esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Analysis of the Environmental Factors Influencing Canopy Interception  
Canopy interception is mainly affected by vegetation characteristics (vegetation species, canopy structure, etc.) and environmental factors (precipitation characteristics, wind speed, temperature, etc.) [11; 52]. The NDVI can reflect the physiological characteristics of vegetation. Thus, temporal evolution was revealed to identify the impacts of environmental variables on canopy interception. The stepwise multiple regression analysis was employed to estimate the relative contribution of each variable to canopy interception, and it specifically included temperature (T), precipitation (P), relative humidity (RH), wind speed (WS), solar radiation (SR), air pressure (AP), potential evapotranspiration (PE), and NDVI. The significance of the multiple regression model was examined by the F-test, which could be reflected by the determination coefficient R\({}^{2}\), and the candidate variables were selected for the model based on a 0.05 significance level [53]. All data were normalized before applying the method. The spatial distribution of the relative contributions of these factors in China was obtained by calculating eight environmental variables to canopy interception in each pixel using GIS technology.  
Finally, the stepwise multiple regression method was applied by setting the normalized canopy interception as the dependent variable and the normalized environmental variables as predictors (independent variables). The contribution of each influencing factor to the change of precipitation interception was calculated using the following equations:  
\[Y=a_{1}X_{1}+a_{2}X_{2}+a_{3}X_{3}+\cdots+a_{n}X_{n} \tag{9}\]  
\[\eta_{1}=\frac{|a_{1}|}{|a_{1}|+|a_{2}|+|a_{3}|+\cdots} \tag{10}\]  
where \(Y\) is the dependent variable, \(X_{1}\), \(X_{2}\), \(X_{3},\cdots\) are the independent variables, \(a_{1},a_{2},a_{3},\cdots\) are the coefficients of this regression equation, and \(\eta_{1}\) is the relative contribution ratio of each dependent variable to the variations in canopy interception.
## 5 Optimal Sensor Placement and Validation Results Based on QR Pivoting
### QR Pivoting for Sparse Sensor Placement  
Inspired by Q-DEIM, the key to achieve the optimal placement of oversampling is to apply the QR factorization with column pivoting to the basis vectors. QR pivoting was proposed by [PERSON] and [PERSON] in the 1960s to solve the least-squares problems [31], which has found utility in various measurement applications [32; 33; 34].  
The QR pivoting decomposes a matrix \(A\in\mathbb{R}^{n\times n}\) into a unitary matrix \(\mathbf{Q}\), an upper-triangular matrix \(\mathbf{R}\) and a column permutation matrix \(\mathbf{C}\), i.e., \(AC^{T}=QR\). QR factorization provides an approximate greedy method consistent with Equation (21), which can achieve the submatrix volume maximization to maximize the determinant. QR factorization increases the volume of the submatrix by choosing a new pivot column with the largest 2-norm and then subtracting its orthogonal projection onto the pivot column.  
Therefore QR pivoting determines \(r\) optimal sensor locations based on the tailored basis vectors when the number of basis vectors equals to \(r\).  
\[\Psi_{r}^{T}C^{T}=QR \tag{22}\]  
Furthermore, the oversampled case, i.e., \(r>p\), may be solved by pivoted QR factorization of \(\Psi\Psi^{T}\), where the pivots are selected from an expensive QR factorization of an \(n\times n\) matrix.  
\[(\Psi\Psi^{T})C^{T}=QR \tag{23}\]  
Based on the optimization method presented in this section, the capability of QR factorization to control the condition number is also validated in the following sections. Essentially the sparse sensor placement is similar to the concepts of the variable and feature selection in machine learning. Moreover, this sensor (feature) selection technique can beextended to data-driven classification, which has additionally been employed in the field of signal processing to sample and estimate signals of graphs efficiently.
## 3 Method  
GPNet is specifically designed to address the challenge of effectively localizing various types of image manipulations. Unlike image-level detection, which focuses on identifying the presence of tampering, pixel-level localization presents a greater difficulty. Consequently, GPNet places a stronger emphasis on solving the localization problem. We explain the architecture of GPNet in greater detail below. The tampered image is fed into GP-Net, and the feature maps are obtained by the CNN branch and the Transformer branch in parallel. Then, feature maps from both branches are fused by the TcFusion module we propose. The final tampered region is obtained through a process of upsampling. The architecture of GPNet is depicted in Figure 2. The green area represents the CNN stream, as discussed in detail in Section 3.1. The CNN stream is responsible for the extraction of relevant feature maps from the input images. The orange area represents the Transformerstream. This stream is explained comprehensively in Section 3.2. The Transformer stream focuses on capturing long-range dependencies and refining the extracted features. The feature maps obtained from both the CNN stream and the Transformer stream, with the same resolution, are then fed into the TcFusion module, depicted in the pink area of Figure 2. The TcFusion module combines the features from both branches to make the final prediction. The specifics of this module are introduced in Section 3.3. By integrating both the CNN and Transformer branches and utilizing the TcFusion module, GPNet aims to leverage the strengths of each approach to enhance the overall performance in pixel-level localization of image manipulations.
