esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Remote Sensing Data  
In this study, we selected about 50 S2 and L8 images, acquired over the Lagoon of Venice in the period 2017-2020, for validation activities and match-up analysis of water turbidity. S2-A and -B satellite images (Level-1C), processed by ESA (European Space Agency) within the European Union's Copernicus programme, were downloaded from the Copernicus Open Access Hub ([[https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)]([https://scihub.copernicus.eu/](https://scihub.copernicus.eu/)) accessed on 12 July 2022), and L8 images were downloaded from the United States Geological Survey (USGS) archive of the Earth Resources Observation and Science (EROS) Center ([[http://glovis.usgs.gov](http://glovis.usgs.gov)]([http://glovis.usgs.gov](http://glovis.usgs.gov)) accessed on 12 July 2022). Turbidity maps at 10 m resolution for S2 and 30 m resolution for L8 wereobtained, similarly to [35; 42; 43]. The acquired imagery was radiometrically calibrated using sensor-specific vicarious gains provided in [44], in order to create a consistent multi-sensor dataset for monitoring aquatic ecosystems, and atmospherically corrected with ACOLITE [Version 20190326.0], applying the dark spectrum approach [45; 46]. This approach uses multiple dark targets in the subscene to construct a \"dark spectrum\" which is used to estimate the atmospheric path reflectance (\(\rho_{path}\)) according to the best-fitting aerosol model. Furthermore, the ACOLITE-derived water leaving reflectances (\(\rho_{w}(\lambda)\)) were converted into turbidity (\(T\)), expressed as a formazin nephelometric unit (FNU), following [47]:  
\[T=\frac{A_{T}^{\lambda}\rho_{w}(\lambda)}{\left(1-\rho_{w}(\lambda)/C^{\lambda} \right)}\ [\text{FNU}]\]  
where \(A_{T}\) and \(C\) are two wavelength-dependent calibration coefficients. The parameter \(C\) was calibrated using \"standard\" inherent optical properties (IOPs) as described in [48], whereas the \(A_{T}\) coefficient was obtained by a non-linear least-square regression analysis using in situ measurements of \(T\) and \(\rho_{w}\) in various European and South American coastal and estuarine environments [47].  
From all the available datasets, two S2 and one L8 images were selected to thoroughly analyze turbidity patterns for the three-day period considered by this study (15-17 September 2020). Landsat thermal bands were also processed, according to [49], to derive sea surface temperature (SST) and identify peculiar thermal characteristics of the coastal area.
# Three-Dimensional Velocity Field Interpolation Based on Attention Mechanism
###### Abstract  
The establishment of a three-dimensional velocity field is an essential step in seismic exploration, playing a crucial role in understanding complex underground geological structures. Accurate 3D velocity fields are significant for seismic imaging, observation system design, precise positioning of underground geological targets, structural interpretation, and reservoir prediction. Therefore, obtaining an accurate 3D velocity field is a focus and challenge in this field of study. To achieve intelligent interpolation of the 3D velocity field more accurately, we have built a network model based on the attention mechanism, JointA 3 DUnet. Based on the traditional U-Net, we have added triple attention blocks and channel attention blocks to enhance dimension information interaction, while adapting to the different changes of geoscience data in horizontal and vertical directions. Moreover, the network also incorporates dilated convolution to enlarge the receptive field. During the training process, we introduced transfer learning to further enhance the network's performance for interpolation tasks. At the same time, our method is a deep learning interpolation algorithm based on an unsupervised model. It does not require a training set and learns information solely from the input data, automatically interpolating the missing velocity data at the missing positions. We tested our method on both synthetic and real data. The results show that, compared with traditional intelligent interpolation methods, our approach can effectively interpolate the three-dimensional velocity field. The SNR increased to 36.22 dB, and the pointwise relative error decreased to 0.89%.  
three-dimensional interpolation; attention mechanism; transfer learning; dilated convolution +
Footnote †: journal:  
Article
# M2 ER: Multimodal Emotion Recognition Based on Multi-Party Dialogue Scenarios
## 2 Detailed Design
### The Untimodal Extraction Model of M2 ER
#### 2.1.1 Text Modality Preprocessing and Feature Extraction  
We used the Embeddings from Language Model (ELMo) [41] pre-trained model to obtain dynamic word vector features for the text modality. The core of ELMo lies in utilizing a bidirectional Long Short-Term Memory (LSTM) [42] recurrent neural network structure for feature extraction. During training, ELMo leverages the entire input text and considers both forward and backward input sequence information simultaneously to obtain more comprehensive text emotional features. We also adopted BERT [43] to extract semantic information at the sentence level. BERT can be used to extract text emotional features, where the proximity of words in the feature vector space reflects their semantic similarity [44]. The BERT model utilizes the transformer as a feature extractor. When processing a task, BERT first transforms the input text to obtain BERT input representation. Then, the transformer encoder performs computations on the input, then the computed results serve as the input for the next transformer encoder. This process is repeated, resulting in the representation of the entire text.  
