esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Encoder for Extracting Features
#### 3.1.2 Atrous Spatial Pyramid Pooling  
A general CNN reduces the spatial resolution of the feature map by using max-pooling or stride in the continuous convolution process to extend the receptive field and reduce the computation costs. However, this process causes loss of spatial information in the deep layer, thereby reducing the accuracy in pixel unit prediction (e.g., semantic segmentation). Therefore, the EAR-Net utilizes ASPP at the end of the encoder to extend the receptive field without reducing the resolution of the feature map. The ASPP was first presented in DeepLab, and it used several atrous convolutions with different rates in parallel. This allowed the CNN to have a receptive field of various sizes while expanding the receptive field.  
In addition, the EAR-Net combines depthwise separable convolution with atrous convolution (named ADSC) to minimize the amount of computation and parameters of newly added ASPP and replaced the \(1\times 1\) convolution with \(1\times 1\) DSConv. ADSC is an operation that applies the atrous rate to depthwise convolution and significantly reduces the computation costs and parameters while maintaining performance. Figure 6 shows the structure of the ASPP.  
The ASPP is processed in parallel with a total of 5 branches: \(1\times 1\) DSConv; \(3\times 3\) ADSC with rates of 6, 12, and 18; and global average pooling with \(1\times 1\) DSConv and upsampling. Then, the feature map of each branch is concatenated, and the combined feature map is reconstructed by \(1\times 1\) convolution. Equation (3) represents the ASPP.  
\[ASPP=W_{1}^{1}(W_{1}^{1}(x)\cdot W_{3}^{6}(x)\cdot W_{3}^{12}(x)\cdot W_{3}^{18 }(x)\cdot\theta W_{1}^{1}\sigma(x)) \tag{3}\]  
Here, \(x,W_{kernel}^{rate},\theta,\sigma\) represent input, DSConv (ADSC if rate is greater than 1), global average pooling, and upsampling, respectively.
# Scaling of FAPAR from the Field to the Satellite
## 4 Results and Analysis
### Validating MODIS Product
#### 4.3.2 Temporal Validation  
The time series of MODIS FAPAR at all _in-situ_ sites were compared to the upscaled ASTER FAPAR values (Figure 14). In the up-scaling process, the STD values of FAPAR within each 64 \(\times\) 64 ASTER pixel block corresponding to each MODIS pixel were also calculated, shown as red bars in Figure 13. The STD represented the degree of sub-pixel heterogeneities within each MODIS pixel. Generally increasing trends of STD with time were also observed. The increased sub-pixel heterogeneities within each MODIS pixel might also be a reason for the less consistency of MODIS FAPAR with the truth reference data on 10 July. On MODIS pixel scale, the No. 4 site has the lowest subpixel heterogeneities while the No. 13 site has the highest subpixel heterogeneities. Although the degrees of subpixel heterogeneities were various, the MODIS FAPAR agreed very well with the upscaled ASTER FAPAR. This is in accordance with the above analysis in spatial comparisons.
# An Improved Federated Learning-Assisted Data Aggregation Scheme for Smart Grids
## 2 Related Works  
We will conduct a comparative analysis of the existing work based on the features within our system. In reference [14], [PERSON] and [PERSON] introduced a technique aimed at training neural networks on horizontally partitioned data by exchanging update parameters. This method ensures the privacy of participants' training data while maintaining the accuracy of the resulting model. Additionally, [PERSON] et al. [15] presented a novel deep learning system that employs additive techniques to safeguard gradients from inquisitive servers, thus preserving user privacy, while assuming no degradation in the accuracy of deep learning with homomorphic encryption. [PERSON] et al. [16] proposed an optimizedfederated learning framework that incorporates a balance between local differential privacy, data utility, and resource consumption. They classified users based on varying levels of privacy requirements and provided stronger privacy protection for sensitive users. Moreover, [PERSON] et al. [17] devised a secure aggregation approach for high-dimensional data and proposed a federated learning protocol that supports offline users using secure multi-party computing.  
[PERSON] et al. [18] proposed a secure framework using homomorphic encryption to protect training data in logistic regression. Their approach ensures security even when dealing with large datasets. [PERSON] et al. [19] utilized homomorphic encryption and [PERSON]'s scrambled circuit to construct a privacy-preserving system for ridge regression. This system outputs the best-fit curve without exposing the input data to additional information. [PERSON] et al. [20] suggested a method that combines DP and SMC to address inference threats and generate highly accurate models for federated learning techniques. [PERSON] et al. [21] proposed a privacy-preserving approach that ensures that user data satisfy DP and can be effectively learned for distributed user data. [PERSON] [22] combined DP and SMC with machine computing, presenting a feasible protocol for learning neural networks in a collaborative manner while protecting the privacy of each recording.  
