esa-sceva/satcom-chunk-collection huggingface dataset:
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### FAS with RGB Camera
#### 3.1.2 FAS Based on Traditional DL  
Early training of DNN in FAS struggled to outperform traditional handcrafted methods due to the need for large-scale data. However, advancements in [PERSON] like U-Net [75] and DenseNet [76], along with large-scale datasets such as CelebA-spood [77] and HiFiMaskt [27], have propelled end-to-end DL methods to dominate the FAS field. Unlike hybrid methods that integrate partially handcrafted features and have no learnable parameters, traditional end-to-end DL methods directly learn the mapping function from facial input to spoof detection. During model training, deep models can use binary loss supervision, pixel-level supervision, pseudo-depth labels, binary mask labels, and other labels to distinguish between real and spoof faces. In addition to using spatial information for facial spoof detection, temporal sequences between video frames can also be used to extract spoofing features. Based on the type or source of the spoofing cues, we categorize traditional DL-based FAS methods into five types: binary supervision methods, depth map methods, binary mask methods, spatiotemporal information fusion methods, and spoof trace separation methods. Below, we provide a detailed explanation of these methods.
## 3 Results
### Second Step Validation
#### 3.2.2 Experiments  
When sensor fusion is applied to develop models, it is necessary to calibrate them intrinsically and extrinsically. This sensor fusion will allow the device to capture images and point cloud information at the same time, accurately and reliably. KITTI has its own calibration matrix for this purpose; however, for our new approach with a configuration different from KITTI, it is required to perform our own calibrations using a custom chessboard calibration system. The calibration procedure and results can be seen in Figure 8, in the same location described in Section 3.2.1.  
The results obtained in this real-life scenario experiment are very similar to the results presented by the models in Table 1. As can be seen in a previous comparative (Table 3), in terms of average precision, the results are close to those of the corresponding model with auto-labeled KITTI as a dataset. In this way, it is shown that the model is able to replicate the results in a very similar way.  
In the best of the results obtained by the Voxel R-CNN model, an accuracy greater than 88% in BEV and in 3D detection greater than 82% can be seen. This shows good results for vehicle detection, which could be improved by optimizing the networks used.
## 4 Discussion
### SOS/EOS Response to Rainfall and Temperature  
Previous studies, such as [PERSON] et al. [11], have used the regression coefficient from multiple linear regression between seasonal parameters and climatic variables to explain the relative influence of rainfall and temperature on the variability of SOS/EOS. In this study, we used the linear mixed model to analyse SOS/EOS response to rainfall and temperature. The mixed model has the advantage of producing estimates of both random and fixed effects, which allowed us to investigate the effects of climate factors while accounting for inter-pixel heterogeneity. Furthermore, it is not hindered by missing data [95]. The best-supported models demonstrate that temperature is a significant predictor of rubber phenology, being negatively related to SOS/EOS.  
Despite the heterogeneity of the SOS/EOS values in the study area, a decreasing trend with an increasing temperature was identified across all pixels. This trend indicates an increase of 1 \({}^{\circ}\)C of temperature advanced the SOS/EOS by approximately 25 days and 14 days, respectively. Temperature is one of the main factors that affect the rate of plant development [96; 97]. Higher temperatures accelerate phenological progression [98]. It can be explained by the fact that the temperature changes affect most biochemical processes of ectotherms, such as reaction kinetics, enzyme inactivation, and hormonal regulation [97]. This has been demonstrated by this study where SOS and EOS were seen to occur earlier with warmer temperatures.  
In this study, the temperature was found to be a key factor in rubber phenology. However, when comparing the results to several crops and regions, the determinant factor of phenology varies considerably. Table 4 shows a comparison of the findings from other related studies. For example, two of the previous studies [99; 100] conducted on forests found rainfall to be an influential factor in forest phenology, while another [101] showed both rainfall and temperature to be important factors.  
