esa-sceva/satcom-chunk-collection huggingface dataset:
# Searchable Data Vault: Encrypted Queries in Secure Distributed Cloud Storage
## 2 Related Work
### Multi-Server SSE Scheme  
A multi-server SSE scheme consists of three algorithms, namely Setup, Search and Update.  
We note that this is similar to a single server SSE scheme, for example, as proposed in [19; 23; 26]. The only difference is the process of division/combination of documents and distribution over multiple storage servers within each algorithm.  
Setup. The algorithm partitions a document \(D_{i}\) into a list of blocks \(B\) and then encrypts and assigns an identifier \(id(.)\) and storage location to every block. Input to this algorithm is the set of documents \(D\) and a keyword index table which map each document \(D_{i}\) to a list of keywords. Output of this algorithm is a set of cryptographic keys \(K\) to be stored securely by the user, index \(I\) and sets of encrypted blocks \(C\).  
Search. This is an interactive protocol initiated by the user which involves the storage servers. The user provides a keyword as input. The protocol takes the cryptographic keys \(K\) generated from Setup to produce a search token _tok_. The token is used to extract the list of blocks and their locations from the index \(I\). Next, the listed blocks are retrieved from the respective storage servers and returned to the user. Finally, the user decrypts the encrypted blocks and reconstructs the documents.  
Update. This is the algorithm for adding (Doc+) or removing (Doc-) documents in the storage servers. The input to the algorithm is an object (e.g., a document \(D_{i}\)), and the operation to be executed on the object. Output of this algorithm is an updated index, and a set of encrypted blocks (for document addition). It can also be extended for adding or removing storage servers from the system.  
The scheme is correct if for any set of documents and a keyword index table, the set of index entries and encrypted documents (or blocks) resulting from Setup and Update would consequently allow Search to identify documents associated to the searched keyword exactly as listed in the initial keyword index table.
# A Cumulant-Based Method for Acquiring GNSS Signals
## 4 Simulation and Experimental Results
### Simulated Signal Acquisition
This represents a 60% improvement in detection probability under challenging multipath conditions.
# Kolmogorov-Arnold Finance-Informed Neural Network in Option Pricing
## 5 KAFIN for Option Pricing
### KAFIN for Option Pricing
#### 5.2.1 Financial Governing Loss for Call Option Pricing  
To mathematically model the financial governing loss for call option pricing using the Black-Scholes (BS) model within a neural network framework, we need to ensure that the network's output adheres to the BS partial differential equation (PDE). The loss function should penalize deviations from the BS model, thereby enforcing the financial constraints.  
Let the neural network output for the call option price be \(u_{\theta}(S,t)\), where \(\theta\) denotes the network parameters. The financial governing loss can be constructed by computing the residual of the BS PDE when applied to the network's output. This residual should ideally be zero across the domain of interest. The residual \(\mathcal{R}(S,t)\) is given by  
\[\mathcal{R}(S,t)=\frac{\partial u_{\theta}}{\partial t}+\frac{1}{2}\sigma^{2} S^{2}\frac{\partial^{2}u_{\theta}}{\partial S^{2}}+rS\frac{\partial u_{ \theta}}{\partial S}-ru_{\theta}. \tag{18}\]  
The financial governing loss \(\mathcal{L}_{\text{financial}}\) can then be defined as the mean squared error of the residual over the sampled points \((S_{i},t_{i})\) in the domain:  
\[\mathcal{L}_{\text{financial}}=\frac{1}{N}\sum_{i=1}^{N}\mathcal{R}^{2}(S_{i },t_{i}), \tag{19}\]  
where \(N\) is the number of sample points.  
By integrating this financial governing loss into the overall loss function of the neural network, we ensure that the network's output respects the financial dynamics dictated by the Black-Scholes model.
## 2 Related Work
### Neural Network-Based Complexity Evaluation Methods  
With the rise of deep learning, neural network-based complexity evaluation methods [34; 35] have gradually been applied in the autonomous driving domain. These methods automatically extract multidimensional scene features through neural networks, enabling them to capture complex nonlinear interaction relationships. For example, [PERSON] et al. [36] proposed a model that uses neural networks to extract features of road participants (such as pedestrians, cyclists, and vehicles) from scene images. By constructing a feature matrix and training on manually labeled data, they achieved quantification of driving environment complexity. [PERSON] et al. [37] further designed a complexity perception network based on an attention mechanism. By integrating YOLO-v3 for object detection to generate heatmaps and using convolutional neural networks and self-attention feature extractors, their method performs a comprehensive analysis of video data and vehicle dynamics, significantly enhancing the accuracy of environmental complexity recognition.  
