esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Wavelet Selection Method for Seismic Signal Intelligent Processing
## 2 Materials and Methods
### Wavelet Selection Method
where \(co_{i}(k)\) represents the correlation coefficient between \(X_{i}\) and \(R_{i}^{k}\), \(S\) represents the total number of signal sampling points. The formula for calculating the variance contribution rate \[vcr_{i}(k)=\frac{\frac{1}{\text{NS}}\sum\limits_{ns=1}^{\text{NS}}a_{i}^{k}(ns)^{ 2}-(\frac{1}{\text{NS}}\sum\limits_{ns=1}^{\text{NS}}a_{i}^{k}(ns))^{2}}{\sum \limits_{k=1}^{2^{n}}\left[\frac{1}{\text{NS}}\sum\limits_{ns=1}^{\text{NS}}a_{ i}^{k}(ns)^{2}-(\frac{1}{\text{NS}}\sum\limits_{ns=1}^{\text{NS}}a_{i}^{k}(ns)) \right]^{2}},\] (8) where \(vcr_{i}(k)\) is the variance contribution rate of \(a_{i}^{k}\), \(\text{NS}\) is the total number of \(a_{i}^{k}\) sampling points. 3. Calculation of relevance \(\{r_{i}|i\in[1,I]\}\) from correlation coefficient and variance contribution rate \[r_{i}=\frac{\sum\limits_{k=1}^{2^{n}}\left(co_{i}(k)-\overline{co_{ij}} \right)(vcr_{i}(k)-\overline{vcr_{i}})}{\sqrt{\sum\limits_{k=1}^{2^{n}}\left( co_{i}(k)-\overline{co_{i}}\right)^{2}\sum\limits_{k=1}^{2^{n}}\left( vcr_{i}(k)-\overline{vcr_{i}}\right)^{2}}},\] (9)
4. Calculate the decomposition stability of this wavelet by the mean \(\mu\) and variance \(\sigma^{2}\) of \(\{r_{i}|i\in[1,I]\}\) \[w=\mu/\sigma^{2}\] (10)  
Finally, the above operation is performed on all wavelets to be selected, and the decomposition stability \(w\) of each wavelet is obtained separately, and the wavelet that makes \(w\) maximum under this dataset is selected as the optimal wavelet.
## 3 Results and Discussion
### Chemical Analysis of Green Composites
Similarly, reactive extrusion by means of isocyanurates, as is the case for TGIC, which is usually initiated by organic radicals from peroxides, such as DCP, have managed to achieve the referred grafting compatibilization [35]. This reaction is accomplished by means of the reaction of the -OH groups that are present both in the terminal groups of the biopolymer molecular chains and on the cellulose surface with the multiple epoxide groups present in TGIC [49]. Therefore, ester bonds are formed with the PHA chains by an esterification reaction with the glycolyl terminal groups of the carboxylic acid, which precedes the etherification of the -OH terminal group [34]. The second reaction generates C-O-C typebonds with subsequent formation of -OH side groups on the cellulose surface [50]. For DCP, [PERSON] et al. [51] described the mechanism of cellulose coupling or grafting onto PHB and PHBV. Briefly, when the peroxide is exposed to heat during the extrusion process, it decomposes into free radicals, with high reactivity, which tend to abstract hydrogens (H\({}^{\prime}\)) from the molecular chains of the biopolymer and cellulose to subsequently initiate the referred grafting process between the two phases of the composites. The authors postulated that, specifically, grafted copolymers are formed at the interfaces of the cellulose and the biopolyester matrix. Figure 5: (**a**) Fourier transform infrared (FTIR) spectra, from bottom to top, of the films of the following: poly(3-hydroxybutyrate-\(co\)-3-hydroxyvalerate) (PHBV), PHBV with microfibrillated cellulose (MFC) at 2.5, 5, and 10 wt %, PHBV with triglyceridel isocyanate (TGIC) and dicumyl peroxide (DCP) and with epoxy-based styrene-acrylic oligomer (ESAO), and PHBV/MFC at 5 wt % with TGIC and DCP and with ESAO. (**b**) Detail of the FTIR spectra for the films of PHBV/MFC at 5 wt % uncompatibilized and compatibilized with TGIC and DCP and with ESAO. Arrows indicate the wavenumbers of the chemical bonds described in the text.
