esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we develop a vector decomposition method of the P- and S-wave modes from the coupled elastic seismic wavefields using SADCGANs for isotropic elastic media. To stabilize the training process and achieve more accurate wavefield decomposition results, we introduce the self-attention modules into the generator and the discriminator to simultaneously extract the local and global features of input samples. To demonstrate the effectiveness, feasibility and adaptability of our developed method, it is applied to a simple isotropic elastic model and the Hess isotropic elastic model. According to the numerical experimental results of different models, we can draw the following conclusions. Our developed method can effectively generate the horizontal and vertical components of the decomposed vector P- and S-wave modes from the coupled elastic seismic wavefields and accurately preserve their amplitude and phase characteristics for different elastic models even if the source position changes. The accuracy of training sample labels largely determines the accuracy of our developed method, and its vector decomposition results of different wave modes can only be as close as possible to the training sample labels. Our developed method is an intelligent data-driven algorithm which is not dependent on elastic model parameters and certain prior conditions compared with the conventional algorithms. Our developed method can be well applied to the elastic wavefield extrapolation process of ERTM to effectively achieve the vector decomposition of the P- and S-wave modes to further improve the multi-wave imaging accuracy.  
Conceptualization, W.L. and J.Y.; methodology, W.L.; software, W.L.; validation, J.Y. and J.C.; formal analysis, W.L.; investigation, J.Y. and J.C.; resources, W.L.; data curation, W.L.; writing--original draft preparation, W.L. and H.W.; writing--review and editing, W.L. and H.W.; visualization, J.Y.; supervision, J.C.; project administration, J.Y.; funding acquisition, J.C. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42030812), the Sichuan Science and Technology Program (Grant No. 2023 NSFSC0257) and the CNPC Innovation Found (Grant No. 2022 DQ02-0306).  
Not applicable.  
The data are available upon request.  
All the authors listed in this paper are very grateful to the editors and the anonymous reviewers for their helpful comments and suggestions to improve the paper.  
The authors declare no conflict of interest.
# Development and Assessment of a 4D Printing Technique for Space Applications
###### Abstract  
Shape memory polymers (SMPs), a class of polymers exhibiting the unique ability to restore deformation induced during the programming process in response to external stimuli, have garnered significant attention. In this study, our objectives were two-fold: to develop an efficient device for programming SMP hinges crafted from polyetheretherketon (PEEK) and to optimize their performance for potential utilization in space applications. Two versions of the programming device were constructed and compared. Through three systematic experiments, we identified optimal programming and recovery conditions for the hinges, revealing the best shape memory effects (SMEs) at a programming temperature of 250 \({}^{\circ}\)C. Remarkably, the hinges were able to recover the previously induced deformation up to 100%, maintaining functionality down to a lower temperature limit of 150 \({}^{\circ}\)C. Notably, these hinges demonstrated a wide operational range of over 180\({}^{\circ}\), rendering them promising for space applications, as extensively discussed within the manuscript. However, challenges arise due to the high recovery temperature of 150 \({}^{\circ}\)C, presenting obstacles in achieving optimal functionality in the demanding conditions of a space environment.  
PEEK 3D printing; shape memory polymers; aerospace materials; shape memory analysis; self-deploying hinges +
Footnote †: journal: Applied Sci. 2020
###### Abstract  
Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A) to adjust the existing radiometric conversion coefficients according to time delay integration (TDI) adjustments and sensor degradation over time. The Second Simulation of a Satellite Signal in the Solar Spectrum (65) radiative transfer model was used to obtain theoretical top of atmosphere radiances for both satellites. As input parameters for the 65 model, surface reflectance values of well-characterized pseudo-invariant tarps were measured using dual ASD FieldSpec(r) 3 hyperspectral radiometers, and atmospheric conditions were measured using Microtops II(r) Sunphotometer and Ozonometer. We updated the digital number (DN) of the radiance coefficients of the satellites; these had been used to calibrate the sensors during in-orbit test periods in 2013 and 2015. The coefficients of determination, R\({}^{2}\), values between observed DNs of the sensors, and simulated radiances for the tarps were more than 0.999. The calibration errors were approximately 5.7% based on manifested error sources. We expect that the updated coefficients will be an important reference for KOMPSAT-3 and -3A users.  