In contrast to prevailing methods, our system primarily enhances the FL process's security by integrating advanced techniques, such as homomorphic encryption and DP. By doing so, it effectively safeguards against inference threats and ensures the privacy of users' sensitive data, even if they decide to withdraw from the learning process before completion.
## 3 Methodology
### Selection Criteria  
The study selection process was strict, with a detailed set of inclusion and exclusion criteria implemented (Table 1) to ensure both the relevance and high-quality standards of the studies chosen.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline
1. Relevance of the Topic: studies that specifically analyze the relationship between innovation, quality and competitiveness in manufacturing PYMES will be included. & 1. Irrelevance of the Topic: studies that do not directly address the relationship between innovation, quality and competitiveness in manufacturing PYMES. \\ \hline
2. Type of Company: They will focus exclusively on small- and medium-sized enterprises (PYMES) in the manufacturing sector. & 2. Type of Company: studies that focus on large companies, microenterprises or sectors other than manufacturing. \\ \hline
3. Temporal dimension: studies published between 2019 and 2024 will be included to ensure the timeliness of the data. & 3. Publication Date: studies published before 2019. \\ \hline
4. Language: studies must be written in English or Spanish. & 4. Language: studies written in languages other than English or Spanish. \\ \hline
5. Type of Study: both qualitative and quantitative studies will be considered, including empirical research, case studies and systematic reviews. & 5. Type of Study: non-systematic narrative reviews, editorials, opinions and comments will be excluded. \\ \hline
6. Available Data: studies that provide sufficient data for meta-analysis, such as sample sizes, correlation coefficients, values of beta, R\({}^{2}\), Chi-square (\(\chi^{2}\)) and F\({}^{2}\). & 6. Lack of Data: studies that do not provide sufficient data for meta-analysis. \\ \hline \end{tabular}
\end{table}
Table 1: Study selection criteria for meta-analysis.
## 2 Materials and Methods  
### Validation Data  
The validation dataset was drawn from the Integrated Carbon Observation System (ICOS) measurement sites situated across Europe. ICOS eddy-covariance flux data are processed and audited consistent with the pipeline methodology developed within the FLUXNET network [4]. For this study, in order to match compatible satellite-derived data available only post 2016 (Sentinel3), we elected to focus on the years 2018 and 2019 employing the 'Warm Winter 2020' dataset, first release. A total of 69 sites were considered. Data exploration steps (e.g., screening for gaps of greater than two months) reduced the final dataset to 64 sites (Table 1, Figure 1, Appendix A). Vegetation categories follow the IGBP land cover classification: evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), mixed forest (MF), closed shrublands (CSH), open shrublands (OSH), savannahs (SAV), grasslands (GRA), permanent wetlands (WET) and croplands (CRO).  
Daily GPP data (gC m\({}^{-2}\) d\({}^{-1}\)) were based on the day-time flux decomposition method and the filtering based on a variable friction velocity threshold (GPP_DT_VUT_USTAR50). We retained only those days for which less than 20% of the underlying half-hourly meteorological and flux data were gap-filled. Our analysis is based on ten-day averages. This was the preferred timescale as voted in a user survey and mirrors the commonly adopted timestep for remote sensing products such as the Copernicus Land Monitoring Service.
## 3 The Statistical Regression Adaptive Kalman Filtering Algorithm
### The Analysis of the Convergence of the Proposed Algorithm  
**Remark 4:**\(\mathbf{X}_{k|k-1}\) _is derived from \(\chi_{i,k-1|k-1}\), which is calculated according to \(\mathbf{P}_{k|k}\). \(\mathbf{P}_{k|k}\) is derived from \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\). Hence, both \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) have a direct influence on the iterative formula. Therefore, the EM approach is employed to calculate the \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) online._  
**Proof:** Calculate the estimations of \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) based on \(\mathbf{Z}_{1:k}\), where \(\mathbf{Z}_{1:k}\) includes all of the measurement information from time 1 to \(k\), and the MLE is selected to infer \(\mathbf{P}_{k|k-1}\) and \(\mathbf{R}_{k}\) because the MLE has asymptotic efficiency and strong consistency. \(\square\)  
However, it is impossible to obtain \(\log p_{\Phi_{k}}(\mathbf{X}_{k|k},\mathbf{Z}_{1:k})\) because \(\mathbf{X}_{k|k}\) is unobserved at time \(k\). Using Equation (10), the suboptimal solution of \(\Phi_{k}\) at the number of the \(l+1\)th iteration can be transformed into the subsequent suboptimal problem. It is defined that \(\Phi_{k}^{(l+1)}\) denotes an approximate solution of \(\Phi_{k}\) at the \(l+1\)th iteration, and it can be approximated as:  
\[\Phi_{k}^{(l+1)}\approx\underset{\Phi_{k}}{\text{argmax}}\Omega(\Phi_{k}, \Phi_{k}^{(l)}) \tag{34}\]  
Figure 4: The flowchart of the proposed SRAKF.  
