are determined by the proposed distance metric, to classify cloud images which are from another feature space. The rest of this paper is organized as follows. Section 2 presents the related work including feature representation for ground-based cloud recognitions and metric learning. The details of the proposed TDLBP and WML are introduced in Section 3. In Section 4, we conduct a series of experiments to verify the proposed method. Section 5 summarizes the paper.
## 2 Materials and Methods
### Grid Size  
Existing studies have shown that grid size has a great influence on the accuracy of simulation results, and mesh size has a strong relationship with the fire characteristic diameter \(D^{*}\). When the fire characteristic diameter \(D^{*}\) is between 4 and 16 times the grid size, the simulation results can reflect the real situation more accurately [24] The calculation method of \(D^{*}\) is as follows:  
\[D^{*}=\left(\hat{Q}/\rho_{\infty}c_{p}T_{\infty}\sqrt{g}\right)^{2/5}, \tag{2}\]  
where \(D^{*}\) is the fire characteristic diameter, \(\hat{Q}\) is the HRR, \(\rho_{\infty}\) is the air density, \(c_{p}\) is the specific heat capacity of air, \(T_{\infty}\) is the ambient temperature, and \(g\) is the gravity acceleration. As shown in Equation (1), the fire characteristic diameter will increase when the ambient pressure decreases.  
As a result, the suitable grid size for the present 30 MW fire ranged between 0.233 m and 0.94 m. The simulation results for the case of curvature radius of 800 m and with ventilation of 2 m/s (the same as test 9) at 150 s were compared using five different grid sizes of 0.25 m, 0.3 m, 0.4 m, 0.5 m, 0.6 m, and 0.9 m, as shown in Figure 2. It is seen that as the grid size decreases, the temperature discrepancy becomes insignificant. Except for the gird sizes of 0.6 m and 0.9 m, the vertical temperatures near the tunnel ceiling from other grid sizes were similar. When the grid size was no more than 0.5 m, the accuracy of the simulations with smaller grid sizes did not improve significantly but required more  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Radius of Curvature, m** & **Longitudinal Ventilation Velocity, m/s** \\ \hline
1, 2 & \(\infty\) & 2, 4 \\
3, 4 & 250 & 2, 4 \\
5, 6 & 400 & 2, 4 \\
7, 8 & 600 & 2, 4 \\
9, 10 & 800 & 2, 4 \\
11, 12 & 1000 & 2, 4 \\
13, 14 & 1200 & 2, 4 \\
15, 16 & 1500 & 2, 4 \\
17, 18 & 2000 & 2, 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of all tests.  
simulation time. Therefore, the grid size was finally determined as 0.5 m. Considering that the flow fields near the fire source were more complex, the grid size in the zone within a 200 m radius surrounding the fire source was refined as 0.25 m, and the grid sizes in the rest zones were set as 0.5 m.