Keywords:KOMPSAT-3; KOMPSAT-3A; Microtops II; radiometric calibration; vicarious calibration +
Footnote †: journal: Remote Sensing, 10, 697
# Adaptive Multi-Grained Buffer Management for Database Systems
## 3 Adaptive Multi-Grained Buffer Management
### Operations of AMG-Buffer  
First, we access the mapping table to determine whether the page descriptor has been loaded in the memory. Next, according to _PageType_ in the page descriptor, we can know whether the page is in the F-Buffer, the P-Buffer, or the S-Buffer. If the page is in the F-Buffer or the P-Buffer, we can retrieve the page address based on _BuffID_ of the corresponding page descriptor. If the page is in the S-Buffer, we need to check the tuple's existence using _VisitBitmap_.
# Machine Learning Techniques for Non-Terrestrial Networks
## 4 Non-Terrestrial Network Architectures
### Multi-Connectivity  
Multi-connectivity (MC) is defined in [34], and it is based on the bearer split at PDCP layer in UP. In some use cases envisaged in NTN, MC can be of interest, above all with TN. Examples are (i) in under-served areas where the terrestrial coverage is limited, as is the throughput for UE; (ii) in the case that the UE needs to have an increased throughput and TN cannot provide it; (iii) and to support vehicle operations (e.g., regional trains) along remote lines where TN coverage is highly variable.  
Figure 10: Protocol stack of the control plane in the NTN architecture with on-board distributed unit [27].  
According to [34], the two sites (i.e., the master node (MN), which provides the CP, and the secondary node (SN)) should be connected through the Xn interface. Two groups of MC can be set: the first case involves two NTEs, while the second involves one NTE and one terrestrial gNB. Depending on the location of the gNB (i.e., the PDCP layer, where the bearer split occurs), proper delays of one flow with respect to the other should be considered according to the channel latencies. Details are in [20; 27]. As a basic idea, in case the gNB is located at the ground (for example, when the MC is performed by two transparent NTEs or by two NTEs with on-board DU but CUs on the ground), the Xn interface is not critical. Similarly, with two on-board gNB (i.e., regenerative NTN), an ISL can provide the required connectivity between the two NTEs. Additionally, in the case of hybrid MC (i.e., involving terrestrial gNB and one NTE), the MC is not critical in case the NTE is transparent or with the CU on the ground (see Figure 8). More challenging is the case when MC involves a NTE with an on-board gNB (i.e., regenerative NTN) and a terrestrial gNB since a connectivity should be set between the MN and SN, and delays should be properly aligned due to the highly different path. Finally, MC can be provided also by two different types of NTE (e.g., one UAV and one LEO). Similar considerations apply related to the location of the gNB.  
In Figure 11, it is reported the typical architecture of the DC. We report two main cases. In the first case, we consider DC between one gNB in the TN and one in the NTN (see the HAPS), while in the second case, we consider two different NTEs, i.e., the HAPS and the satellite [20; 27]. Easier implementations are when gNBs are located both on-board or both at the ground, while more challenging is when gNB is on-board on an NTE and the second is in the TN. Anyway, in all cases, the Xn interface must be available to split the flows between the two involved gNBs.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 3 Methods
### Crop Classification  
Every crop classification procedure of this study was performed using the SVM classification algorithm. This particular method was selected, considering its high effectiveness, robustness, and generalization ability, especially when dealing with high dimensional, low quality, imbalanced, or scarce data [133; 134]. Apart from this, SVM algorithm has already been widely used and proved effective in crop type mapping applications using remotely-sensed data [19; 130; 134]. Specifically, four SVM classification models were produced, based on different sets of input data. In more detail, SVM classifier was trained using as input data: (1) Sentinel-2 optical features (S2 model), (2) Sentinel-1 polarimetric features (S1 model), (3) Sentinel-1 polarimetric features and Sentinel-2 optical features combined (S1/S2 model), as well as (4) an optimal subset of 80 mixed-type features obtained from a single GA implementation (GA model). Additionally, in an attempt to estimate features' relative importance, we carried out 100 GA runs and recorded how often a particular feature appeared in the 100 optimal feature subsets that were derived. We decided to conduct 100 individual experiments, since this number is relatively large, allowing for a statistical analysis of satisfactory quality with marginally permissible computational costs. The selection frequency of each feature could be interpreted as a measure of its relative importance for the crop classification problem at hand. Based on GA's feature selection results, we obtained a sorted list of the most important individual features that were exploited as input data for the construction of ten additional crop classification models. For all classification scenarios examined, SVM was implemented by using a second degree polynomial kernel and its hyperparameters were optimized using the grid-search method. The optimal values that were derived for each parameter and for all the investigated classification scenarios are presented in Table S3. Moreover, since we examine a multi-class crop classification problem, \"one-against-one\" strategy was employed, combining 45 binary SVM classifiers for each pair of classes in order to construct a single multi-class classifier [77]. Reference samples were randomly split into a training (30%) and a testing set (70%), resulting in a total number of 3698 training fields. The performance of each crop classification model was evaluated using four accuracy metrics: overall accuracy (OA), as well as class-specific f1-scores, user's accuracy (UA), and producer's accuracy (PA). The classification results were averaged over 10 runs for each scenario.