Employing the Bayesian theory, Equation (10) could be given by Equation (35):  
\[\begin{split}\Omega(\Phi_{k},\Phi_{k}^{(l)})&=\int \left[\log p_{\[PERSON]_{k}}(\mathbf{X}_{k|k}|\mathbf{Z}_{1\pm k})p_{\[PERSON]_{k}}( \mathbf{Z}_{1\pm k})\right]_{p_{\[PERSON]_{k}^{(l)}}}(\mathbf{X}_{k|k}|\mathbf{Z}_{1 \pm k})d\mathbf{X}_{k|k}\\ &=\int\left[\log p_{\[PERSON]_{k}}(\mathbf{X}_{k|k}|\mathbf{Z}_{1\pm k}) +\log p_{\[PERSON]_{k}}(\mathbf{Z}_{1\pm k})\right]_{p_{\[PERSON]_{k}^{(l)}}}(\mathbf{X}_ {k|k}|\mathbf{Z}_{1\pm k})d\mathbf{X}_{k|k}\\ &=\int\log p_{\[PERSON]_{k}}(\mathbf{X}_{k|k}|\mathbf{Z}_{1\pm k})p_{ \Phi_{k}^{(l)}}(\mathbf{X}_{k|k}|\mathbf{Z}_{1\pm k})d\mathbf{X}_{k|k}+\log p _{\[PERSON]_{k}}(\mathbf{Z}_{1\pm k})\end{split} \tag{35}\]  
Making use of \(\Phi_{k}=\Phi_{k}^{(l)}\) in Equation (35), we derive  
\[\partial\Omega\Big{(}\Phi_{k}^{(l)},\Phi_{k}^{(l)}\Big{)}=\int\log p_{\Phi_{k} ^{(l)}}\Big{(}\mathbf{X}_{k|k}|\mathbf{Z}_{1\pm k}\Big{)}p_{\Phi_{k}^{(l)}} \Big{(}\mathbf{X}_{k|k}|\mathbf{Z}_{1\pm k}\Big{)}d\mathbf{X}_{k|k}+\log p_{ \Phi_{k}^{(l)}}(\mathbf{Z}_{1\pm k}) \tag{36}\]  
Subtracting Equation (36) from Equation (35), the following equation is derived:  
\[\begin{split}&\log p_{\Phi_{k}}(\mathbf{Z}_{1\pm k})-\log p_{ \Phi_{k}^{(l)}}(\mathbf{Z}_{1\pm k})=\\ &\quad\quad\
## 3 Results  
### Comparison of Selected Tree-Based Models  
Among the models, GB exhibited the best performance, as indicated by the highest R\({}^{2}\) of 0.59 (Figure 2). Following closely, RF demonstrated comparable predictive ability with an R\({}^{2}\) of 0.57. Lastly, the DT model displayed the weakest performance, with an R\({}^{2}\) of 0.45. Furthermore, the RF and GB models exhibited higher prediction accuracy with fewer errors. Moreover, these two models showed a more concentrated distribution of estimates around the observations, suggesting increased consistency and reliability in their predictions compared with those of the DT model (Figure 2).  
The optimal values for the hyperparameters of each model used in the study are summarized in Table 3. For the DT model, a maximum depth of six was selected to prevent overfitting caused by the excessive expansion of the tree structure. The minimum number of samples per node was set to 12. In the RF model, a maximum depth of 12 and a minimum number of eight samples per leaf were chosen. In the GB model, a maximum depth of eight and a minimum number of 18 samples per leaf were chosen. Additionally, an internal node requires at least eight samples for splitting to manage complexity and minimize overfitting concerns. Our RF and GB models comprised 100 trees to ensure accurate results. However, adding more trees beyond this point yielded no further improvements.