The studies of phenology in Alpine regions [33; 102], African savanna [104], European and South Korean vegetation [92; 97] have similar findings to this study. More importantly, our result also agrees with previous studies carried out on rubber plantations. Two ground studies also discovered temperature as a critical driver of rubber phenology, either as the only factor [9] or with sunshine hour [5]. [PERSON] [42] stated that both rainfall and temperature are important climatic factors for rubber phenology. However, he also mentioned that temperature was the most influential factor, particularly in rubber refoliation. The temperature is not only an important key indicator for rubber phenology but also for rubber yield. In their study, [PERSON] et al. [106] discovered temperature to be the only significant predictor for rubber yield compared to other climatic parameters such as rainfall, relative humidity, and vapour pressure deficit.
## 5 Experiments and Results
### Reconstruction Evaluation
#### 5.1.1 Three-Dimensional Reconstruction Model--Lytro Illum  
To estimate depth with Lytro Illum cameras, we followed one of the most popular approaches in current research: the use of cost volumes [35; 36]. A cost volume is a volumetric structure in which each layer represents the cost of assigning the pixels of the image to a given depth. The lower the cost, the more likely the pixel is to be at that same depth. In a way, one can set the cost volume \(C\) to  
\[C_{x,y,z}=\frac{1}{p(x,y,z)} \tag{3}\]  
where \(p\) is an estimate of the probability of a coordinate in the image \(\{x,y\}\) being at a given depth \(z\). The advantage of using a cost volume instead of a \"probability volume\" is associated with the fact that depth estimation can be treated as a minimization problem.  
For a given image, we generated the cost volume using the method [35] and its method of post-processing the volume. Then, we applied our roughness restriction to further refine the results. In this method, given an infinite-dimension \(C\) cost volume on the depth axis, \(z\), the \(l\) depth is estimated by selecting the lowest cost layer for each pixel:  
Figure 11: Resulting depth map after roughness restriction [35] and cost volume refinement [36].  
Figure 10: Representation of a cost volume, where light colors represent less probability that the points are at this depth.  
\[l_{x,y}=argmin_{z}Cx,y,z \tag{4}\]  
The roughness constraint is defined as a modification in the depth solution:  
\[l^{\prime}_{x,y}=argmin_{z}C^{\prime}x,y,z=argmin_{z}Cx,y,z+\lambda R(x,y,z) \tag{5}\]  
where \(\lambda\) is a number in the range \([0,1]\) and \(R(x,y,z)\) is the constraint function. The restriction encourages each pixel to be placed near its neighbors. Given a kernel of neighborhood \(N\) and size \(n\), the roughness restriction is given by  
\[R(x,y,z)=\sum_{i=1}^{n}G\left(d_{(x,y)+N_{i}},z\right) \tag{6}\]  
where \(G\) is the incentive function for a given depth \(t\). Using inverted Gaussian distributions, \(G\) is defined as  
\[G(t,z)=1-e^{-\frac{(z-t)^{2}}{8}} \tag{7}\]  
Finally, the depth of the image was reconstructed from the final finite volume, using an existing common method--approaching parabolas. The lowest cost layer for each pixel was found, \(l_{x,y}\), and then it was approached as a second degree polynomial at the cost of the \(l_{x,y}\) layer and its neighboring layers \(l_{x,y+1}\) and \(l_{x,y-1}\). Finally, the depth of the pixel \(d_{x,y}\) was set as the minimum of the polynomial. This allowed solutions to be found at points between layers, resulting in smoother and more accurate predictions.  
Additionally, some cost volume refinement could be performed by applying the method of [36] in order to improve the depth results. As mentioned by the authors, the refinement in a prior stage (cost volume) is beneficial in terms of final accuracy over the refinement of the depth map.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 3 SCADA Data Processing for for xLSTM
### Hyperparameter Tuning for xLSTM
#### 3.2.2 Hyperparameter Optimization for mLSTM Configuration  
Hyperparameter optimization of the mLSTM model, as listed in Table 4, centers on enhancing the matrix memory capabilities and covariance update mechanisms. Tuning focuses on parameters that govern the interaction between query-key-value transformations and the matrix memory structure, which directly impacts the mLSTM model's capacity to store and retrieve temporal dependencies in wind turbine operational data. Essential parameters include the matrix memory dimension, which determines the storage capacity for temporal patterns, and the projection factor that influences the mLSTM model's ability to capture complex relationships in high-dimensional spaces. The configuration of these parameters significantly affects the mLSTM model's ability to process parallel sequences and maintain effective information flow through the covariance update rule.  
