esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 New GTR and GBR Using Tissue Engineering
### Biomaterial Scaffold for Bone Cell Infiltration
#### 3.3.1 Natural Polymers: Collagen, Alginate, Chitosan, and Hyaluronic Acid
3.3 Bioceramics: Hydroxyapatite (HA), Bioactive Glass, \(\beta\)-Tricalcium Phosphate (\(\beta\)-TCP)  
Bioceramics are inorganic biomaterials which are well documented as bone fillers in dental applications [98]. Calcium phosphate bioceramics are moldable, easy to handle, and they get hardened in situ, which enables them to adapt in complex defects [99]. Also, they have bioactivity, excellent biocompatibility, hydrophilicity, similarity to native bone inorganic components, osteoconductivity, and potential osteoinductivity [99] This potential activity is possible with a surface of bioceramics which absorbs and exhibits osteoinductive factors or via the gradual release of calcium and phosphate ions into nearby tissue, stimulating the differentiation of osteoblasts [100]. HA is the most common ceramic of TE and improves the adhesion and proliferation of osteoblasts with bone-like mineral components [100]. However, the crystalline form of HA showed a slow degradation rate, and it is relatively brittle when bearing weight [101]. Methods to improve the mechanics, such as adding other compounds, sintering, and using amorphous HA which has a faster degradation rate, were introduced [101; 102; 103]. Bioactive glass is a silicon oxide with substituted calcium [98]. It forms a calcium phosphate layer in the surface when exposed to body fluid, and its biocompatibility and surface pores allow tissue in-growth [104]. \(\beta\)-TCP is widely used due to its ability to form a strong bone-calciumphosphate bond and its faster degradation rate [105]. Interestingly, when \(\beta\)-TCP is combined with HA, a mixture termed biphasic calcium phosphate (BCP) is produced [106]. In comparison to other calcium phosphate ceramics, BCP has significant advantages including controlled bioactivity and stability, while promoting bone in-growth, especially in large bone defects; BCP also has a higher degradation rate than HA, but a slower one than \(\beta\)-TCP [106]. Even bioceramics have ideal properties for their use as scaffold materials, and they are very brittle due to their high stiffness and low flexibility [107]. Thus, their combination with supporting materials such as PLLA and PLGA was tried and the results showed improved mechanical properties and osteogenic potential [108].
## 2 The SASW Method
### The Data Processing of SASW Method  
The SASW analysis typically includes three major stages. Firstly, repeated field tests are conducted by changing the different distances of two receivers to collect the field-measured data. Secondly, the filtering process is used to eliminate the unexpected signals that have a low-quality phase spectrum [35], and the representative dispersion curve, i.e., a function of frequency and phase velocity, is built on the basis of the phase difference in the cross-power spectrum obtained by the measured field signals in the frequency domain. Once the two receivers collected the surface wave signal in the time domain, a fast Fourier transform would be performed to obtain the frequency domain data. By extracting the phase difference from the phase information contained in the spectrum signal, the wave velocity and its corresponding frequency can be calculated. Finally, the third and most challenging stage is the inversion analysis.  