Neural networks have demonstrated exceptional feature extraction capabilities in quantifying complexity in autonomous driving scenarios. They adaptively identify key elements from multidimensional scenes and reduce reliance on expert knowledge. However, the complexity scoring process of neural network models lacks interpretability and is highly data-dependent, with limited generalization ability in extreme scenarios.
# High Reliable Uplink Transmission Methods in GEO-LEO Heterogeneous Satellite Network
## 3 Performance Evaluation
### Outage Probability of Cooperative OMA  
When users move to the beam edge of the LEO satellite, GEO SATCOM network can be used as a complementary solution. In this scenario, the beam-edge user would be served by the GEO satellite with an independent channel. Then, the uplink of beam-center and beam-edge users is viewed as OMA, where the GEO and LEO satellites work cooperatively.  
Since the uplink channels of the beam-center user and the beam-edge user are spectrally independent in cooperative OMA, the definitions of both outage probabilities can be expressed as \(Pr_{1}^{{}^{\prime}}=\mathbb{P}(SNR_{1}^{{}^{\prime}}<T_{0})\) and \(Pr_{2}^{{}^{\prime}}=\mathbb{P}(SNR_{2}^{{}^{\prime}}<T_{0})\), respectively.  
For \(U_{1}\), the outage probability can be further calculated as  
\[\begin{array}{rl}Pr_{1}^{{}^{\prime}}&=\mathbb{P}(SNR_{1}^{{}^{\prime}}<T_{0 })\\ &=\mathbb{P}(|h_{1}|^{2}(1-\alpha)\beta<T_{0})\\ &=\mathbb{P}(|\rho_{1,l}|^{2}<\frac{T_{0}}{(1-\alpha)\beta K_{1}})\\ &=\int_{0}^{\frac{T_{0}}{(1-\alpha)\beta K_{1}}}f_{|\rho_{1,l}|^{2}}(y)dy\\ &=\varepsilon_{1,l}\sum_{k=0}^{\infty}\frac{(m_{1,l})k_{1,l}^{k_{1,l}^{k_{1,l }^{k_{1,l}}}}}{(k_{1}^{2})k_{1,l}^{k_{1,l}^{k_{1,l}}}}\gamma(k+1,\frac{T_{0}}{ (1-\alpha)\beta K_{1}}k_{1,l}).\end{array} \tag{17}\]  
According to (9), the outage probability of \(U_{2}\) can be written as  
\[\begin{array}{rl}Pr_{2}^{{}^{\prime}}&=\mathbb{P}(SNR_{2}^{{}^{\prime}}<T_{0 })\\ &=\mathbb{P}(|g_{2}|^{2}\alpha\beta<T_{0})\\ &=\mathbb{P}(|g_{2}|^{2}<\frac{T_{0}}{\beta})\\ &=\mathbb{P}(|\rho_{2,g}|^{2}<\frac{T_{0}}{\beta^{2}(\delta_{2})C_{2}G_{\alpha }\beta})\\ &=\int_{0}^{\frac{T_{0}}{(2\delta_{2})G_{\alpha}\beta}}f_{|\rho_{2,g}|^{2}}(x) dx.\end{array} \tag{18}\]  
Let \(K_{2}^{{}^{\prime}}=l^{2}(d_{g})G_{2}G_{s}\). (18) becomes  
\[\begin{array}{rl}Pr_{2}^{{}^{\prime}}&=\int_{0}^{\frac{T_{0}}{K_{2}^{{}^{ \prime}}\alpha\beta}}f_{|\rho_{2,g}|^{2}}(x)dx\\ &=\varepsilon_{2,g}\sum_{k=0}^{\infty}\frac{(m_{2,d_{g}})x_{2,g}^{k_{1,g}^{k_ {1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g }}^{k_{1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g}}^{k_{1,g}^{k_{1,g}}^{k_{1,g}^{k_{1,g}}^{k _{1,g}^{k_{1,g}^{k_{1,g}}^{k_{1,g}^{k_{1,g}}^{k_{1,g}^{k_{1,g}^{k_{1,g}^{k_{1,g }}^{k_{1,g}^{k_{1,g}^{k_{1,g}}^{k_{1,g^{k_{1,g}}^{k_{1,g^{k_{1,g}}^{k_{1,g^{k _{1,g}}^{k_{1,g^{k_{1,g}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}\\\\\\ \end{\rightrightright\}\}\}\right\rightright\rightrightrightright\right \right\right\right\right\right\right\right\rightright\]\\ \end{\] ( \tag{19}\]
## Data and Data Preparation
### Mass Balance Data  
Direct mass balance measurements and related ELA/AAR values are available for more than 30 years for the three glaciers HEF, KWF and VNF. The measurements follow the fixed date system defined as the period from 1 October to 30 September in the following year. Over the course of the melting period, the transient snow line roughly coincides with the equilibrium line over the glacier. Towards the end of the mass balance year, however, fresh snow fall could cover the ablation area, or exposed snow from previous years (i.e., firn) could cause a difference between the SLA and the ELA. Because the repeat cycle of Landsat impedes a continuous SLA monitoring, we use the annual maximum SLA of one summer season to approximate the ELA. In addition, we use the minimum SCR as a proxy for the AAR of a melting period. The ELA values from 1985 to 2016 are provided in geoidal heights for HEF, KWF and VNF by the World Glacier Monitoring Service (WGMS).