## 2 Methodology
### CNN-Based Regressors
#### 2.3.1 Neural Network Regression  
The main advantage of neural networks is that instead of selecting a pair of a feature extractor and a regression model, we select an architecture that allows learning features and predicting cell counts. Moreover, the learning process is end-to-end that significantly simplifies the optimization, and increases adaptive capabilities to a specific problem or data. Moreover, a neural network regression relies only on pixel intensities, whereas other cell counting approaches commonly require additional information, such as the cell's shape, size, and location.  
As mentioned previously, manually extracting features is time and labor-intensive and its performance is hard to predict. Therefore, deep-learning-based models and especially Convolutional Neural Networks (CNN) have become more popular among researchers and practitioners. Because their architecture is intrinsically hierarchical, CNNs can automatically and adaptively learn multi-level hierarchies of features that are translation-equivariant [61]. This means that inside of the CNN, early layers will learn low-level features such as edges, textures, and boundaries, while deeper layers can learn high-level semantic features that are close to the target labels.  
The CNN architecture usually consists of several building blocks, such as convolutional layers, pooling layers, and fully connected layers, whereby the first two layers are repeated multiple times, followed by one or more fully connected layers. In the convolutional layers, features are extracted through a combination of linear and nonlinear operations, i.e., a convolutional operation whereby a kernel is applied across the input and nonlinear activation functions such as the rectified linear unit (ReLU) and its variants. In the pooling layers, feature maps are typically downsampled to introduce translation invariance to small shifts and distortions and to decrease the number of learnable parameters. Max pooling and global average pooling are most commonly used. The output feature map of the last convolutional layer is commonly flattened and connected to one or more fully connected layers.  
As for obtaining large amounts of high quality, labeled data in medical imaging is seldom available because of the cost and necessary workload of radiology experts, transfer learning is often applied. With transfer learning, a network is pretrained on an extremely large dataset, such as ImageNet, and then applied to a different task in a different domain [62]. The underlying notion is that early layers in this network are already trained to recognize low-level features and that these features can be shared across unrelated datasets. This makes transfer learning particularly useful for small datasets.
# Research of Enzyme-Induced Carbonate Precipitation on Strength Behavior of Reinforced Sand
###### Abstract  
Stabilizing sandy soil with inadequate engineering properties is essential for constructing infrastructure systems in all regions, especially in desertification-prone areas. Enzymatically Induced Carbonate Precipitation (EICP) offers an innovative solution, with advantages over conventional soil reinforcement methods due to its low energy consumption and carbon emission. This emerging reinforcement technique has proven effective in enhancing soil strength, yet the effects of variables such as curing time and cementation solution concentration, and their micro-mechanistic implications on sandy soil, remain understudied. This study conducted a series of unconfined compressive strength (UCS) tests and microstructural analyses on EICP-treated sand. The results showed that the optimal curing time for EICP-reinforced sand is seven days, with its strength being contingent upon soil density. The maximum UCS value was observed at a relative density of 0.7 and a cementation solution concentration of 1 mol/L. Mechanistically, EICP strengthens soil integrity through calcium carbonate-mediated cementation and particle bridging, thereby boosting soil strength. Micro-CT imaging and fractal dimension analyses reveal that the precipitation process decreases both the size and connectivity of the pores, while simultaneously increasing their surface heterogeneity and enhancing the overall toughness. This research establishes a foundational framework for advancing EICP applications in soil stabilization engineering.  
Enzymatically Induced Carbonate Precipitation (EICP); soil strength improvement; micro-CT; fractal dimension; pore structure
# Wheat Quantity Monitoring Methods Based on Inventory Measurement and SVR Prediction Model
###### Abstract  
Due to the influences of the storage environment, water content change, particle settlement, natural loss, and other factors, the distribution density of wheat and the volume of grain pile in the storage process are gradually changed so that the single weight calculation method cannot objectively evaluate the storage quantity of wheat and also causes difficulties to the regular inspection of the quantity of wheat stock. To meet the practical needs of wheat inventory monitoring, a wheat inventory monitoring method based on inventory measurement and the support vector machine regression (SVR) prediction model is proposed. By collecting the working papers for the physical inspection of wheat in grain warehouses in Shanxi province, Hebei province, Henan province, Jiangsu province, and other places, the storage time, storage weight, storage moisture content, measured moisture content, measured volume weight, measured net volume, and measured weight for inspection were selected as training samples for the SVR prediction model, and kernel function selection and parameter optimization were carried out. We developed an optimal prediction model for the amount of wheat in the grain depots. In the actual grain store measurement process, the net volume of wheat in the current grain store was obtained by a laser volumetric measuring apparatus, the actual bulk density of wheat was sampled, and the actual moisture content of wheat was measured by sampling. The three samples, their storage time, their storage moisture content, and their storage weight were fed into the trained SVR prediction model as new samples, and the predicted weight of the wheat in the current grain store was obtained from the output. The error rate calculation procedure was introduced to achieve an anomalous judgment error rate for grain depots. The experimental results showed that the SVR prediction model based on the linear kernel function had a very low mean squared error and high determination coefficient, and the average prediction accuracy of the grain stock error rate reached 93.2 percent, which can meet the requirements of wheat quantity monitoring in grain warehouses.  