The purpose of inversion analysis is to obtain the shear wave velocity profile (the plot of wave velocity versus depth) of the measured site from the dispersion curve using some complicated optimization algorithms, such as the maximum likelihood method, the dynamic stiffness matrix method, and the transfer matrix method [36]. The inversion procedure is a nonunique and nonlinear problem that cannot be solved directly. Most of the inversion techniques depend on the iterative calculation from a reasonable initial guess of the stiffness profile to the final solution. After that, the elastic modulus can be calculated from the linear elastic theory due to the minimal strain (less than 0.001%) generated in the measurement [11], as shown in the following equation:  
\[E=2\cdot\rho\cdot V_{s}^{2}(1+\mu) \tag{1}\]  
where \(\rho\) represents the density of the pavement material, \(\mu\) is the Poisson's ratio, \(V_{S}\) represents shear wave velocity, and \(E\) is the elastic modulus.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 4 WFO Design
### Calculate Priority
The priority of the nodes in \(T_{\mathcal{L}_{k}}\) can be calculated using Equation (8). \[P_{i}=1+\sum\limits_{j=1}^{n}\alpha_{i,j} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Node** & \(\mathbf{N_{2}}\) & \(\mathbf{N_{7}}\) & \(\mathbf{N_{8}}\) & \(\mathbf{N_{9}}\) & \(\mathbf{N_{10}}\) & \(\mathbf{N_{11}}\) \\ \hline \(N_{2}\) & 0 & 1 & 1 & 1 & 1 & 1 \\ \(N_{7}\) & 0 & 0 & 0 & 0 & 1 & 1 \\ \(N_{8}\) & 0 & 1 & 0 & 0 & 1 & 1 \\ \(N_{9}\) & 0 & 1 & 1 & 0 & 1 & 1 \\ \(N_{10}\) & 0 & 0 & 0 & 0 & 0 & 0 \\ \(N_{1 where \(P_{i}\) is the priority of node \(i\) in \(\mathcal{L}_{k}\). \(n\) denotes the number of edge nodes. The constant \(1\) is set for all nodes with a priority greater than \(0\). Through calculation, we can determine the size of each node's priority and generate the final data forwarding link \(\mathcal{L}^{\prime}_{k}\). Because of the high complexity of priority calculation, we propose a heuristic priority calculation algorithm (HPC), Algorithm 2. The priority calculation time is affected by the number of nodes in the queue. The time increases exponentially with the increase of the number of nodes. The priority calculation runs depending on the number of nodes taken from several hours to a few days to produce the optimal solutions. In the experiment, when the number of nodes in a queue is \(14\), the solution time of the optimal approach took \(1\) h and \(28\) min. The algorithm determines the final path by iteratively finding the node with the shortest delay as the next hop. For a sequence of ECs \(\mathcal{L}_{k}\), when \(i=1\), lines \(3\)-\(7\) are executed \(n\) times. When \(i=1\), lines \(3\)-\(7\) are executed \(n-1\) times. Therefore, if there are \(n\) ECs, the total number of cycles of HPC is \((n+(n-1)+\cdots+1)=(n^{2}/2+n/2))\) time. If the algorithm is computed on the CC, the time complexity of HPC is \(O(n^{2})\), where \(n\) is the number of ECs. Actually, line \(3\)-\(7\) can be evaluated by the ECs. ```
Input:\(\mathcal{L}_{k}\) Output:\(\mathcal{L}^{\prime}_{k}\)
1\(n\leftarrow\)The number of nodes in \(\mathcal{L}_{k}\)
2 for\(1\leq i\leq n\)do
3 for\(j\in\mathcal{L}_{k}\)do
4 if\(j\in\mathcal{L}^{\prime}_{k}\)then
5 Delete node \(j\) from \(\mathcal{L}_{k}\)
6 else
7 Calculate the delay of node \(i\left(\widetilde{\tau}_{i,j}+\widehat{\tau}_{i}\right)\)
8
9\(\mathcal{L}^{\prime}_{k}\leftarrow\)The node with the minimum delay
10 return\(\mathcal{L}^{\prime}_{k}\)
```  
**Algorithm 2**Heuristic priority calculation
###### Abstract  
From the viewpoint of vibration control, if the amplitude of the main frequencies of the vibration response can be reduced, the vibration energy of the structure is greatly reduced. Modal parameters, including modal shapes, natural frequencies, and damping ratios, can reflect the dynamics of the structure and can be used to control the vibration. This paper integrates the idea of \"forgetting factor weighting\" into eigenvector recursive principal component analysis, and then proposes an operational modal analysis (OMA) method that uses eigenvector recursive PCA with a forgetting factor (ERPCAWF). The proposed method can identify the transient natural frequencies and transient modal shapes online and realtime using only nonstationary vibration response signals. The identified modal parameters are also suitable for online, real-time health monitoring and fault diagnosis. Finally, the modal identification results from a three-degree-of-freedom weakly damped linear time-varying structure shows that the ERPCAWF-based OMA method can effectively identify transient modal parameters online using only nonstationary response signals. The results also show that the ERPCAWF-based approach is faster, requires less memory space, and achieves higher identification accuracy and greater stability than autocorrelation matrix recursive PCA with a forgetting factor-based OMA.  
