esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Georeferencing SAR Images  
The process of extracting SL and computing rates of SL change from SAR imagery is depicted in the flowchart shown in Figure 4.  
Initially, S1-SAR data are processed to produce a georeferenced image using the tools provided by the Sentinel Application Platform developed by the ESA (2019). Double correction is applied to the signal to remove noise (thermal and border noise) after updating the orbit state vectors provided in the metadata of SAR data (applying the orbit file). Next, radiometric correction is applied to the data so that the pixels in the SAR images represent the radar backscatter of the reflecting surface (calibration). Another radiometric calibration (terrain flattening) is applied to reduce the distortions caused by different local incidence angles on the scene under observation. Using the digital elevation model from the Shuttle  
Figure 4: SAR processing flowchart used in this study showing the three main consecutive steps of georeferencing, SL extraction, and filtering.  
Figure 3: Available S1 Level-1 Ground Range Detected datasets for Start Bay as of 31 July 2023. In brackets, the number of available images is reported (**a**); examples of S1 VV (**b**) and VH (**c**) acquisition in Start Bay (UK), 10 June 2023. Credit: European Union, contains modified Copernicus Sentinel data 2023, processed with EO Browser.  
Radar Topography Mission (SRTM), this tool recalculates the actual radiometry based on the area illuminated per pixel, thereby limiting the effect of topography on backscatter intensity. A despeckling filter is applied to the SAR image to mitigate the speckle effect, which results in a degradation of the resolution and improves the image's interpretability. There are several speckle filters available on the Sentinel Application Platform. In this study, we use the [PERSON] filter [26]. This particular filter offers a balance between maintaining spatial resolution and preserving edges, making it a favorable choice [24; 27; 28]. Following a conversion from linear to decibel (dB) scale, as highlighted in [24], terrain correction is applied to the data. This tool corrects the geometric distortions caused by topography, such as foreshortening and shadowing, which are responsible for geolocation errors. Based on the same elevation model used in terrain flattening, terrain correction moves image pixels into proper spatial relationships with each other. The final step is collocation. Using an optical image from S2, previously reprojected in the same coordinate reference system (WGS84) and resampled, all the SAR images are \"collocated\", overlapping them with the same S2 image.
# Children's Functional Clothing: Design Challenges and Opportunities
## 4 Functional Textiles
### Functional Textiles for Improved Protection and Wellbeing
#### 4.1.3 Thermoregulating Textiles  
Comfort is a fundamental aspect for human wellbeing [46]. Providing an optimal and comfortable thermal environment is essential for ensuring the health and development of children. Thermo-regulating textiles, engineered materials designed to adjust to environmental conditions, contribute to overall wellbeing by providing thermal comfort and reducing energy consumption. Thermal comfort refers to the psychological state of contentment with the ambient temperature, avoiding extremes of cold or heat [34].  
During normal wear, the body's insensible perspiration generates a continuous heat and moisture vapour flux, crucial for thermoregulation and thermal comfort [46]. Recognising and addressing thermal comfort contributes to children's physical wellbeing and their focus and engagement in various activities. This is because ensuring an ideal thermal environment influences emotions, productivity, and overall comfort, creating a more favourable atmosphere for learning and play.  
Individual comfort levels with similar thermal sensations vary due to factors such as physical activity, metabolic level, and temperature susceptibility [34]. The authors note that fabrics excel in either breathability or moisture absorption, rarely balancing both. However, a study conducted by [PERSON] et al. [46] developed interlock fabrics featuring with a TransDry (TransDry is a patented technology that combines the comfort of cotton with high-performance moisture-wicking capabilities), incorporating hydrophilic and hydrophobic yarns alternately. This strategic design optimises the comfort properties of knitted fabric by overcoming the usual trade-off between breathability and moisture absorption.  
Various types of textiles have been studied for their potential in thermoregulatory clothing, including radiation-controlled, conduction-controlled, dual-mode, and responsive textiles [27; 33]. Overall, thermo-regulating textiles are crucial in enhancing comfort, performance, and wellbeing in various aspects of daily life. Their ability to adapt to changing conditions makes them an asset in achieving optimal comfort and functionality.