In conclusion, in the feature extraction part of text modality, we utilized the pre-trained model of ELMo and BERT to obtain text emotional features from the word-level and semantic-level perspectives, respectively. Finally, the extracted features from both parts were combined to obtain complete text emotional features. The process of text modality feature extraction is illustrated in Figure 3.
# Quantum Image Steganography Schemes for Data Hiding: A Survey
## 3 Quantum Image Steganography Schemes
### Turtle Shell-Based Matrix  
[PERSON] et al. [24] were the first to apply the idea of embedding information using a turtle-shell-based matrix, which constitutes a numerical pattern that consists of turtle shells represented as hexagon shapes containing octal numbers from 0 to 7.  
Inspired by [PERSON] et al., [PERSON] et al. [15] in 2022 implemented quantum steganography by applying the turtle shell matrix with LSB to hide an NEQR grayscale image into an NCBI color carrier image. The research by [PERSON] et al. was the first to use a turtle-shell-based matrix as an embedding scheme for quantum steganography as well as simultaneously implementing other functionalities such as human vision system model (HVS), and LSB into their algorithm.  
To choose the locations for hiding qubits, the algorithm selects various pairs of the cover image's qubits from three RGB color channels (red, green, and blue), where each has a different probability to be chosen depending on how sensitive the HVS is to it. This way, the algorithm will create either (blue, red) or (blue, green) qubit pairs as a guide for hiding. To increase the security and extend the image representation, the algorithm added an error detection code called a parity bit at the end of each binary sequence. After that, it did three qubits, two in the blue channel LSB and one in either green or red channel LSB, using the turtle-shell-based matrix.  
In the first step of the turtle-shell-based matrix scheme, the algorithm divides the carrier image into blocks, where each image block contains an entangled quantum state representing the coordination of each pixel. Then, based on the RGB qubit pair's grayscale value, the qubits will be embedded into the corresponding image blocks following an algorithm that dictates their position. Based on the results from comparing the proposed turtle-shell-based matrix approach's performance to other papers, the method has displayed a significant improvement in security, and achieves better trade-off between image quality and embedding capacity compared to older schemes [15].
## 3 Discussion  
### Polarizability Due to Electronic Transitions and UV-Vis Absorption  
As an initial benchmark for the polar implementation, we report in Figure 1 the results of its application on TDDFT outputs of the benzene molecule computed for increasing values of the number of excited states (\(N\)). The correctness of polar implementation is checked against independent results obtained from frequency-dependent linear response theory (i.e., CPKS equations). The latter approach is implemented in Gaussian09 and computes the polarizability avoiding the sum over states. It can be noted that the convergence of polar results to the CPKS results is smooth over a wide range of photon energies. No appreciable differences are observed when the number of states in the sum of Equation (5) is large enough (in our case \(N=1000\)). The static limit of the polarizability \(\Re[Tr(\alpha^{ee}(0;0))]\) is known to be proportional to the volume occupied by core and valence electrons, both for atoms [52] and molecules [53]. For this reason, to reach convergence with respect to CPKS results, one needs to include enough electronic excitations in the sum over states expansion of \(\alpha^{ee}\), so to make sure that also contributions from core-excitations are present. As expected, convergence is faster when approaching resonance conditions (e.g., close to 7 eV in Figure 1a), for which single transitions dominate the sum over states expression of the polarizability.  