Keywords:operational modal analysis; principal component analysis; weighted forgetting factor; online recursive eigenvector; vibration control +
Footnote †: journal: Applied sciences
# A Roadmap to Integrate Digital Twins for Small and Medium-Sized Enterprises
## 4 Discussion
### RQ1: Design of the Communication Framework
This application would serve to build a more comprehensive DT model and DTH where data from all stages would be collected to form a single source of truth. The users of such data would be able to make well-informed decisions, be adaptive to unforeseeable circumstances and yield higher productivity. Grafana was the visualisation tool used in this article to display key temperature readings and current state of the 3D model simulation (and as a result, the physical twin). The customisable dashboard grants users the ability to project data on a cloud-based platform, monitor events and recognise opportunities for operational improvement. Similar to Nodered, Grafana is both open-source and low cost, fitting the software criteria for SMEs.
## 5 Experiments
### Experimental Results
#### 5.3.1 Comparison with Baseline Models  
Baseline models can be divided into two categories, non-NN-based methods and NN-based methods. The non-NN-based methods include K-nearest neighbor (K-NN), weighted K-NN, GBDT regression, and linear regression. The NN-based methods include MLP, LSTM, and LSTM-IDW. The details of the baseline models are as follows.  
* **K-nearest neighbor (K-NN) regression:** The idea of the K-NN regressor is to predict the value by using the nearest monitoring stations as references. In this study, we set the nearest neighbors to three (i.e., k = 3). That is, the predicted value was calculated by using the average scores of the three nearest stations.
* **Weighted KNN regression:** The weighted K-NN predicts the target values by using the inverse-distance-weighted [26] average scores of the nearest stations. That is, a closer station will have a greater influence on the predicted value of the target station. In this study, we used k = 3 as the setting of the K-NN regressor.
* **Gradient Boosting Decision Tree (GBDT) regression:** GBDT is a state-of-art tree-based model that captures complex cross features [27].
* **Linear Regression:** A linear model that assumes a linear relationship between the input variables and the output variable.
* **Multilayer Perceptron (MLP):** For the MLP model, we concatenate all features on all timestamps as input. The MLP model has two layers.
* **LSTM:** For the LSTM model, we concatenated all static features with every time step of time-series data as input features. The LSTM model contains two LSTM layers and one dense output layer.
* **LSTM-IDW:** The LSTM-IDW model is a combination of the LSTM model and the IDW method. We use LSTM to make an AQI prediction of the labeled grids using their historical data first, and then use IDW interpolation to infer the future predictive values of unlabeled grids based on the prediction values of LSTM.  
Table 1 is the overall comparison between baseline models and our proposed model. Our model outperforms baselines particularly in long-term prediction. The results of LSTM-based models are worse than other methods, for the reason that is also the challenge of our target: the lack of historical air quality data of unlabeled grids makes it difficult for LSTM to make predictions.  
In addition, the error of LSTM-IDW is slightly smaller than IDW and K-NN, and LSTM-IDW performs much better than LSTM. LSTM-IDW is mostly affected by the accuracy of the first stage predicted method before interpolation, and the limitation of IDW interpolation's calculating spatial correlation still influences the overall performance. From the results, our proposed model is able to calculate more critical spatial-temporal correlations and performs better than comparative baselines with the same input features.