## 4 Results
### Leave-One-Out-Accuracy  
This is applied only to the ASL finger-spelling dataset which comprises user-specific implementations of sign gestures. It works in the following manner:  
* Consider User A. To find the leave-one-out-accuracy for this user, the model under consideration is trained on the remaining users and tested on User A.
* This process is repeated for all users. The mean accuracy for all the users gives the leave-one-out-accuracy for the entire dataset.  
This user-specificity can be accurately captured with the aid of 'Hand coordinates' which are used to find the leave-one-out-accuracies as shown in Table 13. For the ANN results in Table 13, the batch size is 16, the neural network is run for 10 epochs and the best weights are used for evaluation.  
It is evident from Table 13 that ANN is effective in capturing the complex non-linear patterns in user-specific variations compared to simple ensemble techniques which is why its mean accuracy is greater than the mean accuracies of the ensemble methods.
## 4 Theoretical Model for Friction Force  
### Existing Friction Force Models  
Some countries, including the Japan Society of Trenchless Technology (JSTT) (Tokyo, Japan) and the China Society of Trenchless Technology (CSTT) (Beijing, China), have formulated technical standards for the calculation of friction force in box jacking projects. However, there is no calculation model for box jacking specified by the British Pipe Jacking Association (PJA 1995), the American Society of Civil Engineers (ASCE 27), the French Society of Trenchless Technology (FSTT 2006), the German Trenchless Association (ATV A-161), and do not stipulate a separate calculation model.  
Table 6 inventories the calculation formulas for the friction force in the above standards. These formulas can be classified into two patterns: (1) theoretical derivation based on different earth pressure models, including Japan's standard BMDN and China's standard TSP, and (2) data statistics based on engineering experience, such as China's standard TCB and TSR.  
TCB and TSR have the same formula, calculating the overall friction force by considering the outer area of the box and the friction force per unit area. The selection of the friction force unit is based on extensive engineering data, as presented in Table 7. However, data statistics are limited due to the lack of mechanical explanation, which leads to prediction deviations. In addition, BMDN and TSP primarily focus on precise calculations of normal earth pressure, differing only in consideration of the soil arching effects. Although the above models do not fully consider the lubricant effect or use segmented calculations, these models have been widely used in practical engineering, and the prediction accuracy will be discussed below.  
Figure 9: Actual jacking forces during construction.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 4 Deep Learning Models for Anomaly Classification
### RNN-Based Deep Learning Models  
The RNN-based models include LSTM [9], GRU [10], LSTMAttention [11], GRUAttention [12], TSSequencer [13], and PatchTST [14]. Each model offers distinct features and advantages for handling time-series data:  
* LSTM and GRU Models LSTM (Long Short-Term Memory) models address the long-term dependency problem by utilizing a cell state and gate structure (Forget, Input, and Output gates). These gates control how much information is retained, added, or discarded, allowing LSTMs to efficiently process sequential data with complex dependencies. GRUs (Gated Recurrent Units) simplify this structure by using only two gates (Reset and Update), making them more computationally efficient while still capable of learning long-term dependencies. However, GRUs may struggle with highly complex dependencies compared to LSTMs.
* LSTMAttention and GRUAttention Models These models enhance LSTM and GRU architectures by incorporating an attention mechanism, which assigns weights to specific time steps, enabling the model to focus on the most important hidden states for extracting relevant information.
* TSSequencer Model The TSSequencer model processes input data in non-overlapping patches using a BiLSTM (Bidirectional LSTM) layer and MLP (Multilayer Perceptron) for channel mixing. Unlike Transformers, which explicitly compute dependencies between all tokens, the BiLSTM layer captures temporal dependencies sequentially, making it a lightweight alternative for time-series data.
* PatchTST Model PatchTST is a Transformer-based architecture designed for multivariate time-series prediction and representation learning. It separates each time series into independent channels and uses a Transformer encoder to process each channel individually. The key innovation lies in its patching mechanism, which divides each channel into patch sequences of length PPP and stride SSS, reducing computational complexity while preserving long-term temporal correlations. The Multi-Head Attention mechanism learns dependencies between patches, and the final output is refined using BatchNorm and residual concatenation.  