In a further independent check, we have validated polar against the reported values of the complex electric dipole polarizability of \(4A^{\prime}\)-diaminoazobenzene [54]. A direct numerical comparison is difficult in this case due to the different quantum chemistry code used here (Gaussian09) and in [54] (ADF). To approach the computational conditions of [PERSON] et al. [54], we adopted for \(4A^{\prime}\)-diaminoazobenzene the PBE functional and the cc-pVTZ basis set. Our results, reported in Figure 2, nicely compare with those reported in Figure 3b of Ref. [54]: the maximum value of the imaginary part of \(Tr(\alpha^{ee})/3\) is about 1000 bohr\({}^{3}\) and the maximum value of the real part of \(Tr(\alpha^{ee})/3\)  
Figure 1: The real part of the isotropic invariant of the electric dipole polarizability of benzene (\(\frac{1}{3}\Re[Tr(\alpha^{ee})]\)) computed by polar from a time-dependent density functional theory(TDDFT) calculation at B3 LYP/6-31G(d,p) level: (**a**) far from the resonance range; and (**b**) close to the resonance range. The dots in both panels have been computed by the CPKS scheme available in Gaussian09 for applied electric fields at given frequencies. The results from the sum over states implemented by polar (Equation (5)) are shown with solid lines for increasing values of the number of states \(N\) included in the sum. The damping used in the polar calculations is \(\Gamma=0.001\) eV.  
is about 600 bohr\({}^{3}\). We also note that the shapes and linewidths of both the real and imaginary parts of the isotropic electric polarizability in Figure 2 are very close to those reported in Figure 3b of Ref. [54].
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
### Analysis of Planet Gear Vibration Displacements
#### 4.2.2 Vibration Displacements of Planet gears under Different Input Torques  
The vibration displacements of the planet gears are subject to variation when there are changes in the input torque. To investigate the vibration displacements of the planet gears in response to varying input torques, five operating conditions were set, as shown in Table 2. For each of the five operating conditions, 80 meshing cycles were selected, and the displacements of the planet gears were analyzed. The time-domain responses are shown in Figures 15 and 16. The peak-to-peak values and variances of the vibration displacements are shown in Figures 17 and 18, respectively.  
Figure 16: Time-domain responses of the planet gear displacements in the differential PGT under the different input torques: (**a**) \(x\)-direction; (**b**) \(y\)-direction; (**c**) \(z\)-direction; (**d**) torsional direction around \(z\)-axis.  
Figure 17: Peak-to-peak displacements of the planet gears under the different input torques: (**a**) \(x\)-direction; (**b**) \(y\)-direction; (**c**) \(z\)-direction; (**d**) torsional direction around \(z\)-axis.  
From Figures 15 and 16, it is observable that as the torque increases, except in the \(x\)-direction, the average displacements of the planet gears in various directions basically tended to increase. The displacements of the planet gears, \(m\), in the \(x\)-direction are characterized by their distinctiveness due to their exceptionally small magnitude. Under the five operating conditions, the maximum magnitude remained at around \(10^{-16}\) m. Therefore, even slight variations in parameters can lead to noticeable changes in its displacement. However, because of its small amplitude, this vibration can be practically negligible. Figures 17 and 18 demonstrate that the peak-to-peak displacements and variances in the planet gears exhibited no discernible patterns in the \(x\)-direction. The peak-to-peak displacements and variances in the planet gear, \(m\), in the \(y\)-direction exhibited a decrease as the torque increased, suggesting a reduction in the vibration amplitude. Conversely, the trend was opposite for the planet gear, \(p\). In the axial and torsional directions, both the peak-to-peak displacements and variances in the planet gears, \(m\) and \(p\), increased with increasing torque, indicating that the vibration became more pronounced in these directions as the torque increased.
## 4 Applicability on Performance-Constrained Platforms
### Smart Cards  
Smart cards, which form the basis for contactless payments, present a challenge for the implementation of post-quantum (PQ) algorithms, especially digital signature algorithms. Compared to the ARM Cortex M4 platform, they have significantly less memory (four times less) and a slower processor (1.7 times slower). When compared with x86-64 platforms, they have 170,000 times less memory and a processor that is 40 times slower. The limited processor speed is further constrained by the slow communication speeds supported (below 100 kB/s) [61].  
Currently, smart cards have hardware blocks implemented for random number generation, AES encryption (supporting one AES block), and ECC/RSA signatures (enabling parallel execution and speeds up to 10 times higher than software implementations). As modern smart cards do not have dedicated hardware for executing PQ digital signatures, and the process of implementation and certification is lengthy (6 to 18 months for certification alone), consideration is being given to how PQ algorithms can be executed on the current versions of smart cards.  