## 3 Results and Discussion
### Short-Term Material Exposure

Table 3: Method validation parameters. desorption was observed in the case of PVC. However, the estrogen sorption on PVC was insignificant (\(p<0.05\)). The extent of estrogen sorption on the other materials was found to be similarly low (less than 20%). The overall desorption of estrogens from these materials was lower than 2% of the initial concentration, even after 90 min soaking in clean deionized water. Materials such as PVC, PVDF and PTFE cut into the columns (13 \(\times\) 100 mm) have been tested by other authors [5] and found suitable for estrogen handling. These authors found that after 24h contact with PTFE and PVC, only minimal amounts of E2, EE2 and E1 were adsorbed. They also observed that some portion of E1, E2 and EE2 was adsorbed on PTFE (0.2 \(\upmu\)m) and PVDF (0.45 \(\upmu\)m) membrane filters, but the differences were lower than 20%, thus insignificant (\(p<0.05\)). In contrast, [PERSON] and colleagues [27] found unexpectedly significant adsorption of estrone onto PTFE membrane filters. In two different experiments, this team demonstrated that physical sorption is involved in the case of estrone. First, they observed that sorption increases with the pore size of the PTFE membrane (80 and 42% for 0.1 and 0.45 \(\upmu\)m pore size, respectively), which is related to the sorption surface. Then, they also supported this theory with an experiment where increasing the amount of filtered solution led to saturation of the studied material, and estrogen was no longer adsorbed onto the membrane. Although both teams worked with similar concentrations (0.5 and 0.4 mg/L, respectively) and filters from the same manufacturer, their results were  
Figure 2: Screening of the relative estrogen sorption (**a**) after 30 min of incubation of six polymeric materials in the deionized water with estrogens (E1, E2, EE2 and E3) and the relative cumulative estrogen desorption (**b**) after 30, 60 and 90 min incubation of the contaminated materials in clean deionized water. The initial concentration (c0) of all estrogens was 100 ng/L. Data represent the mean \(\pm\) standard deviation of relative concentrations (related to initial concentration). Asterisks mark significant differences detected using ANOVA and Dunn test at \(p<0.05\). opposed. Additionally, working with membranes or small areas of tested materials in combination with such high estrogen concentrations does not reflect the conditions in environmental samples. All tested plastic materials are commonly used in laboratories, the chemical industry or food production, including, e.g., water quality control processes. Sorption of estrogens to them can cause potential losses and thus underestimate real estrogen concentrations in the analysed samples. On the other hand, the desorption into the samples can elevate at least the background level and cause discrepancies during analyses, especially in samples with concentrations close to the limits of detection/quantification.
# Optimal Estimation Retrieval of Aerosol Fine-Mode Fraction from Ground-Based Sky Light Measurements
## 2 Model and Methods
### Methodology
#### 2.3.1 OE inversion Method
The L-BFGS-B algorithm is a highly effective tool in bounded minimization problems with the requirements of a priori knowledge,cost function, and the gradient of the cost function. When the results after multiple iterations satisfy the condition of \(\|\mathbf{x}^{\mathrm{P}+1}-\mathbf{x}^{\mathrm{P}}\|\leq\varepsilon\), the iterative process is terminated, and an optimized estimate of \(\mathbf{x}\) is obtained, where \(\varepsilon\) is the threshold for convergence.