Additionally, PatchTST employs a self-supervised learning approach using a Masked Autoencoder, which masks patches instead of individual time steps, enabling deeperlearning of temporal patterns. This method enhances the model's ability to generalize by learning robust feature representations from unlabeled data.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 2 Related Work
### Concurrency Control
#### 2.2.1 Transaction Decomposition  
For two transactions with inter-transaction dependencies, all three above approaches require one transaction critical path to wait for the other to complete. To address these limitations, transaction decomposition-based methods have been proposed [6; 7; 8; 9]. Transaction decomposition minimizes contention and reduces the duration of locks on commonly accessed data items by analyzing programs to divide a transaction into independent sub-transactions, which enables controlled access to uncommitted writes through dependency tracking. IC3 [8] integrates a static analysis of transaction workloads with runtime techniques that manage and enforce dependencies among concurrent transactions, ensuring serializability while permitting parallel execution of certain conflicting transactions. [PERSON] [9] partitions a transaction into groups, then employs different CC protocols for groups to execute independently.
## 3 Results and Discussion
### Overall Migration Measure  
According to Article 3 of Regulation (EC) 1935/2004 [30], the release of substances from food contact materials should not bring about unacceptable changes in the composition of the food. That is why the overall migration limit, which represents the maximum permitted amount of non-volatile substances released from a material into foods or food simulants, was set to 10 mg of substances per 1 dm\({}^{2}\) of surface area of plastic material.  
The results of the overall migration measurements of PBSA, PBSA NCP, PHBV and PHBV NCP, and the corresponding aged materials, are reported in Figures 2 and 3.  
All the studied materials showed an overall migration value lower than the authorized threshold of 10 mg.dm\({}^{-2}\). Based on that criterion, these materials meet the level of requirement that determines their allowance for food contact with acidic and fatty foods. Nevertheless, different behaviors were observed depending on the polymer type and the food simulant in contact and are in agreement with the results of previous works on these materials [31]. The contact with 95% ethanol was already identified as the \"worst case\" condition most affecting the inertial properties of bio-polyester. The impact of the accelerated aging, when it was significant, decreased the overall migration whether nanoclays were added or not. This can be explained to a large extent by the increase in the degree of crystallinity induced by the treatment evidenced in Section 3.1. Considering that the migration values measured in this experiment correspond mainly to the release of polymer fragments soluble in the various solvents  
Figure 1: Pictures of the materials before and after accelerated aging.  
Figure 2: Overall migration of PBSA, PBSA NCP and the corresponding aged materials to the selected food simulants (3% acetic acid, 95% ethanol and isooctane).  
used as food simulants, the expansion of the crystalline phase can be considered as an obstacle to the migration process. It should be noted that the impact of UV treatment is particularly significant on the PHBV in contact with ethanol, this result suggesting a combined effect of UV irradiation and ethanol sorption known to induce crystallization of this polymer.  
The impact of the addition of nanoclays appears to be variable and only slightly significant in most cases. Overall, the addition of such nanofillers tends to increase the overall migration rate when no UV treatment is applied to materials, no significant behavior being observed after aging of the materials. However, it is established that nanoclays have a nucleating effect which tends to increase the crystallization rate of semi-crystalline thermoplastic polymers [32; 33], and should therefore decrease the migration.
# Using Feedback Strategies in Simulated Annealing with Crystallization Heuristic and Applications
## 4 SA with Incomplete Cost Evaluation Applied to EIT
### Partial Evaluation and Interval SA  
Evaluating the objective function on (9) typically requires solving a linear system. Since SA requires a large number of objective function evaluations, the total computational cost of a full EIT reconstruction by SA can become quite large. Let us suppose that it is possible to obtain an estimate for the objective functions \(\tilde{E}\) bounded by upper and lower limits \(E^{\text{max}}\) and \(E^{\text{min}}\). Then, it is possible to obtain similar estimates for the variation of the objective function at the \(i\)-th iteration:  
\[\Delta\tilde{E}_{i} =\tilde{E}_{i}-\tilde{E}_{i-1}\] \[\Delta E^{\text{max}}_{i} =E^{\text{max}}_{i}-E^{\text{min}}_{i-1}\] \[\Delta E^{\text{min}}_{i} =E^{\text{min}}_{i}-E^{\text{max}}_{i-1}.\]  
This corresponds to the classical difference operation in interval arithmetic. Then, by the definition of the SA algorithm, if in a given iteration \(\Delta\tilde{E}\), \(\Delta E^{\text{max}}\), \(\Delta E^{\text{min}}\) satisfy  
\[\exp\left(-\Delta E^{\text{max}}/T\right)\geq\begin{cases}1-P_{\text{err}}& \text{if }\Delta\tilde{E}\leq 0,\\ \exp\left(-\Delta\tilde{E}/T\right)-P_{\text{err}}&\text{if }\Delta\tilde{E}>0 \end{cases} \tag{10}\]  
\[\exp\left(-\Delta E^{\text{min}}/T\right)\leq\min(1,P_{\text{err}}+\exp\left( -\Delta\tilde{E}/T\right)) \tag{11}\]then an algorithm that uses the approximations \(\tilde{E}_{i}\) has a probability of less than \(P_{\text{err}}\) of deviating from an algorithm that uses the exact values \(E_{i}\).  