## 1 Introduction
### Background  
LiDAR as well as other sensors used for tracking objects can be deployed on mobile or static platforms. The first case is the typical setup for mobile mapping, autonomous vehicles, and robotics. This is a challenging case, because the platform is moving, as is the tracked object, and consequently, the sensor has to be accurately georeferenced at all time. In contrast, the static setup is a simpler case, since the sensor georeferencing has to be done only once. This arrangement is commonly used in surveillance or transportation applications for traffic monitoring. In this study, the static sensor configuration is applied.  
Several algorithms exist for tracking objects, such as pedestrians or vehicles, in 3D LiDAR data, and can be divided into model-free and model-based approaches according to [PERSON] et al. [15]. The model-free approaches do not assume any properties, such as the shape of the object or motion model, which the tracked object is following. In contrast, model-based approaches use prior information regarding the object to achieve more reliable tracking. Obviously, model-free algorithms are more general, but model-based methods perform better. Methods introduced in this study are model-based and consider the type of the object assuming certain types of motion models.  
The general workflow of the object tracking in point clouds includes object segmentation, recognition and tracking. The dynamic elements of each scan are separated from the static background during the segmentation step, then the object is recognized in the model-based approach [2; 3]. Afterwards, the trajectory or motion parameters extracted from consecutive scans, and thus, the captured points from the moving objects, are known epoch-by-epoch. One can easily estimate the trajectory by calculating, for example, the center of gravity (COG) of the segmented points from each scan. Other solutions are based on point cloud registration algorithms. The iterative closest point (ICP) method [16] can be used to estimate, for instance, the six degrees of freedom (rigid body) transformation matrix between the consecutive and segmented scans (see for instance [17]). The transformation matrices contain the rotation and translation parameters that can be used for computing the trajectory. In another approach, instead of using the whole segmented scan, the common feature points or points sets can be extracted from the scans, and the transformation matrix is estimated based on these correspondences [18]. The tracking can be improved with considering the previous scans using Bayesian or Markov approaches [18; 19], or the final trajectory can be refined with Kalman filtering or other smoothing techniques.
# Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder
## 3 Results and Discussion
#### Comparison with State-of-the-Art-Methods  
The proposed CNA architecture's performance was compared with the results presented in papers that used the same database, but different methods and experimental setup. Considering this, the presented comparison is used to put the achieved results into context, since the same database was used. The papers listed in the tables presented their results through the average value of _Acc_, and in order to make a comparison, in Tables 4 and 5 only the coefficient _Acc_ is shown. In Table 4 papers using _k_-fold cross-validation for the network testing are presented. Additionally, the performances were compared with works in which the network was not tested by _k_-fold cross-validation but used one test, Table 5. It has been shown that the proposed architecture achieves better results than the results presented in the literature.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Evaluation Method** & **Data Division** &
\begin{tabular}{c} **Training** \\ **Dataset** \\ \end{tabular} & **Mean/Median** & _Acc [\%]_ & _Se [\%]_ & _Sp [\%]_ & _Pr [\%]_ & _Dice [\%]_ \\ \hline Cross-validation & Record-wise & Original & mean & 99.17 & 99.68 & 69.48 & 70.06 & 68.78 \\  & & median & 99.53 & 99.84 & 83.89 & 83.04 & 8  
In the literature, we also found other methods that were evaluated on the same dataset, like [PERSON] et al. (2016). They have applied a new image segmentation method that involves several different approaches to determine the intensity threshold on which pixels are extracted. Furthermore, they performed segmentation on several other databases, among which was the database used in this paper, but the results show metrics and segmentation for only seven images. Additionally, [PERSON] et al. (2017) presented the CNN architecture for segmentation and classification of different types of brain tumors, but the  
Figure 3: Examples of segmentation after training the network on the original dataset with recordwise data division. The original MRI image, mask, and segmented tumor are shown. The type of tumor, as well as the _Dice_ coefficient achieved, are also indicated.  
results of segmentation are presented only in the form of a few examples. In his work, [PERSON] [1] proposed a new architecture of autoencoders to segment tumors in the brain. Still, when dividing the database, an uneven division was used, i.e., 5% of the images were used for testing, and 0.2% of the training set, which makes up 95% of the total image database, was used the validation set.