Keywords:wheat; inventory quantity; support vector machine regression; margin of error; prediction model +
Footnote †: journal: Applied Sciences
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 5 Coalition Formation Game for MPC Creation  
Although the MPC as a payment service consortium contributes to reducing the PSPs' collateral cost, the induced payment latency is considerable according to the MPC size; accordingly, the coalitional gain is not super-additive but limited by the latency cost. Therefore, we propose the MPC creation mechanism in the coalition formation game (CFG) framework, in which independent and rational PSPs form MPCs dynamically based on the distributed formation algorithm, which leads to a stable state for the MPC partitions.
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 2 Methodology
### Additional Random Slow Fading \(\psi_{dB}\)
To calculate the parameter values in dB, we can use the following equations which is defined in [20] as,  
\[mean=\mu_{dB}=e^{(\mu+\frac{\sigma^{2}}{2})} \tag{5}\]  
\[variance=\sigma_{dB}^{2}=e^{(2\mu+\sigma^{2})}\left[e^{\sigma^{2}}-1\right] \tag{6}\]  
From variance, one can calculate standard deviation as,  
\[\sigma_{dB}=\sqrt{e^{(2\mu+\sigma^{2})}\left[e^{\sigma^{2}}-1\right]} \tag{7}\]  
The cumulative distribution function (CDF) of lognormal can be used to determine the probability as,  
\[F(\psi_{dB})=\frac{1}{\sigma\sqrt{2\pi}}\int\limits_{0}^{\psi_{dB}}\frac{e^{- (ln(x)-\mu)^{2}}}{x}dx \tag{8}\]  
The shape of the path loss distribution depends primarily on two parameters scale \(\mu\) and shape \(\sigma\). We have calculated values of both parameters at each distance. A trendline for scale and shape values is obtained, which can be used to calculate lognormal distributions as shown in Figure 5. The path loss statistics provide equivalent probability distributions, which can be used to determine variance as in Equation (9). Standard deviation value at  
Figure 4:0 Trendline equations for \(\mu\) and \(\sigma\) parameters with varying distance up to 100 km. each distance is calculated based on its variance. Standard deviation value increases with increasing distance linearly, whereas mean of path loss increase lognormally with distance. Figure 4:1  
Figure 4:2  
where \(\mu_{dB}\) is the mean of path loss, \(x\) is distance and the value of standard deviation \(\sigma_{dB}\) defines the variance of additional slow fading or path loss. This additional path loss needs to be added to the average measured path loss at varying distances. The availability of the system depends on the fade margin calculated considering these additional path loss variations. The outage probability of slow fading for a given threshold value of \(\psi_{dB}=X_{0}\), and is defined as,  
Figure 4:3  
where, the distribution of \(\psi_{dB}\) depends on standard deviation parameters \(\sigma_{dB}\) and \(\mu_{dB}\).
# Deterministic Coresets for \(k\)-Means of Big Sparse Data +
## 4 Notation and Main Result
### Sparse Coresets  
Unlike previous work, we add the constraints that if each point in \(P\) is sparse, i.e., has few non-zeroes coordinates, then the set \(S\) will also be sparse. Formally, the _maximum sparsity_\(s(P):=\max_{p\in P}\|p\|_{0}\) of \(P\) is the maximum number of non zeroes entries \(\|p\|_{0}=|\left\{i\mid p_{i}\
eq 0,i\in[d]\right\}|\) over every point \(p\) in \(P\).  
In particular, if each point in \(S\) is a linear combination of at most \(\alpha\) points in \(P\), then \(s(S)\leq\alpha\cdot s(P)\). In addition, we would like that the set \(S\) will be of size independent of both \(n=|P|\) and \(d\).  
We can now state the main result of this paper.  