# Pipe Crack Recognition Based on Eddy Current NDT and 2D Impedance Characteristics
## 3 Feature Extraction of the ECT Signal Based on the Impedance Diagram
### Investigaton of the Influence of Lift-Offs on the Impedance Diagram  
Considering that ECT is very sensitive to lift-off, the influence of lift-off on the impedance diagram is investigated. Using the automatic scanning system with the stimulating frequency as well as the scanning speed kept unchanged, three samples with different types of cracks are tested under different lift-off values. Neglecting the thickness of the probe jacket protecting the internal coil, the four lift-off values are respectively 0 mm, 1 mm, 2 mm and 5 mm.  
Figure 9 shows the retrieved impedance diagrams of a weld crack, body crack and weld toe crack under different lift-offs. The crack depth for all cracks is 80%. It can be observed that for each type of crack, as the lift-off increases, the pattern size of the impedance diagram accordingly decreases, yet the pattern structure remains the same. When the lift-off is less than 2 mm, the pattern size of the impedance diagram is acceptable, in which case the crack type can be easily identified. When the lift-off exceeds 2 mm, the tiny pattern size is beyond acceptance and the crack type can hardly be distinguished correctly. Consequently, the lift-off value is supposed to be less than 2 mm during the test process. In our experiments, to avoid the possible impact from lift-off fluctuation, we employed a specially designed spring structure, as illustrated in Figure 10, to keep the probe tip in touch with the specimen surface.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Number of closed curves that exceed the threshold area & 1 \\ Expectation of closed curve areas & 385 \\ Variance of closed curve areas & 0 \\ Expectation of the slope of fitted lines & 19.38 \\ Variance of the slope of fitted lines & 160.38 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic parameters.  
Figure 8: Impedance diagram for extraction of the characteristic parameters.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 3 Proposed Machine Learning Models
### Ensemble Methods
#### 3.5.1 Bagging  
Bagging method (bootstrap aggregating) can generate multiple versions of a predictor to obtain an aggregated predictor [68]. It generates multiple models independently of different versions of the datasets via random bootstrapping of the original training set. In other words, several training examples could repeatedly appear in different bootstrap replicates. Then, the individual predictions are aggregated through a combination method (either voting or averaging) to form the final prediction. The bagging method can be used to reduce the variance of a base estimator (e.g., a regression tree), by introducing randomization into its construction procedure and making an ensemble out of it. This study used four individual models to build bagging ensemble models including an LR bagging ensemble model (abbreviated as Bag_LR), an SVR bagging ensemble model (abbreviated as Bag_SVR), a BPNN bagging ensemble model (abbreviated as Bag_BPNN), and a CART bagging ensemble model (abbreviated as Bag_CART).
# Rain Monitoring with Polarimetric GNSS Signals: Ground-Based Experimental Research
## 3 Results and Discussion
### Statistical Results of G22 Satellite in 2015
#### 3.2.3 Comparison with Weather Station  
In Figure 13a, the tendency of accumulated rainfall from Jurong is similar with that of accumulated phase shift. Thus, their correlation coefficient is largest, up to 0.9631. From Figure 13b, the trend of accumulated rainfall from radar is well consistent with that of accumulated phase shift. Furthermore, their correlation coefficient reaches 0.9818, which is better than that with Jurong. This is also due to the fact that the path-averaged rainfall matches with GPS signals' ray in space, while rainfall from weather station is just a point.
# Three-Dimensional Processing of Reflections for Passive-Source Seismology Based on Geometric Design
## 2 Extracting 3D Virtual Shot Records Based on Geometry  
### Principle of Reflection of Waves from Passive Sources  
Interferometry is the basis of calculations for passive seismic exploration and can be derived from the equation of the acoustic wave according to the reciprocity theorem [31; 32]:  
\[2R\big{\{}\,G(x_{A,},x_{B,}\omega)\big{\}}\,S(\omega)=\frac{2}{\,v\rho}\,\langle u (x_{A},\omega)u(x_{B},\omega)\rangle \tag{1}\]  
where \(G(x_{A},x_{B,}\omega)\)  
We use a 2D forward model of a passive source to illustrate the process of extraction of the virtual shot records of the reflections by the passive source. We established a model of velocity as shown in Figure 2a, with a length of 3000 m, a depth of 800 m, and a grid spacing of \(\mathrm{dx}=\mathrm{dz}=10\) m. The lower, left, and right boundaries of the model were the boundaries of absorption, and we set 31 absorption layers. The upper boundary was a free boundary. The model contained three geological horizons with velocities of 1000, 2500, and 4000 m/s from top to bottom. A receiver was arranged after every 10 m on the surface, with a total of 301 receivers. There were 2000 sources of random noise (Figure 2b), and 60 s of random noise records were obtained through forward modeling (Figure 3). The source was made to continuously shake during acquisition, and its amplitudes and phases were completely random. Each of the channels of random noise was related to the other 300 channels. That is, one channel of the seismic record of a virtual source was chosen as the source, and the others were used as receivers (Figure 4a). The signal-to-noise ratios (SNRs) of the near-offset data in the red box in the figure were high. The profile of migration was obtained, as shown in Figure 4b, by calculating all 301 respective channels, using the conventional method of processing seismic data.  