## 4 Test Method and Apparatus
### Testing Apparatus Design and Setting for Joint Displacement Testing  
The apparatus consists of a UTM (Universal Testing Machine) and a specially structured acrylic container. The test specimen is first installed in the acrylic container, which is then filled with water, and inserted into the UTM for displacement load testing. Once installed, the upper mortar slab section of the specimen is pulled up and down vertically in relation to the bottom slab, which is fixed to the apparatus. Refer to Figure 6 for details.  
Below is a set of more detailed steps for installing the specimen onto the testing apparatus:  
1. The test specimen is installed into the acrylic chamber apparatus by screwing the threaded conduit of the bottom mortar slab onto the metallic thread at the bottom of the chamber.
2. The container is filled with approximately 15 L of water. The specimen should be completely submerged in water.  
Figure 5: Waterproofing membrane installation process illustrated (**a**) mortar sections placement and surface treatment; (**b**) primer, adhesive, or other adhesion material application; (**c**) waterproofing sheet application; (**d**) Test specimen complete and ready for testing.  
Figure 6: Testing apparatus illustrated (**a**) Container and components; (**b**) Setting in apparatus.  
3. The chamber is installed onto the UTM. The bottom connection is anchored with a rivet to the jig of the UTM. The upper mortar slab threaded conduit is then anchored to the upper jig of the UTM.
4. The joint displacement speed (construction joint movement rate) is set to 50 mm/min.
5. The test is initiated in the conditions outlined in Table 7.
## 5 Conclusions  
An inter-laboratory exercise with PEMS in eight laboratories in Italy showed that the reproducibility of the PEMS methodology is at the same level (CO 20-25%, NOx 23-31%) or only slightly higher (CO\({}_{2}\) 5.5% vs. 2.9%, PN 39% vs. 29%) compared to the laboratory legislated method. The reproducibility values of the Golden PEMS were at the same levels as the laboratory method. The higher scatter of the CO\({}_{2}\) measurements was attributed to the exhaust flow measurement, while the higher absolute levels of the PN were attributed to particle losses taking place from the vehicle until the dilution tunnel. These results confirm the permissible tolerances of the regulation. Additionally, they open the possibility of conducting inter-laboratory exercises only with PEMS, without the need of chassis dynamometers.  
conceptualization, B.G. and A.D.D.; methodology, B.G., S.C., M.M., M.S., G.M., R.T., M.D., G.C., F.D.T., M.V.P., M.C., and A.D.D.; validation, M.C.; formal analysis, B.G.; data curation, A.D.D.; writing--original draft preparation, B.G.; writing--review and editing, B.G., S.C., M.M., M.S., G.M., R.T., M.D., G.C., F.D.T., M.V.P., M.C., and A.D.D.; supervision, A.D.D.; project administration, A.D.D.  
This research received no external funding.  
The authors would like to thank the technical personnel for conducting the testing. Special acknowledgments to [PERSON] for his suggestions at an earlier draft.  
The authors declare no conflict of interest.  
The opinions expressed in this manuscript are those of the authors and should in no way be considered to represent an official opinion of the European Commission and their organizations. Mention of trade names or commercial products does not constitute endorsement or recommendation by the authors.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 2 3-D Antenna Configuration
### 3-D AES and Antenna Element's Positioning
#### 2.1.1 Transmit Antenna Configuration  
Massive MIMO with dipole antennas are crossly implemented at the TX. The 3-D Cartesian coordinate systems \(\rho_{mn}\) with \(X_{m,n}=(x_{m,n},y_{m,n},z_{m,n})\) spherical coordinate of a point, where \(x_{m,n}=\rho_{m,n}\sin\phi_{AAoD}\cos\theta_{EAoD},y_{m,n}=\rho_{m,n}\sin\phi_ {AAoD}\sin\theta_{EAoD}\), and \(z_{m,n}=\rho_{m,n}\cos\theta_{EAoD}\). However, antenna element's point of SRAA can be obtained from its Cartesian coordinates \((x,y,z)\) by the formula  
\[\rho_{m,n}=\sqrt{x_{m,n}^{2}+y_{m,n}^{2}+z_{m,n}^{2}} \tag{1}\]  
The position of two individual antenna elements, one indexed by \(lk\) and the other one indexed by \((mn)\) at the TX can be computed as  
\[d^{BS}_{X_{pq}}=\sqrt{(x_{mn}-x_{lk})^{2}+(y_{mn}-y_{lk})^{2}+(z_{mn}-z_{lk})^ {2}} \tag{2}\]
# An Interpretable Deep Learning Method for Identifying Extreme Events under Faulty Data Interference
## 5 Conclusions  
This paper presents an interpretable vision-based deep learning method for identifying seismic events under the interference of multi-type anomalies. The raw time series was transformed into time- and frequency-response images, respectively, turning the seismic event and anomaly detection problem into an image classification task. Transfer learning was conducted based on the ResNet34, and the interpretation algorithm Grad-CAM was embedded for outputting understandable heat map to highlight the important region for classification. The novelty of the research is that both seismic data and faulty data are considered, which aims at the challenging issue in practical engineering applications.  