## Appendix C Details about Wind Farm Proxy  
Let the canonical basis of the plane be \((e_{1},e_{2})\) with \(e_{1}=(1,0)\in\mathbb{R}^{2}\) and \(e_{2}=(0,1)\in\mathbb{R}^{2}\). We denote by \(R_{\alpha}\) the rotation matrix \(\begin{bmatrix}\cos\alpha&-\sin\alpha\\ \sin\alpha&\cos\alpha\end{bmatrix}.\) The formula of the wind farm proxy function is given below for a wind in the direction of \(e_{1}\):  
\[F(\{\mathbf{x}_{1},...,\mathbf{x}_{n}\})=\sum_{i=1}^{n}\biggl{(}\prod_{j,j \
eq i}f_{p\theta}(\mathbf{x}_{j},\mathbf{x}_{i})\biggr{)}f_{0}(\mathbf{x}_{i }). \tag{10}\]  
\(f_{0}\) is supposed to be constant, and \(f_{p\theta}(\mathbf{x}_{j},\mathbf{x}_{i})\) expresses the penalty factor on the yield of the turbine at \(\mathbf{x}_{i}\) that is caused by a turbine at \(\mathbf{x}_{j}\). When the direction of interaction coincides with the x-axis, the interaction loss between rotors at \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) is  
\[f_{p\theta}(\mathbf{x}_{j},\mathbf{x}_{i})=\left\{\begin{array}{ll}1&\text {if}&x_{i}\leq x_{j}\,\\ (\frac{||\mathbf{x}_{i}-\mathbf{x}_{j}||}{1+||\mathbf{x}_{i}-\mathbf{x}_{j}|| }f_{L}(\mathbf{x}_{j},\mathbf{x}_{i})+\frac{1}{1+||\mathbf{x}_{i}-\mathbf{x}_ {j}||}f_{\theta}(\mathbf{x}_{j},\mathbf{x}_{i}))f_{prox}(\mathbf{x}_{j}, \mathbf{x}_{i})&\text{if}&x_{i}>x_{j}\.\end{array}\right. \tag{11}\]  
Before explaining \(f_{L}\), \(f_{\theta}\) and \(f_{prox}\), we mention that the two terms of \(f_{p\theta}\) are weighted with non-fixed functions depending on the distance between the two points. \(f_{L}\) corresponds to a power gain increasing with the distance between rotors and having a maximum of 1. Its expression is the following:  
\[f_{L}=\frac{1}{1+\exp(-p_{1}(\Delta-l))}\,\]  
In regards to the second term, we have  
\[f_{\theta}(\mathbf{x}_{j},\mathbf{x}_{i})=\left\{\begin{array}{ll}0&\text {if}&\mathbf{x}_{i}=\mathbf{x}_{j}\\ 1&\text{if}&x_{i}=x_{j},\mathbf{x}_{i}\
eq\mathbf{x}_{j}\\ \frac{2}{\pi}\arctan(\frac{|y_{i}-y_{j}|}{|x_{i}-x_{j}|})&\text{otherwise}\\ \end{array}\right. \tag{12}\]  
The component \(f_{\theta}\) quantifies the gain depending on the angle \(\overrightarrow{\mathbf{u}},\overrightarrow{\mathbf{x}_{i}\mathbf{x}_{j}}\), with \(\overrightarrow{\mathbf{u}}\) the direction of interaction. The result is multiplied with a third function in order to further penalize the proximity between turbines. We have  
\[f_{prox}(\mathbf{x}_{j},\mathbf{x}_{i})=\frac{1}{1+\exp(-p_{2}(\Delta^{\prime }-radius))}\]  
with \(p_{2}\), \(radius\) positive parameters and \(\Delta^{\prime}=\|\mathbf{x}_{i}-\mathbf{x}_{j}\|\). We can notice that all the terms of Equation (12) are between 0 and 1, which results in \(f_{p\theta}\in[0,1]\). In order to implement a function modeling the effect of a wind in the direction of \(R_{\alpha}e_{1}\) (with \(\alpha\in(0,2\pi)\)), we compute the coordinates of the inputs on the basis \((R_{\alpha}e_{1},R_{\alpha+\pi/2}e_{1})\) and apply the last function on the new inputs (with the new coordinates). The values of \(p_{1}\), \(p_{2}\) for the threefunctions defined above are, respectively, 0.15 and 0.5. We choose \(l=10\) and \(radius=3\) for \(F_{0}\) and \(F_{45}\).