In [32], this property is used by [PERSON] and [PERSON] to create stopping criteria for the underlying linear solvers that evaluate (9) for EIT reconstruction. By limiting the probability of deviation to less than 5%, EIT images were reconstructed by SA in as few as 20 iterations of a conjugated gradient solver operating on meshes of more than 1000 nodes.
## 3 Clustering Rewards Based on Compound Correlation
### Compound Correlation  
The compound correlation between UAVs \(i\) and \(j\) that couples the communication link correlation and group information is defined:  
\[e_{ij}=\alpha_{g}f_{ij}^{g}+\alpha_{c}f_{ij}^{c} \tag{3}\]  
where \(\alpha_{g}\) and \(\alpha_{c}\) are the corresponding weights. \(f_{ij}^{g}\) denotes the group correlation between UAVs \(i\) and \(j\):  
\[f_{ij}^{g}=\begin{cases}1,&g_{i}=g_{j}\\ 0,&else\end{cases} \tag{4}\]  
\(g_{i}\) is the group to which UAV \(i\) belongs.  
The total intra-cluster compound correlation of cluster \(C_{k}\) is the following:  
\[f(C_{k})=\sum_{(i,j)\in\mathcal{E}_{k}}e_{ij} \tag{5}\]  
Theorem 1 shows that the merge of two clusters can always increase the intra-cluster communication link correlation as long as the clustering constraints are satisfied, which means that clusters with maximal size can be built. Therefore, Goals 1 and 3 in Definition 1 can be fused, then Goal 1 can be ignored in subsequent analysis.  
Theorem 1: _The merge of two clusters will increase the intra-cluster communication link correlation as long as the clustering constraints are satisfied._  
Proof: See Appendix A.  
We combine Goals 2 and 3 by building the compound correlation indicator in (3). It is proven in Theorem 2 that the more UAVs in the cluster belong to the same group, the higher the total intra-cluster compound correlation.  
Theorem 2: _When more UAVs belong to the same group divided into one cluster, the total intra-cluster compound correlation will be higher._  
Proof: See Appendix B.  
Through the above analysis, we have fused the three goals of the structured clustering problem into a compound correlation indicator. Therefore, we can build a clustering structure that fulfills the desired goals by aggregating UAVs with higher compound correlation.
# High-Fidelity Steganography: A Covert Parity Bit Model-Based Approach
This is usually accomplished by training a CNN to classify stego and cover images of a targeted steganography algorithm. In the end, if the CNN is not able to classify the images, then the steganography technique is considered safe, as the CNN is not able to differentiate between the cover and stego images. This implies that the stego image is at least semi-identical to the cover image and that the hiding process did not introduce very many artifacts into the image. The contributions of the paper can be summarized as follows:  
1. A new data hiding strategy based on DCT modeling maximizes the similarity between the stego signal and the original cover signal, thereby improving the quality of the stego signal. 2. A new DCT modeling strategy is proposed that is suitable for steganography techniques. The proposed modeling phase adopts a polynomial fitting strategy to generate a signal approximating the cover signal. Unlike other recent approaches such as [21; 22] which superimpose the secret signal over the modeled signal to produce the stego signal, the proposed strategy adaptively adds or subtracts secret samples to or from the modeled pixels to match the original DCT coefficients of the cover signal based on the error sign between the modeled signal and the cover signal. This adaptive approach maximizes the similarity between the stego and original cover signals, which enhances the imperceptibility of the stego image. 3. A new strategy reduces the communication cost between the encoder and decoder. The decoder requires the polynomial coefficients in order to extract the secret data. The default approach in other DCT modeling approaches is to model the DCT region in a column-by-column manner. This approach increases the communication cost. We propose extracting a single DCT signal in a zigzag manner instead of column-by-column. The structure of the paper is as follows. Section 2 reviews related state-of-the-art literature/The DCT transform is introduced in Section 3. The embedding and extraction processes of the proposed scheme are explained in Section 4. Section 5 discusses the experimental results. Lastly, the conclusions are summarized in Section 6.