## 3 Results and Discussion
### Mechanical Properties  
Tensile tests were conducted to analyze the mechanical properties according to the MWNT content and structure form, and the results are presented in Figure 4. There are two types of interaction between nano-fillers and a matrix polymer: covalent and non-covalent interactions [36; 37]. Since these different interactions could affect the composite's mechanical properties, considering the interaction between the filler and matrix is essential. In our case, there are no active functional groups between the MWNT and the NR. Therefore, only physical interactions were considered when discussing the composite's mechanical properties. When CNT is combined with a polymer, the tensile stress is transferred to the interface between the CNT and the polymer and converted to shear stress. Therefore, CNT composites are more mechanically strengthened than pure polymers [38]. Consequently, as the MWNT content increased, Young's modulus of the composites increased, but the elongation decreased, owing to the trade-off relationship between the modulus and elongation [39]. In addition, we analyzed the effect on the mechanical properties of sealing the pure NR layers in a single composite. Because pure NR has a lower modulus and better elongation than a single composite, the hybrid composite not only has a moderate modulus, but also increased elongation [3]. In addition, pure NR layers may prevent surface cracks from tensile strain on the conductive composite layer, owing to the excellent adhesion between the interfaces. These results are presented in Table 1. Based on the elongation analyzed through the tensile test, the range for the stretching/releasing dynamic strain sensing test of all samples was set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Mechanical Property**} & \multirow{2}{*}{**Pure NR**} & \multicolumn{2}{c}{**1 wt.\%**} & \multicolumn{2}{c}{**5 wt.\%**} \\ \cline{3-6}  & & **Single** & **Hybrid** & **Single** & **Hybrid** \\ \hline Young’s modulus [MPa] & \(1.8\) & \(3.7\) & \(2.6\) & \(13.79\) & \(4.6\) \\ Elongation at break [\%] & \(528\) & \(298\) & \(321\) & \(105\) & \(142\) \\ \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of pure NR and MWNT/NR composite.  
Figure 4: Strain–stress curve of pure NR and MWNT/NR composite according to each concentration and structural formation pathway.
# Sentinel-1 GRD Preprocessing Workflow +
## 3 Conclusions  
A standard generic workflow to preprocess Copernicus Sentinel-1 GRD data was presented. The workflow aims to produce a set of preprocessed Sentinel-1 GRD data, offering a benchmark for the development of new products and operational down-streaming services based on consistent Copernicus Sentinel-1 GRD data.  
The workflow applies a series of standard corrections, and to apply a precise orbit of acquisition, remove thermal and image border noise, perform radiometric calibration, and apply range Doppler and terrain correction. Additionally, Sentinel-1 GRD products can be spatially coregistered to Sentinel-2 MSI data grids, in order to promote the use of satellite virtual constellations by means of data fusion techniques.  
The following are available online at www.mdpi.com/2504-3900/18/1/11/s1.  
This work contains modified Copernicus Sentinel data (2019). The author is grateful to the many individuals working on the development of free and free open-source SNAP software for supporting the sharing of knowledge.  
The author declares no conflict of interest.
## 6 Simulation Results and Discussions  
The computer simulations section contains three parts. In the first part, the traditional OFDM, GFDM, WHT-GFDM [18] and the proposed WHT-LWT-GFDM method are compared in the AWGN channel environment. In the second part, comparative performance analysis of four methods is performed in the frequency flat Rayleigh fading channel environment. PAPR improvement of the above schemes are evaluated in the third part of the section. Computer simulations are performed by independent 100 Monte Carlo cycles for Binary Phase Shift Keying (BPSK) modulation. In all simulations, the roll-off factor of the RC pulse shaping filter was 0.95, and the simulations are carried out using \(K=64\) sub-carriers and \(M=8\) sub-symbols for GFDM and the proposed waveform. BER metric is employed in the first and the second part of simulations.  