For the mLSTM configuration detailed in Table 4, hyperparameter sensitivity was most pronounced in the matrix memory dimension and projection factor parameters. The matrix memory dimension of 128 was selected after evaluating values between 64 and 256, with performance plateauing above 128 while computational requirements increased  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Input Size & 1 & Dimension of input features \\ Head Size & 32 & Dimension per attention head \\ Number of Heads & 4 & Parallel attention mechanisms \\ Projection Factor & 4/3 & Feed-forward network expansion ratio \\ Forget Gate Init & [3; 6] & Initialization range for forget gates \\ Batch Size & 32 & Training batch size \\ Learning Rate & 0.01 & Initial learning rate for optimization \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameter Configuration for sLSTM.  
quadratically. Unlike sLSTM, the projection factor parameter of mLSTM was optimized at 2.0 within the range of \([1.5,2.0,2.5,3.0]\). The projection factors below 2.0 limited representational capacity (decreasing \(R^{2}\) by 3-5%), while higher values led to parameter redundancy without significant accuracy improvements. The selected batch size of 32 maximizes GPU memory utilization efficiency with the specified matrix memory dimensions, achieving near-optimal throughput (96% of the theoretical maximum) on the employed hardware configuration. This comprehensive parameter sweep enabled the identification of configuration values that maximize the complementary strengths of both sLSTM and mLSTM components within the unified xLSTM architecture.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### Quality Level 5 (Best) SSTs  
The differences in SST between SLSTR-A and SLSTR-B for all valid pixels of excellent quality (GHRSST quality level of 5) for the September data are closely matched with a mean bias of \(-0.014\) K and a standard deviation of \(0.119\) K. Figure 7 shows the differences separated out by retrieval algorithm (N2, N3, D2 and D3) and small differences between them can be seen at the sub 0.1 K level (see Table 3). In part, these small differences are likely due to the use of incorrect retrieval coefficients for S3B, where currently the S3A coefficients are being used for S3B data, as the S3B specific coefficients are yet to be derived and applied within the operational SLSTR processing. Any bias caused by the missing SST coefficients can be reduced further by applying the SSES bias term (see below).  
The fact that such small differences in the data are observable using the tandem data does, however, highlight the level of detail it is possible to attain. Because of the issue with the current SLSTR-B SST retrieval coefficients, however, care must be taken in interpreting the data as some of the variance will, in part, be due to using incorrect retrieval coefficients rather than showing true differences between the sensors and/or the errors caused by small timing/geolocation differences. Once the SLSTR-B retrieval coefficients have been properly calculated, the tandem data will need to be reassessed so the current work must be considered to be very preliminary.  
Figure 7: SST differences for different retrieval algorithms (N2—Orange, N3—Green, D2—Brown, D3—Purple) for excellent (QL5) cases and a 30 s time difference.
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results  
This section presents the evaluation of our proposed DYNASTI solution. The evaluation was carried out by simulations to demonstrate the benefits of the proposal and the improvement of flexibility and dynamism levels when using multiple instances. Each result in the graphs is the averaging or summing of over 20 runs. The error bar was included in every graph using confidence interval based on confidence level of 95%.  