Figure 14: Visualization of the confusion matrix of Figure 13.  
Figure 15: Samples of classification errors.  
Additionally, not only classified labels, but also explainable visual information are provided, enhancing human trust in the decision-making of the neural network.  
Four seismic monitoring datasets of a long-span cable-stayed bridge were used for method verification. The dataset contained not only normal and seismic data but also four anomaly patterns: Missing, Minor, Bias, and Drift. The results show that the seismic responses were successfully identified with a precision of over 80 percent and a recall of over 60 percent. The recognized earthquake starting time corresponded well with the groundtruth. Moreover, pattern Missing, Minor, and Drift were accurately identified, while the recall of pattern Bias was relatively low due to a large number of misclassified normal samples. Meanwhile, the regions of interest for each sample for classification were obtained, revealing that the neural network focused on low-level and discrete features at shallow layers, and high-level and holistic features at deep layers. Additionally, representative features for each pattern were discovered. By analyzing the heat maps, the reason for the misclassification of some samples was identified, namely the coexistence of multiple patterns features in the same sample. Therefore, we will investigate fine-grained image segmentation or a multi-label classification method to solve the problem.  
Conceptualization, [PERSON]; methodology, Z.T.; software, J.G.; resources, C.Z. and W.X.; data curation, J.G.; writing--original draft preparation, J.G.; writing--review and editing, Z.T. and W.X.; supervision, Y.W.; project administration, W.X.; funding acquisition, Z.T. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Yunnan Fundamental Research Projects (Grant No. 202301 AT070394, 202101 AU070032), and the Fund for Less Developed Regions of the National Natural Science Foundation of China (Grant No. 12162017).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
## 6 Experiment
### Benchmark Test
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & \\ \cline{3-10} \(\lambda\) & **Algorithms** & **LoSS** & \multicolumn{3}{c}{**Game**} & \multicolumn{3}{c}{**Maze**} & \multicolumn{3}{c}{**Random**} & \multicolumn{3}{c}{**Street**} \\ \cline{3-10}  & & & **Length** & **Time** & **Length** & **Time** & **Length** & **Time** & **Length** & **Time** \\ \hline \multirow{4}{*}{0.3} & \multirow{2}{*}{A*} & No & 45.63 & 1.853 & 45.98 & 0.803 & 42.21 & 1.588 & 49.59 & 2.009 \\  & & Yes & 43.33 & 1.869 & 44.14 & 0.823 & 40.99 & 1.609 & 47.14 & 2.027 \\ \cline{2-10}  & \multirow{2}{*}{Theta*} & No & **43.31** & 2.804 & **44.08** & 1.207 & **40.35** & 2.022 & **47.11** & 3.051 \\  & & Yes & 43.31 & 2.815 & 44.06 & 1.221 & 40.32 & 2.038 & 47.11 & 3.063 \\ \cline{2-10}  & \multirow{2}{*}{Lazy Theta*} & No & 43.35 & 1.993 & 44.20 & 0.884 & 40.73 & 1.671 & 47.19 & 2.171 \\  & & Yes & 43.34 & 2.004 & 44.16 & 0.897 & 40.66 & 1.687 & 47.16 & 2.182 \\ \cline{2-10}  & \multirow{2}{*}{LLA*} & No & **43.33** & **1.561** & **44.15** & **0.686** & **40.62** & **1.357** & **47.14** & **1.699** \\  & & Yes & 43.32 & 1.572 & 44.12 & 0.699 & 40.56 & 1.372 & 47.13 & 1.712 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average path length and execution time (s) with/without LoSS smoothing. Figure 7: The average path length and execution time plotted against the heuristic weight. LLA* is the fastest, and it generates a shorter path than Lazy Theta*.