**Theorem 1** (Small sparse coreset).: _For every weighted set \(P=(P^{\prime},u,\rho)\) in \(\mathbb{R}^{d}\), \(\varepsilon>0\) and an integer \(k\geq 1\), there is a \((k,\varepsilon)\)-coreset \(S=(S^{\prime},w,\phi)\) of size \(|S|=k^{O(1/\varepsilon^{2})}\) where each point in \(S^{\prime}\) is a linear combination of \(O(1/\varepsilon^{2})\) points from \(P^{\prime}\). In particular, the maximum sparsity of \(S^{\prime}\) is \(s(P)/\varepsilon^{2}\)._  
By plugging this result to the traditional merge-and-reduce tree in Figure 1, it is straight-forward to compute a coreset using one pass over a stream of points.  
**Corollary 1**.: _A \((k,\varepsilon\log n)\) coreset \((S,w,\phi)\) of size \(|S|=\log(n)\cdot k^{O(1/\varepsilon^{2})}\) and maximum sparsity \(s(P)/\varepsilon^{2}\) can be computed for the set \(P\) of the \(n\) points seen so far in an unbounded stream, using \(|S|\cdot s(P)/\varepsilon^{2}\) memory words. The insertion time per point in the stream is \(\log(n)\cdot 2^{(k/\varepsilon)^{O(1)}}\). If the stream is distributed uniformly to \(M\) machines, then the amortized insertion time per point is reduced by a (multiplicative) factor of \(M\) to \((1/M)\log(n)\cdot 2^{(k/\varepsilon)^{O(1)}}\). The coreset for the union of streams can then be computed by communicating the \(M\) coresets to a main server._
## 3 Results  
### Results of Gap Acceptance  
Across all three conditions, the three-way interaction was not statistically significant (Full: \(\chi^{2}(5)\) = 6.92, \(p\) = 0.227; Partial-AVs: \(\chi^{2}(5)\) = 3.80, \(p\) = 0.578; Partial-TVs: \(\chi^{2}(5)\) = 6.15, \(p\) = 0.292) and was therefore removed. After removing the three-way interaction, the two-way interaction between eHMI information content and speed was not significant in any of the models (Full: \(\chi^{2}(5)\) = 5.43, \(p\) = 0.366; Partial-AVs: \(\chi^{2}(5)\) = 2.88, \(p\) = 0.719; Partial-TVs: \(\chi^{2}(5)\) = 6.66, \(p\) = 0.247) and was excluded.  
After removing the two-way interaction between eHMI information content and speed, the interaction between eHMI information content and gap size was statistically significant in the Full (\(\chi^{2}(5)\) = 11.38, \(p\) = 0.044) and Partial-AVs (\(\chi^{2}(5)\) = 11.71, \(p\) = 0.039) models, while the interaction between speed and gap size was significant in the Partial-TVs model (\(\chi^{2}(1)\) = 7.86, \(p\) = 0.005). To maintain consistency across model structures, both interactions, eHMI information content \(\times\) gap size and speed \(\times\) gap size, were retained in the final fixed-effects specification for all three models.
## 6 Conclusions and Perspectives  
In this paper, the SPP technology is applied to determine the user position calculated by GPS, Galileo and GLONASS data fusion. Data fusion may be containing outliers which negatively impact the final results. To address this issue, the MM-Estimation method is used to eliminate the outliers and IRWLS method is used to optimize the estimated results. Although the MM-Estimation and the IRWLS are effective, a method to detect the positioning errors is needed. The RAIM algorithm was used for this purpose to detect positioning errors exceeding the alert limit. As a result, the proposed robust Extended Kalman Filter is built by combining the Extended Kalman Filter, the MM-Estimation, the IRWLS and the RAIM method to improve the position accuracy. Robust GPS, Galileo and GLONASS data fusion using the robust-EKF increases the position precision by about 84.0% compared to non-robust estimation using data of ABMF base station. Furthermore, the position precision is significantly enhanced using the rEKF-LSTM method by about 95% compared to non-robust data combination. To prove the power of rEKF-LSTM, the approach is used for data from three other base stations: AJAC, GRAC and LMMF. The position accuracy is significantly improved by about 87%, 77% and 93% compared to the non-robust data combination, respectively.  