In this study, OFDM waveform is created according to the IEEE 802.11a/HIPERLAN/2 standard. Accordingly, 48 sub-carriers carry data in an OFDM symbol, while 4 sub-carriers are used as pilots. 12 sub-carriers are also sent as empty. Accordingly, after creating 64 complex samples, time domain samples are obtained with the help of IFFT. In the classical OFDM system, the last 16 of the time domain samples are copied and added to the head to prevent ISI. By adding these 16 samples, called CP, an OFDM symbol consisting of a total of 80 samples is obtained. 100 realizations of this OFDM signal is generated, resulting in a total length of 100 \(\times\) 80 = 8000 bits OFDM data packets.  
For the GFDM waveform, as described in detail in Section 2, the roll-off factor of the RC pulse shaping filter is 0.95 and the GFDM symbol consisting of 512 samples and \(M=8\) sub-symbols and \(K=64\) sub-carriers are obtained. A total of 20 \(\times\) 512 = 10,240 length GFDM data packets are obtained by generating 20 of this GFDM signal.  
WHT-GFDM and WHT-LWT-GFDM waveforms are also produced in the same way. In the WHT-GFDM waveform, data that are modulated differently from GFDM are subjected to WHT transform, and data packets of the same length are formed. The proposed WHT-LWT-GFDM data packets are also subjected to I-LWT transformation after WHT conversion in the WHT-GFDM waveform, and then data packets of the same length are formed.  
CP was not used in GFDM waveform, WHT-GFDM waveform, and the recommended waveform.
# Vortex-Induced Vibration of Deep-Sea Mining Pipes: Analysis Using the Slicing Method
## 4 Numerical Simulation and Results  
### Vortex-Induced Vibration Response of Deep-Sea Mining Pipe  
In the realm of practical marine engineering, cylindrical structures often align and move with seawater currents. These movements are susceptible to vortex-induced vibrations triggered by incoming flows, potentially leading to fatigue damage or even catastrophic structural failures. Over the last century, extensive research involving numerous physical and numerical simulations has been conducted by both domestic and international scholars. This substantial body of work has established a robust foundation for ongoing research in this field. This section employs Fluent2020R2 software, alongside a C language-compiled UDF program and overlapping grid technology, to systematically explore the vortex-induced vibration behaviors of a quasi-three-dimensional deep-sea mining pipe across various flow rates. Additionally, this research delves into how oscillating flows impact the vortex-induced vibrations of such structures. Also, when we select the 5 m riser, other properties do not change; only the lower-end constraint changes (fixed end becomes free end). And a downward vertical tension is applied to the free end of the deep-sea mining pipe, which is the same as the top tension value of the traditional riser in the model verification, and other physical parameters are unchanged. This ensures that the natural frequency of the pipe is consistent.
## 3 Experiments
### Experimental Results and Discussion
Next, by combining the designed DGR algorithm with FC-LSTM, STGCN, and DCRNN algorithms, the corresponding models are names as DGR-FS-LSTM, DGR-STGCN, and DGR-DCRNN, respectively, while predefined the forecasting interval as 30 min, the performance indices of traffic forecasting are displayed in Table 3 under DGR-FS-LSTM, DGR-STGCN, and DGR-DCRNN. It is obvious that the forecasting performances under models combined with DGR algorithm are better than those without DGR algorithm. That means, on the premise of not reducing the forecasting accuracy, the dynamic graph reconfiguration algorithm can reduce the amount of input data under the dynamic traffic flow effectively. Figure 8: Forecasting results for PEMS-BAY dataset. (**a**) The comparison curves under 15 min forecasting interval. (**b**) The comparison curves under 30 min forecasting interval. (**c**) The comparison curves under 60 min forecasting interval. (**d**) The curves of forecasting errors. From the above simulation results and analyses, with relatively few traffic nodes, the proposed EnGS-DGR model outperforms the state-of-the-art traffic forecasting models with both the short-term and the long-term forecasting intervals.
# Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
## 5 Inter-Head Similarity Analysis
### Applying SVCCA and CKA  
In order to verify whether the multi-head strategy affects models' representation subspaces, we examine SVCCA statistics between representations of heads in each model. To utilize SVCCA and CKA, we collect responses \(X=[X^{1},...,X^{N}]\) of each head at the last layers of three modules (encoder's self-attention, decoder's self-attention, and encoder-decoder attention) from development dataset consisting of _num_sentence_ sentences, so that we have \(N=num\_sentence\times token\_per\_sentence\) many \(d\)-dimensional vectors. We compare nine models with a number of heads \(h=\{2,4,8,16\}\) and hidden size \(d=\{64,128,256,512\}\) in order to examine how those architectural parameters change inter-head diversity. We report our results of the last layer of the encoder-decoder attention module only, yet we find the same tendency through every layer of every module.