Figure 1: Schematic diagram of reflection interferometry.  
Figure 3: Random noise records lasting 60 s.
## 2 Results and Discussion
### Candidate Genes Involved in the Biosynthesis of Triterpenoid Saponins
#### 2.2.1 Terpenoid Backbone Biosynthesis
Two units of FPP join in a \"tail-to-tail\" fashion, catalysed by squalene synthase (SS), to yield the hydrocarbon squalene. Subsequently, squalene is oxidised by squalene monooxygenase (SM) with the cofactors O\({}_{2}\) and NADPH to give rise to another important precursor, 2,3-oxidosqualene. We found two IDI genes (Unigene3767, Unigene3833), three GPS genes (Unigene837 and CL7170.Contig1 and 2), four FPPS genes (CL2187.Contig1 to 4), seven GGPPS genes (CL4542.Contig1, CL6970.Contig1, Unigene10539, Unigene11301, Unigene17743, Unigene27823 and Unigene8778), three SS genes (CL3649.Contig1 to 3) and seven SM genes (CL3649.Contig1 to 3, Unigene14310, Unigene15274, Unigene18579 and Unigene1988) in the _I. asprella_ transcriptome (see Table S3).
## 2 Materials and Methods
### Binding Agents  
Lime was used in this study as the primary stabilising agent. Lime (4% by dry weight of soil) was used to stabilise the soil. This quantity of lime is in keeping with the range of the optimum amount required at least for the development of the correct pH environment and that can lead to the replacement of any available exchangeable cations of clays by calcium, as well as the possible precipitation of ettringites (2016; 2017). A fixed amount (8% calculated by dry weight of soil) of GGBS was used in the stabilised soil mix. This proportion of GGBS is within the range suggested to eliminate the deleterious effects of ettringite in the case  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Oxide Composition** & **Kaolinite** & **Lime** & **GGBS** \\ \cline{2-4}  & **(\%)** & **(\%)** & **(\%)** \\ \hline SiO\({}_{2}\) & 49.00 & 1.80 & 34.10 \\ Al\({}_{2}\)O\({}_{3}\) & 36.00 & 0.7 & 13.00 \\ Fe\({}_{2}\)O\({}_{3}\) & 0.75 & 0.3 & 0.51 \\ CaO & 0.06 & 66.7 & 39.00 \\ MgO & 0.30 & 0.60 & 9.50 \\ K\({}_{2}\)O & 1.85 & 0.01 & 0.50 \\ TiO\({}_{2}\) & 0.02 & - & 1.30 \\ Na\({}_{2}\)O & 0.10 & 0.20 & 0.30 \\ SO\({}_{3}\) & – & 0.30 & 0.30 \\ Mn\({}_{2}\)O\({}_{3}\) & – & - & 0.70 \\ LOI & 12.00 & 28.79 & 1.90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of materials.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Property** & **LL** & **PL** & **PI** & **SC (\%)** & **CC** & **SG** & **MDD** & **OMC** & **USCS** \\ \cline{2-6}  & & & **(\%)** & **(\%)** & & **(kN/m3)** & **(\%)** \\ \hline Value & 58 & 30 & 28 & 74 & 26 & 2.6 & 15 & 17 & CL \\ Standard & ASTM D4318-17 (2017) & ASTM D422-63 (2007) & ASTM D854-10 (2010) & ASTM D1557-12e1 (2012) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of soil.  
of deformation or swelling especially when compared to the quantity of lime used [11]. The chemical compositions of these binding agents are shown on Table 1. Particle size distribution of the materials used are given in Figure 1.