## 3 Proposed Scheme
### Communication Model  
In cooperative training, the data transmission of the uplink channel is easily limited. In addition, the model parameters are subject to noise interference during channel transmission. To reduce the communication overhead between the node and the central server, an improved auto-encoder model is considered in the communication process of the FL.  
In the communication process of global aggregation, it is assumed that \(n\) nodes participate in FL and send model parameters through a shared wireless channel. To ensure a fast, reliable, and privacy-preserving transmission process, the node first compresses and denoises the sent data through an auto-encoder. Upon receiving the data, the server decodes it to retrieve the actual parameters, facilitating model fusion. Specifically, node-to-server communication learns the mapping from input to itself through the encoding and decoding phases. Let \(E(.)\) represent the encoding function and \(w_{n}\) be the model weight of client \(n\). The channel input for client \(n\) is represented as \(H_{n}\):  
\[S_{n}=H_{n}E(w_{n})+N_{n}, \tag{3}\]  
where \(S_{n}\) is the channel output received by the server, and \(N_{n}\) is the noise term. \(D(.)\) is the decoding function. According to the received channel output, the decoding process of the server can be represented as:  
\[\widehat{w}_{n}=D(S_{n}). \tag{4}\]  
After decoding, \(\widehat{w}\) is the model update information for client \(n\). The server obtains the model update information of each client to complete the model fusion. The \(n\) nodes will obtain the updated spectrum-aware model and repeat the process until the model accuracy is as required.
# Advantages of Corrosion-Resistant Overlay Welding on Steel S355J2N
## 4 Results
### Salt Spray Test  
In corrosion testing, the goal is to assess the corrosion susceptibility by artificially simulating the effects on the sample.  
Figure 13 shows the state of the overlay welding before (Figure 13a) and after (Figure 13b) the salt spray test. The strong corrosive environment caused significant corrosion damage to the S355J2N base plate, but the corrosion-resistant layer appears undamaged to the naked eye. This conclusion is confirmed by markers indicated with red arrows.  
In order to compare the corrosion resistance of the welded joints with other steels of similar composition, we examined the resistance to pitting corrosion by determining the PREN (Pitting Resistance Equivalent Number) index [34]. The composition of the E316L wire electrode used in the second and third layers is very similar to the 316L (min. 2.5% Mo) austenitic steel, which has a characteristic PREN value of 25.3-30.7. Therefore, the PREN value of the deposited overlay welding should also fall within this range. The PREN value is determined according to the following Equation (4):  
\[PREN=Cr\%+3.3 Mo\%+16N\% \tag{4}\]
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 1 Introduction
### Motivation and Contribution
Despite the fact that smooth decision boundaries between a genuine user and seen impostors yield better generalization, they do not help detect unseen impostors be cause unseen impostors may have completely different patterns from seen impostors. To improve the robustness against unseen impostors, the proposed random pattern mixing aims to enlarge the feature space of impostor data, producing various synthetic patterns not present in seen impostors. To respond to the third challenge, we devise a novel local feature pooling (LFP)-enabled temporal convolution network (TCN) for raw sequential touch sensor data modeling. The TCN [30] is a variant of the convolution neural network (CNN) modified for sequential data modeling. [PERSON] et al. exploited the TCN for touch-gesture authentication, showing its superior performance over the LSTM baseline [31]. Indeed, the TCN can learn global information with its large receptive field and yields noise-robust global representations of touch screen data. In this paper, we enhance the TCN with the LFP module, which enables aggregating local feature information to learn fine-grained representations of impostors. Specifically, the LFP module prevents local information loss due to the sparsity of the kernel in the dilated convolution. Moreover, the LFP module is parameter-free; thus, it is suitable for applications on memory-constrained mobile devices such as smartphones. Finally, we demonstrate continuous authentication based on the decision and score-level fusion. Although many researchers have worked on continuous authentication, prior works have applied the sensor-level fusion, which enlarges window size according to re-authentication time [2; 3; 5; 10]. However, this approach exaggerates the long-term dependency problem for the recurrent neural networks (RNNs) or increases the model complexity of the CNNs. Therefore, we explore alternatives using the decision and score-level fusion, which maintain the window size and prevent the problems above. In summary, our contributions are as follows. * We formulate continuous mobile-based biometric authentication as a few-shot anomaly detection problem, aiming to enhance the discrimination robustness of the genuine user and seen and unseen impostors. * We present a sequential data augmentation method, random pattern mixing, which expands the feature space of impostor data, providing synthetic impostor patterns not present in seen impostors. * We propose the LFP-TCN, which aggregates global and local feature information, characterizing fine-grained impostor patterns from noisy sensor data. * We demonstrate the continuous authentication based on the decision and score-level fusion, which can effectively improve the authentication performance for longer re-authentication time. The rest of this paper is organized as follows. Section 2 reviews related works. Section 3 introduces our authentication method. Section 4 explains the experimental settings. Section 5 presents the experimental results and analysis. Finally, we end with the conclusion in Section 6.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 4 Present Work: colaGAE  
### Motivation  
The pretext task is a crucial aspect of graph SSL. In this section, we explain why mutual isomorphism represents a novel approach to the pretext task.  