We assessed (i) the overhead of the DIO and DAO in order to measure the impact of the control message interruption, (ii) the end-to-end delay for successfully received messages in the sink, assessing the impact they can have on different numbers of nodes and applications, (iii) the energy consumption which measures the total energy spent by the sensor nodes, and (iv) lost packets.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 3 Developments Related to the Extraction of Soil Properties from Remote Sensing Data
### Remote Sensing of Soil Properties as Training Data for Digital Soil Mapping  
Similar to laboratory measurements, RS predictions can be used as an \"s\" factor in the SCORPAN model (i.e., they are used as if they were \"measurements\" of the variable of interest). In this case, the main challenge is to incorporate the uncertainties associated with these inputs into the SCORPAN model. Note that this issue also applies to laboratory measurements, although it can be assumed that they are generally more accurate than those derived from RS data. Another important limitation of using RS data as direct inputs to calibrate DSM models is that soil reflectance only captures information about the surface layer, while soils are 3D objects. Only radar or gamma-ray spectrometry techniques are capable of observing a soil at depth.  
If the prediction does not include a spatial component, and if there are a sufficient number of validation points, an easy and often-used way to verify the absence of a spatial component is to perform geostatistical analyses on the prediction residuals. If there is a spatial structure on the residuals, it is possible to improve the predictions by kriging or find that this trend might be related to another covariate that could be incorporated in a SCORPAN model.  
It is important to keep in mind the consequences of uncertainty if these predictions are not only used to predict a value at a given time, but also intend to monitor changes in a soil characteristic. First, cumulative errors at two dates will affect the minimum detectable change (i.e., [238]). Second, it is debatable whether it is better to map soil attribute values at two dates and then infer changes from their differences, or whether it is better to directly map the changes themselves. Both from a spatial and soil process point of view, the spatial structures and controlling factors of a soil attribute at two different dates may not be the same as the changes between two dates. It should be noted that the above remarks are relevant to both for the \"direct RS detection of soil properties\" and DSM.  
Figure 4: Evolution of the cumulative number of articles per soil threat.
# THz Range Low-Noise SIS Receivers for Space and Ground-Based Radio Astronomy +
## 2 Materials and Methods
### Nb/AlO\({}_{x}\)/Nb and Nb/AlN/NbN SIS Mixers
For the Nb/AlN/NbN junctions with 0.5 \(\upmu\)m\({}^{2}\) area fabricated on an Si substrate, a gap voltage \(V_{g}\) = 3.5 mV was measured. In contrast, the Nb/AlN/NbN junctions with thin (100 nm) Nb electrodes fabricated on the NbTiN film have \(V_{g}\) = 3.22 mV and quality \(R_{gg}/R_{n}\) = 20 for \(R_{n}A\) = 8.5 \(\Omega\cdot\upmu\)m\({}^{2}\) (current density is of approximately 30 kA/cm\({}^{2}\)), see Figure 2.  
To design a low-noise THz-mixer, the capacitance of an SIS junction should be tuned out by specially-designed inductive elements; for that, the junction capacitance should be determined with high accuracy, i.e., the main parameters of the tunnel barrier (average barrier height \(\varphi\) and thickness \(d\)) must be evaluated. We determined the tunnel barrier thickness and its average height from the IVCs at high voltages [43] as proposed by [PERSON] [44] and as generalized by [PERSON] for tunnel junctions in the limit of low bias voltages [45]. The main parameters of the tunneling barrier of the Nb/AlOx/Nb and Nb/AlN/Nb junctions are estimated in a wide range of current densities, see Figure 3. A decrease in the height of the tunnel barrier of the junction with the AlN barrier by 0.3 eV in comparison with the oxide junction makes it possible to realize SIS junctions with a current  
Figure 2: IVC of the Nb/AlN/NbN mixer element inserted in the NbTiN/Al stripline, fabricated on quartz substrate for APEX telescope (area of tunnel junction \(A\) = 0.5 \(\upmu\)m\({}^{2}\), \(V_{g}\) = 3.22 mV, \(R_{n}S\) = 8.5 \(\Omega\)\(\upmu\)m\({}^{2}\), \(J_{g}\) of approximately 30 kA/cm\({}^{2}\)); the Josephson supercurrent is suppressed by the magnetic field (black line). IVC of the SIS mixer pumped by a CW LO source delivering approximately 0.5 \(\upmu\)W of power to the SIS junction at 950 GHz is shown by the red line. A cross-section of the mixer element incorporated in a NbTiN/Al microstrip line is presented in the inset. density of more than 15 kA/cm\({}^{2}\) with an insulating layer thickness of approximately 1 nm that is allowable technologically, which makes it possible to obtain a quality parameter \(R_{sq}/R_{n}\) as high as 25, even for high current density (\(R_{n}A<10\)\(\Omega\cdot\upmu\)m\({}^{2}\)).