## 4 Results and Discussion
### Case 4: Minimization of Fuel Cost, Power Losses, and Voltage Drops  
Previously, the proposed algorithm optimized a specific objective, such as fuel cost, power loss, or voltage drop. However, this may impact other attributes as can see in Tables 5, 7 and 9. In Table 5, ESDO succeeded in achieving the least fuel cost but it failed to achieve at the same time the least V.D. Also, Table 7 shows the ESDO reaches the best value for power loss at the expense of high fuel cost and voltage drop. In the third scenario, the  
Figure 8: Case 3 convergence curve. Figure 9: V.D. for different algorithms. ESDO variants attained very low V.D. compared to other techniques, despite having the highest power loss as shown in Table 9. In order to handle this issue, the suggested approach in Case 4 utilizes Equation (24) as a multi-objective function to update the parameters with the aim to reduce fuel costs, power losses, and voltage drops, simultaneously. Table 10 shows all the parameter values for all algorithms in this case. Figure 10 illustrates Case 4's convergence curve; this graph presents the objective function values over 50 iterations for different optimization algorithms. The PSO curve represents the highest initial objective function value lying above 1180; this dropped drastically between iterations 5 and 20 and then gradually stabilized at around 1020 by the 50 th iteration. MVO, represented by the cyan-colored curve, stays very low from the start, then slowly decreases with minor bumps, before it converges and stabilizes around 1020. GOA shows, in general, very slow convergence; it started high and improved very little before it converged and stabilized at a higher objective function value compared to other algorithms. The HHO starts at about 1140, sharply drops in the early iterations, quickly stabilizes, and further improves slightly after iteration 25, finally stabilizing below 1040. HO starts high, dropping rapidly within the first few iterations, then converges smoothly
# Combined Mie Resonance Metasurface for Wideband Terahertz Absorber
## 2 Structure Design and Simulation
(**b**) Absorption spectra of the traditional metasurface absorber with single dielectric resonator (side lengths from 31 \(\upmu\)m to 35 \(\upmu\)m) in one unit cell.
## 1 Introduction  
In the last few years, we have been witnessing a new \"Space Race\" with numerous players united by the willingness to establish a sustainable human presence around the Moon environment. It is estimated that international space agencies assisted by a constantly growing number of private space companies have scheduled more than 140 missions towards the Moon during the decade 2023-2033 [1]; the greatest boost comes from the National Aeronautics and Space Administration (NASA) Artemis program closely followed by the analog Chinese and European lunar programs.  
In such an animated framework, it is necessary to set up a series of guidelines to ensure the sustainability and long-term preservation of the lunar space and surface; for these reasons, the International Space Exploration Coordination Group (ISECG) has been created. It is a non-binding coordination forum of space agencies, which exchange information regarding interests, plans and activities in space exploration and which work together to strengthen both individual exploration programs and the collective effort. One of the most useful contributions is the setting up of a Global Exploration Roadmap (GER) that presents a shared international vision of human and robotic space exploration and reflects the exploration strategy and goals of the ISECG space agencies [2].  
Due to the certified presence of water ice and its favorable environmental conditions, the lunar south pole has been pointed out as the most suitable region to accomplish proposed exploration goals and discovering its main features will be pivotal.  
Within this context is placed this work whose aim is to provide an overview of the lunar south pole by highlighting the aspects that have made it an ideal candidate for future human settlement. In particular, the state-of-the-art analysis of this region is strengthenedby a different perspective of the lunar south pole thanks to an experimental Earth-based spectroscopic survey within the visible range carried out from the Asiago Astrophysical Observatory (Asiago, Italy). The purposes of this survey are several, from the search of correlations with water abundance to the characterization of the lunar south pole areas to provide data and information for the design of future orbiter missions.