## 4 Experimental Results
### Experiment Analysis
#### 4.2.3 Real Data  
Accidents  
In order to study the effect of the support level on execution time ratios, we varied the support levels from 45% to 25% for the FDCNB-MINING and FLR-mining algorithms in six different simulation cases of the network bandwidth environment. The data [19] covered traffic accidents in the Flanders region of Belgium for the period from 1991 to 2000. The number of traffic accidents, number of different attribute values, and average length of each accident were 340,183, 572, and 45, respectively. Table 1 shows the network bandwidth environment of the six simulation cases. The results are shown in Figure 4. We found that the FDCNB-MINING algorithm had a good performance in the unstable network bandwidth environment and was superior to the FLR-mining algorithm. The detailed execution time ratios are listed in Table 4. In the experiment, the proportion of execution time of  
Figure 3: Effect of varying support from 0.01% to 0.05% on proportions of execution time in FDCNB-MINING and FLR-mining.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Support (\%)** & **0.05** & **0.04** & **0.03** & **0.02** & **0.01** \\ \hline Case 1 & 1.0013 & 1.0138 & 0.9960 & 0.9927 & 0.9904 \\ Case 2 & 0.3978 & 0.3713 & 0.3738 & 0.3957 & 0.3414 \\ Case 3 & 0.8857 & 0.8801 & 0.9093 & 0.9048 & 0.8976 \\ Case 4 & 0.8934 & 0.8720 & 0.8885 & 0.8001 & 0.8284 \\ Case 5 & 0.8163 & 0.7814 & 0.7776 & 0.8078 & 0.7255 \\ Case 6 & 0.8678 & 0.8108 & 0.7655 & 0.7668 & 0.6603 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Proportions of execution time for FDCNB-MINING/FLR-mining while varying support from 0.01% to 0.05% and Case1 to Case 6.  
FDCNB-MINING to that of FLR-mining also decreased with the decrease in support threshold from 45% to 25% for most of the cases, demonstrating that the proposed FDCNB-MINING was more scalable than FLR-mining. The average proportion of execution time of FDCNB-MINING to that of FLR-mining was about 74%.
# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 3 Experimental Results  
### Implementation Details  
We generated patches for three classes (i.e., MSI-L, MSI-H, and normal tissue) by preprocessing the training dataset. To avoid the class imbalance problem, classes with fewer patches were oversampled by allowing replacement. Eighty percent of the patches were used for training and the remaining were used for cross-validation. To avoid overfitting, we applied different data augmentations to the patches, including horizontal and vertical flips, random rotations, and crops.  
For the segmentation network, we used FPN [34], which was robust in multi-scale object detection. In this study, we used EfficientNets [38] as the encoder backbone network because it is more accurate and efficient compared to other convolutional networks such as VGG16 [21] and ResNet [22]. For the classification network, we selected Inception-Resnet  
Figure 3: Two-stage classification pipeline for microsatellite instability (MSI) status prediction.  
V2 because it showed the best performance for breast cancer classification compared to VGG19, Inception-V3, and Inception-V4 [37]. The segmentation and classification networks were trained using the same data, prepared as described in Section 2.2. We chose a dice loss for the segmentation network to consider the imbalance of the dataset. For the classification network, we used a cross entropy as a loss function, which is commonly used for such a task; the imbalance in the dataset still exists; however, it was dealt with oversampling. Table 1 represents the F1-score results when different optimizers and learning rate schedulers were implemented. Based on the preliminary results, the Adam optimizer was used with an initial learning rate of 1e-4 and a step decay scheduler for both networks.  
The models were implemented in PyTorch using the Python library for segmentation [39]. We trained the network for 100 epochs with a batch size of 64 using NVIDIA Tesla V100 GPU. The network was trained using 47 training datasets and tested with another 10 validation datasets, comprising five MSI-L and five MSI-H WSIs.