In this work, the emphasis is laid on the improvement of the positioning accuracy for a slowly moving receiver. Only data from base stations are used, for which velocities are almost equal to zero and helps enhancing the performance of the LSTM model. A perspective of our work could be assessing the performance and limitations of the method on data from a user in motion and adapting or potentially developing a new method taking into account additional velocity information. While considering data from moving users, sensor noise levels are not constant and the challenge is to estimate an adapted LSTM modeling. Moreover, an additional challenge concerns the GNSS signal blockages. A suggested solution is to combine GNSS and INS (Inertial Navigation System) data. Along these lines, the perspective for this work is to use an adapted rEKF-LSTM model for combining GNSS and INS data from users in motion.  
[PERSON] proposed the general idea of the method presented and realized its application to real data. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], suggested the problematic and the field of application, reviewed the idea applied and provided many suggestions. All authors have read and agreed to the published version of the manuscript.  
This research has been performed within the framework of e-PANEMA project. The authors would like to thank the funding organization ADEME.  
Our warmest thanks go to all the other partners of the e-PANEMA project and also to the people involved in monitoring the project within the ADEME agency. Also our thanks are for anonymous reviewers who have contributed effectively in their comments and suggestions to obtain this version of the paper.  
The authors declare no conflict of interest.
# Finite Differences on Sparse Grids for Continuous-Time Heterogeneous Agent Models
## 4 Numerical Approach Using an Upwind Scheme
#### 4.1.4 Matrix Notation
To solve the linear system with consistent basis representations, we use the hierarchical to nodal basis transformations \(\mathbf{E}\) and obtain  
\[\left(\left(\frac{1}{\Delta}+\rho\right)\mathbf{E}-\mathbf{\Lambda}-\mathbf{A }^{n}\right)\mathbf{v}^{n+1}=\mathbf{u}^{n}+\frac{1}{\Delta}\mathbf{E} \boldsymbol{\
u}^{n} \tag{23}\]  
for diffusion processes where \(\mathbf{\Lambda}\) is built with difference operators and thus works on hierarchical coefficients, whereas for Poisson processes we have  
\[\left(\left(\frac{1}{\Delta}+\rho\right)\mathbf{E}-\mathbf{E}\mathbf{\Lambda} -\mathbf{A}^{n}\right)\mathbf{v}^{n+1}=\mathbf{u}^{n}+\frac{1}{\Delta} \mathbf{E}\boldsymbol{\
u}^{n} \tag{24}\]  
where \(\mathbf{\Lambda}\) models the Poisson process. Notice that the resulting vectors on both sides of the equation are given in nodal values and the solution \(\mathbf{v}^{n+1}\) is given in hierarchical values again such that we can simply use it in the next iteration for the computation of its derivatives. The overall procedure is given in Algorithm 1; see [34; 35] for more details on the algorithmic details for the adaptive sparse grids approach, refinement, and coarsening, etc. ```
1:Data: model parameters, sparse grid parameters Result: solution \(\mathbf{v}\) of HJB equation
2:Initialization:
3: generate sparse grid
4: compute hierarchical to nodal basis transformation matrix \(\mathbf{E}\)\(\triangleright\) see after (10)
5: generate finite difference operators \(\triangleright\) see (3) in Section 3.1
6: set up matrix \(\mathbf{\Lambda}\) that models stochastic process \(\triangleright\) see (20) in Section 4.1.4
7: compute initial guess in hierarchical representation \(\mathbf{v}^{0}\)\(\triangleright\) see e.g., (25) for model (3) and (4)
8:Iterative part:
9:for\(n=0,1,\ldots\)do
10: refine sparse grid and initialize the new sparse grid
11: compute forward and backward differences of \(\mathbf{v}^{n}\)\(\triangleright\) use FD operators
12: compute optimal controls \(\triangleright\) consumption, deposits for model (3) and (4),
13:\(\triangleright\) use forward and backward differences of \(\mathbf{v}^{n}\)
14: build drift matrix \(\mathbf{A}^{n}\)\(\triangleright\) (19) in Section 4.1.4, follow upwind scheme and use FD operators
15: solve (23) respectively (24) for \(\mathbf{v}^{n+1}\)\(\triangleright\) see Section 4.1.2, linearized HJB equation
16:if\(\mathbf{v}^{n+1}\) is close to \(\mathbf{v}^{n}\) according to stopping criteria then
17:\(\mathbf{v}\leftarrow\mathbf{v}^{n+1}\)
18: STOP
19:endif
20: coarsen sparse grid
21:endfor
```  
**Algorithm 1** Solving the HJB equation on (adaptive) sparse grids