GNNs are designed to extract valuable information from raw data and create representations that are generalizable, transferable, and robust. However, aggregating node features using a single latent space may not be enough. This perspective is supported by the attention mechanism, demonstrated in the work of [PERSON] et al. [35]. The attention mechanism is a way of expressing the same entity in different latent spaces. Each attentional head in the model represents a unique latent space. For instance, in BERT [36], each of the twelve attention heads produces a 64-dimensional vector, which are concatenated into a final 768-dimensional vector.  
The node representations are then used to reconstruct the graph structure \(A\). A recent study shows that simple MLPs distilled from trained GNN teachers perform comparably to advanced GNNs on node classification [37]. This suggests that the graph's topological structure can be effectively integrated into node-level features as prior information. In other words, promising node representations can accurately recover the graph's topological structure.  
In summary, node representations are obtained by synthesizing multiple continuous latent spaces and can recover the graph's structure. To achieve node-level representations from different latent spaces, we propose a series-encoder to generate various outputs. The series-encoder consists of a series of encoders, where each output is isomorphic to the adjacency matrix under the same graph. Because of the transductive nature of isomorphism, all outputs are mutually isomorphic. Therefore, mutual isomorphism is an inherent natural property of the series-encoder, and can serve as the pretext task.
Evaluation of the Mineral Manganese OXMN009 and OXMN009P in the Chemical Looping Combustion (CLC) Process Using Thermogravimetry  
[PERSON]  
1 University of Guayaquil, Guayaquil 090514, Ecuador11 University of Guayaquil  
solid oxygen carrier, usually a metal oxide. This conveyor constantly circulates between two interconnected fluidized bed reactors, a reduction reactor (RR) and an oxidation reactor (RO). This arrangement prevents direct contact between the fuel and air during combustion [1].  
Compared to conventional fossil fuel combustion technologies, the CLC process has the potential to reduce greenhouse gas emissions and improve air quality significantly (see Figure 1).  
CLC transfers oxygen from the air to fuel via an oxygen transporter to prevent contact between the air and fuel.  
Oxygen transport is achieved by continuously circulating the oxygen transporter between two interconnected fluidized bed reactors (fuel and air). Fuel reactors oxidize fuel to carbon dioxide (CO\({}_{2}\)) and water vapor (H\({}_{2}\)O) primarily by reducing metal oxides.  
Traditionally, the CLC process has been widely studied for the combustion of gaseous fuels. However, there has been significant interest in exploring the CLC process for burning solid fuels, such as coal, due to their abundance and low cost in the last decade [2]. Not only is the combustion of coal through the CLC process attractive, but there is also the possibility of using other solid fuels, such as biomass, due to its potential to mitigate associated CO\({}_{2}\) emissions [3; 4; 5].  
There are two approaches to approaching the CLC process with solid fuels. The first involves pre-gasifying the coal using oxygen and water vapor to produce syngas, similar to the first stage of pre-combustion capture technology ([PERSON] et al., 2010) [6]. The resulting synthesis gas is introduced as fuel into the reduction reactor, following an approach similar to the CLC process with gases. Within this strategy of direct coal feeding into the reduction reactor, two options have been proposed: in situ coal gasification (iG-CLC) and combustion with oxygen decoupling in the gas phase (CLOU) [6].  
Decoupling gaseous oxygen with coal, commonly known as chemical looping with oxygen uncoupling (CLOU), is characterized by using an oxygen carrier to release gaseous oxygen. This allows the direct combustion of coal without requiring a prior gasification stage.