## 4 Conclusions  
Due to the complexity of the reservoir sedimentation process and the immaturity of the sediment research, the calculation of the reservoir sediment erosion and deposition has relied on complicated differential equations with abundant empirical parameters. Since the BP-ANN model has a strong ability to deal with complex non-linear mapping with simpler calculations, it is adopted in order to study the relationships between reservoir flushing sediment and its influencing factors. Four major factors impacting the reservoir sediment flushing efficiency are determined by the analysis on 1D unsteady flow and sediment mathematical model, which are reservoir inflow, water level, outflow, and the inflow sediment concentration. The observed data of the Three Gorges Reservoir from 2003 when it was put into operation to 2010 are used to train the BP-ANN model, and the data of the year 2011 is used for testing. The results show that the established BP-ANN model is able to reflect the essential relationship between the sediment flushing and its influencing factors, especially in flood season, when the majority of the sediment inflows. To improve the accuracy of the model, more observed data is needed in the future.  
This research was supported by the National Basic Research 973 Program of China (Grant No. 2012 CB417002), the National Natural Science Foundation of China (Grant No. 51409248), and the Open Research Fund of State Key Laboratory of Simulation and Regulation of Water Cycle in the River Basin, China Institute of Water Resources and Hydropower Research (Grant No.WHR-SKL-201409).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} TGP & Three Gorges Reservoir \\ ANN & Artificial Neural Networks \\ BP & Back Propagation \\ MLP & Multiple Layer Perception \\
1D/2D & 1-dimensional/2-dimensional \\ \end{tabular}  
Figure 6: Error histogram with 20 bins of the trained and tested data.
# A Coarse-Grained Molecular Model for Simulating Self-Healing of Bitumen
###### Abstract
For instance, [PERSON] et al. [13] proposed a coarse-grained (CG) potential for polycyclic aromatic hydrocarbons, while [PERSON] et al. [14], an energy-based CG potential for carbon and silicon nanomaterials. Based on experimental thermodynamic data, [PERSON] et al. [15] used the GROMOS 45A3 force field for biomolecules to parameterize a CG potential for liquid alkanes. [PERSON] et al. [16] developed a transferable CG hydrocarbon model by combining MD with particle swarm optimization. [PERSON] et al. [17] used the square gradient theory to describe the change in interfacial tension with pressure and molecular chain length and the effect of nitrogen interfacial adsorption on interfacial tension for four binary systems composed of nitrogen pressurized n-alkanes (n-pentane, n-hexane, n-heptane, and n-octane). [PERSON] et al. [18] applied the CG force field developed by the statistical correlation fluid theory to the asphaltene model and carried out MD simulations to verify the CG representation of a benchmark system of 27 asphaltenes in pure solvents (toluene or heptane). [PERSON] et al. [19] improved the CG Martini force field through the Flory-Huggins theory to analyze the aggregation of bitumen molecules. However, CGMD is not frequently employed in bitumen research; to apply other force fields, such as Martini to bitumen systems, some adjustments need to be made for the specificity of the bitumen molecular structure and there is no unique CG force field for bitumen systems. We developed a CG bitumen model and used it to simulate crack healing for the first time. The CG bitumen model is about 112 times larger than the MD model we previously used [8]. The crack's volume is roughly 481 times bigger than that of the MD model. In this way, the simulated crack width approaches the actual crack width.