## 3 Results
### Regression Models with Selected Parameters  
#### 3.5.1 UAV Regression Models  
From the variable importance plot of the best-performing UAV regression model using 12, 20, and 27 May data, combinations of variables were tested in the RF and SVR models. Evaluating based on thresholds of variable importance, as shown in Figure 5, we selected variables for the top 7, 8, 9, 13, and 16 variable groups. An additional group containing only MicaSense band reflectances and VIs was included for comparison as common modelling approaches in other studies use only spectral variables [3]. Furthermore, 70% of each variable group dataset was used for model calibration (Table 9). Overall, RF had better performance than SVR in terms of higher R\({}^{2}\) and lower RMSE values. All variable group models using RF had a high R\({}^{2}\) of 0.96 except for the spectral-only variable group with lower R\({}^{2}\). RF models' RMSE values were also very close, within <0.10 g/m\({}^{2}\) difference, the lowest being the top 9 and top 13 variable groups at 1.07 g/m\({}^{2}\). Again, the RF spectral-only variable group had poorer performance in comparison to other RF models, as its RMSE value was significantly higher. The SVR spectral-only variable group performed poorly compared to all the other SVR models. Of the SVR models, the top 7 variable group had the best performance with an R\({}^{2}\) of 0.86 and an RMSE of 2.05 g/m\({}^{2}\). The top 8, 9, 13, and 16 variable SVR models had only slightly lower R\({}^{2}\) and higher RMSE values compared to the best SVR model.  
The calibrated models were applied to the remaining 30% of datasets for validation (Table 10). In the validation models, SVR had better performance than RF except for the spectral-only model. The best-performing model was SVR with the top seven variables, resulting in an R\({}^{2}\) of 0.80 and an RMSE of 2.62 g/m\({}^{2}\). Compared to the best model, other SVR models had close performance, but as more variables were added, the R\({}^{2}\) had lower values.  
Figure 6: Variance Importance plot produced by the PlanetScope RF regression model of data from 12, 20, and 27 May and 24 variables, using the function varlmpPlot() in RStudio. Higher IncNodePurity values indicate more impact on canopy nitrogen estimation. (1) Please refer to Table 1 for the full name of vegetation indices. TWL1, total wetness index #1; TWL2, total wetness index #2.
## 2 Material and Methods
### Discriminative Scheme Using MOSM-GP  
Let a set of \(N\) labeled BEA trials \(\{\mathbf{x}_{n},l_{n}\}_{n=1}^{N}\), each of them belonging either class \(A\) or \(B\), that is, \(l_{n}\in\{A,B\}\). In the case of DEAP dataset, classes correspond to low and high valence, while, for the MI dataset, left- and right-hand movement imagination are considered. As stated in Section 2.3, a single MOSM-GP models each BEA trial as the stochastic process \(\mathbf{f}_{n}^{(l)}\), resulting in \(N_{A}\) and \(N_{B}\) MOSM-GPs for classes \(A\) and \(B\), respectively. Furthermore, on the evidence of a new BEA trial \(\mathbf{X}_{*}\), the marginal likelihood for each learned MOSM-GP is computed as:  
\[p(\mathbf{f}_{n}^{(l)}(\mathbf{X}_{*})|\mathbf{X}_{n},\mathbf{f}_{n}^{(l)},\mathbf{X}_{*})=\mathcal{ N}(\mathbf{f}_{n}^{(l)}(\mathbf{X}_{*}),\mathbf{K}(\mathbf{X}_{*},\mathbf{X}_{*})). \tag{11}\]  
By evaluating the marginal likelihoods on all training MOSM-GPs, the new BEA trial label is estimated as follows:  
\[l_{*}=\operatorname*{arg\,max}_{l\in\{A,B\}}E\left\{p(\mathbf{f}_{n}^{(l)}(\mathbf{X} _{*})|\mathbf{X},\mathbf{f}_{n}^{(l)},\mathbf{X}_{*}):l_{n}=l\right\}, \tag{12}\]where \(E\{\cdot:l_{n}=l\}\) denotes the expectation operator over training trials belonging to class \(l\). Figure 1 illustrates the proposed discriminative MOSM-GP framework, termed DMOSM-GP.