During algorithm implementation, it is essential to employ masked algorithms to achieve protection against side-channel attacks. Masked algorithms generally require longer execution times and consume more memory (for example, the execution time for Dilithium increases by 5.6 times) [62].  
In conclusion, only Dilithium fits within the constraints of smart cards. Falcon requires too much RAM and too much time for key generation. Sphincs+ has an issue with the size of the signature, which can be partly mitigated by hash-based accelerators for embedded devices but not for smart cards [63].  
For example, the authors of [64] introduced a variant of Dilithium known as DiLizium, which has the potential to replace distributed RSA and ECDSA signature schemes in authentication contexts, including applications like Smart-ID.
## 5 Comparative Study of Optimization Strategies
### Case 1: Planar Workpiece
Figure 10: Residual error of the concerned surface region polished along: (**a**,**b**) the unoptimized path; (**c**,**d**) the RMS-map-based optimized path; and (**e**,**f**) the PV-map-based optimized path. at the four edge corners, respectively. The reason for the abrupt change of PV value in the residual error region corresponding to A13 is related to the center effect produced by the spiral path polishing. The sudden change in PV value at the four edge corners may be related to a 90 degree change in the polishing direction of the planned polishing path at the four corner points. Regardless of the above outlier points, it can be seen that the residual errors obtained by using the PV-map-based optimization model have smaller and closer PV values. In addition, it can be seen that the RMS-map-based optimization model improves the spread of PV values over the sub-regions of residual error. As shown in Figure 6b,d, the ideal RMS and PV values for the two optimized models are RMS = 0.0097342 \(\lambda\) and PV = 0.013193 \(\lambda\), respectively. However, we found that RMS and PV values obtained after optimization are about 20% larger than the target values. This may be related to the fact that the bulk material removal depth varies in the adjacent path interval caused by varying surface form error, unlike the constant bulk material removal depth in the experiment in Section 3. Power spectral density analysis was performed on the residual errors obtained by the above three polishing paths, and the results along the \(x\)-direction and \(y\)-direction are, respectively, shown in Figure 12a,b. It can be seen from the PSD curves that whether the path optimization model is based on the RMS or PV map, the amplitude variation of the power spectral density of the mid-spatial frequency components in the residual error obtained by the optimized path is more stable. It can be concluded that the proposed optimization strategy can effectively smooth the distribution of MSF error components in the residual error.
## 2 Classification of Electromagnetic Surfaces
### Single-Layer and Multiple-Layer  
When talking about two-dimensional surfaces, we usually refer to single-layer surfaces where \(L_{z}\) is much smaller than \(\lambda\), as shown in Figure 7a. However, many desirable properties are realized by cascading multiple layers together like Figure 7b. For instance, multiple-layer surfaces can be utilized to improve bandwidth performance of FSS [3]. Four-layer structures are used to realize a 360\({}^{\circ}\) transmission phase range with magnitude larger than -1 dB for FSS-type transmitarray antennas [23]. One feasible solution to problems of multiple-layer structures is to determine surface susceptibilities of each layer and characterize whole structures using equivalent circuit model [24]. Multiple-layer surfaces with a ground plane as bottom layer are called impenetrable surfaces [25], such like EBG and reflectarray antenna [26]. As their name implies, there is no field components below the ground plane of impenetrable surfaces. Conversely, surfaces without bottom ground plane are named as penetrable surfaces, which usually have non-zero transmission like FSS and transmitarray antenna.  
Figure 5: Electromagnetic surfaces consisting of (**a**) isolated scatterers (**b**) isolated apertures.  
Figure 6: Electromagnetic surfaces which are (**a**) isotropic (**b**) anisotropic.
## 3 Traffic Multi-Maps Generation Based on Disjoint Path Flows
### TWM Weights Generation Based on Dj-ksp
The complete TWM generation is described in Algorithm 1. When the routing agent uses the TWM map \(\mu_{i}^{d}\) based on the path flow \(r_{i}^{d}\), the route costs are calculated with the new edge weights. The occupancy/capacity link-cost model described by (3) evolves to (23). \[t_{e}^{TWM}(x_{e})=\beta_{e}^{d,i}*(1+\alpha\frac{x_{e}}{W_{e}})^{\beta} \tag{23}\]  
It is also possible to define an obvious \(Y_{Toll}\) strategy that adds/removes tolls to the generalized cost function in (7). \begin{table}
\begin{tabular}{c c c c c} \hline
**Flow** & **Sub-Flow** & **Path Flow**\(r_{i}^{d}\) & \(c_{i}^{d}\) & \(\alpha_{m}\) \\ \hline \multirow{4}{*}{\(x_{1}\)} & \(x_{11}\) & \(r_{11}\) & 1 & 0.5 \\ \cline{2-5}  & \(x_{12}\) & \(r_{12}\) & 3 & 0.17 \\ \cline{2-5}  & \(x_{13}\) & \(r_{13}\) & 5 & 0.10 \\ \cline{2-5}  & \(x_{14}\) & \(r_{14}\) & 7 & 0.07 \\ \hline \multirow{4}{*}{\(x_{2}\)} & \(x_{21}\) & \(r_{21}\) & 2 & 0.5 \\ \cline{2-5}  & \(x_{22}\) & \(r_{22}\) & 4 & 0.25 \\ \cline{2-5}  & \(x_{23}\) & \(r_{23}\) & 6 & 0.17 \\ \cline{2-5}  & \(x_{24}\) & \(r_{24}\) & 8 & 0.13 \\ \hline \multirow{2}{*}{\(x_{3}\)} & \(x_{31}\) & \(r_{31}\) & 5 & 0.5 \\ \cline{2-5}  & \(x_{32}\) & \(r_{32}\) & 10 & 0.25 \\ \hline \end{tabular}
\end{table} 
Table 2: Example of \(\alpha_{m}\) values based on their path cost with \(\alpha^{e}=0.5\) factor. ```
0: traffic network \(\Theta\), commodities \(D\), shortest paths algorithm \(\delta\), max SP \(K\), disjoint level threshold \(\phi\), disjoint level criteria \(Disj_{n}\) and \(DjERC_{n}\) as \(C_{1}\) and \(C_{2}\), alfa factors \(\alpha^{c}\) and \(\alpha^{R}\), edge proximity radius \(R\)
0: TWM maps \(\left[\mu_{i}^{d}\right]\) as \(twm\)
1:\(twm\leftarrow()\)
2:for all(d in D)do [iterate over commodities]
3: {Get Dj-kSP and their free-flow costs}
4: {Use ESX-C or similar algorithm}
5: \((r_{i}^{d}),(ffc_{i}^{d})\gets get\_DjKSP(\Theta,d,\delta,K,\phi,C_{1},C_{2})\)
6:\(nr\leftarrow size((r_{i}^{d}))\)
7:\(\mu\leftarrow array(nr,\Theta)\) [allocate maps]
8:for all(r in \((r_{i}^{d})\))do [weight the maps]
9:\(\mu(r)\leftarrow\Theta\) [assign the initial network]
10:\(\alpha_{m}\leftarrow\alpha^{c}*\frac{min(ffc_{i}^{d})}{ffc_{i}^{d}}\)
11:for all(\(\epsilon\in n\))do [assign weights to path edges]
12:\(\mu(r)(\epsilon).weight\leftarrow\alpha_{m}*\Theta(\epsilon).weight\)
13:endfor
14:\(rEdges\gets get\_surroundingEdges(\Theta,r,R)\)
15:for all(\(\epsilon\) in \(rEdges\))do [assign weights to the R-closest edges]
16:\(\mu(r)(\epsilon).weight\leftarrow\alpha_{m}*\alpha^{R}*\Theta(\epsilon).weight\)
17:endfor
18:endfor
19:\(twm\leftarrow push(twm,\mu^{d})\)
20:endfor
```  
**Algorithm 1** TWM generation algorithm based on Dj-kSP  
Figure 3: Synthetic traffic network showing how TWM areas are selected for two non-overlapping path flows between origin and destination with a distance radius R = 2 (created with Matlab R23A [71]).
## 2 Mobility Models in FANETs
### Path Planned Mobility Models
#### 2.4.1 Flight Plan  
Flight Plan (FP) mobility model [66] is a path-planned model that defines a flight UAV plan in a special file for mobility. It is then used to make a time-dependent network topology map, as shown in Figure 16. Whenever the present flight plan and the original flight plan vary, the latter gets modified. Moreover, FP is commonly used for tactical mission and aerial transportation operations, where the whole flight trajectory is planned before starting a mission. It has been widely used in data collection from the sensor to UAVs [67]. Further, it can be adopted in semantic-aware aircraft trajectory prediction [68]. Figure 16 illustrates the approach.