esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Electrochemical Sandwich Assay Combining Aptamers with Other Materials  
Another avenue for the use of aptamers in electrochemical sandwich assays involves using other materials and molecules such as molecularly imprinted polymers, other biomolecules such as lectins, or redox-active nanomaterials. A summary of these assays is presented in Table 4. An electrochemical biosensor for the reliable detection of the amyloid-\(\beta\) oligomers was developed by using molecularly imprinted polymers (MIPs) and aptamer as the recognition element by forming an MIPs/target/aptamer sandwich assay [90] as shown in Figure 9. The A\(\beta\)-specific aptamer was immobilized on the surface of 200 nm SiO\({}_{2}\) nanoparticles decorated by a shell of smaller Ag nanoparticles using HS- aptamerar via Ag-S bonds to yield the SiO\({}_{2}\)@Ag-aptamer bioconjugate. An MIP biosensor for A\(\beta_{1\text{-}42}\) was prepared by using a template of chitosan on a glassy carbon electrode onto which AuNPs and reduced graphene oxide had been deposited. Graphene oxide (GO) and gold nanoparticles (AuNPs) were used to improve the electrical conductivity and surface-to-volume ratio. Differential pulse voltammetry was used to detect the oxidation of the Ag nanoparticles. The LOD for A\(\beta_{1\text{-}42}\) oligomers was 1.22 pg mL\({}^{-1}\) with the linear range of detection 5 pg mL\({}^{-1}\) to 10 ng mL\({}^{-1}\). The detection range of this biosensor covers much of the physiological concentration range for A\(\beta\) oligomers in patients, which has been reported to be in the range of 1.25-12.5 ng mL\({}^{-1}\). Lectins used along with DNA aptamers have been explored as molecular recognition elements for glycoproteins. A biosensor for carcinoembryonic antigen (CEA) was developed based on Concanavalin A (Con A) and a DNA aptamer. CEA was detected with a linear range of 5-40 ng mL\({}^{-1}\) and with a limit of detection of 3.4 ng mL\({}^{-1}\), lower than the threshold level in human serum for cancer patients of 10 ng mL\({}^{-1}\)[91]. The CEA target was selectively captured on the gold electrode surface by DNA aptamers. Con A served as the bridge between CEA and HRP (as an enzyme label) as the affinity of ConA was verified with both CEA and HRP, as both are glycoproteins. Differential pulse voltammetry (DPV) currents were measured in the presence of hydroquinone and hydrogen peroxide.
## 4 Wheel Flat Identification Based on Acceleration of Axlebox
### VMD-ES Analysis Results under 350 km/h  
This section presents the results of the simulated axlebox acceleration by using the VMD-ES method described in Section 3. The analysis condition includes 0-50 mm length of flat at 350 km/h.  
Figure 7a-f correspond to the length of the flat varying from 0 to 50 mm, respectively. The analysis frequency range is 0 Hz\(\sim\)600 Hz. Black dot marks represent the impact frequency of the wheel flat which is 33.6 Hz, and its relationship with the speed of vehicle is in Equation (10). Red dot marks represent interference signals unrelated to the wheel flat. According to the relationship between the length of fastener and train speed (as shown in Equation (11)), the frequency of the fastener support is 155.6 Hz which is consistent with the frequency of red dot marks. The results show that the impact frequency of the wheel flat and the frequency of the fastener support both appear in the identification results with the small flat length. As there is an increase of the flat length, the amplitude of interference frequency caused by fastener support gradually decreases. Meanwhile, the peak values in ES increase monotonically with the length of flat. It is worth noting that the irregular low amplitude frequency in ES is mainly caused by the short-wave excitation of the track irregularities especially in fault-free condition, as shown in Figure 7a. Therefore, the VMD-ES method is sensitive to the wheel flat impact and can extract the characteristic frequency of the wheel flat from the strong interference signals, which ensures the accurate identification of wheel flat.  
\[f=\frac{v}{2\pi R} \tag{10}\]  
\[f=\frac{v}{L} \tag{11}\]  
where \(v\) is the speed of train, \(L\) is the length of fastener, and \(R\) is the wheel radius.  
Figure 7: _Cont._
## 1 Introduction
When radiation consisting of photons of wavelength lower or equal to 387 nm reaches the surface of the photocatalyst, an electron-hole pair (\(\mathbf{e}_{\text{(CB)}}^{-}\), \(\mathbf{h}_{\text{(VB)}}^{+}\)) is generated due to the promotion of electrons from valence band (VB) to conduction band (CB) [23], as shown in Equation (1):  
\[\text{TiO}_{2}+\text{hv}\to\text{TiO}_{2}+\mathbf{e}_{\text{(CB)}}^{-}+ \mathbf{h}_{\text{(VB)}}^{+} \tag{1}\]  
In the photocatalytic process, the molecules oxidation is caused mainly by \(\bullet\)OH (with a redox potential of 2.8 eV), superoxide (O\({}^{2\cdot-}\)), and hydroperoxide (HO\({}_{2\cdot}\)) radicals [24]. The \(\bullet\)OH redox potential is higher than that generated by the holes (h\({}^{+}\)) produced in the VB of the photocatalyst (2.53 eV) [25,26]. Reactive species can degrade pollutants until mineralization, thus producing non-toxic substances [27]. Radical \(\bullet\)OH is capable of degrading most organic and organometallic pollutants by transforming them into CO\({}_{2}\), water, and inorganic ions [28]. In the past, recalcitrant substances have been successfully degraded by means of photocatalytic processes [29]. Different types of reactors can be used for wastewater treatment through heterogeneous photocatalysis. They are all based on the use of a semiconductor, which receives UV radiation from either a UV lamp or solar radiation [30]. Flat plate reactors (FPR) using natural solar radiation present several advantages, especially with their low manufacturing and maintenance costs. This type of technology, which works with natural solar radiation, can be applied particularly in countries in the planet's sunbelt given that they receive an average annual solar irradiation of 5.5 kWh/m\({}^{2}\)/day [31]. The objective of the present work was to optimize the degradation of AMX in an aqueous solution by means of photolysis and heterogeneous photocatalysis with the use of sunlight as activation energy, using flat plate reactors (FPR) with surfaces of 0.1 and 1 m\({}^{2}\)and TiO\({}_{2}\) P25 as a photocatalyst for the reaction. This work also studied the influence of the addition of H\({}_{2}\)O\({}_{2}\) (0 and 4 mM) on the AMX degradation reaction as a function of different pH values (3.5, 4.15, 7, and 9) through a kinetic study and ANOVA.
## 3 Experiments and Discussions
### Visualization of Dimensionality Reduction  
One direct way to evaluate the effectiveness of the dimensionality reduction technique is to visualize the low-dimensional data. As is widely known, the original data points are distributed in a high-dimensional manifold space, which is difficult to visualize and observe. One of the primary goals of dimensionality reduction algorithms is to identify the most significant feature dimensions within the data and map the samples according to these dimensions, so called principle components. Once the dimensionality of the data points is reduced, the retained features represent the principal components of the dataset.  
By examining the distribution of samples along these principal component dimensions, we can clearly observe the variations in the key discriminated features of the samples.  
Therefore, FMDR is employed to reduce the dimensionality of the image dataset, compressing it into two dimensions and visualizing the results. As shown in Figure 5, when the samples of the ORL dataset are compressed to two dimensions by FMDR, \"facial orientation angle\" and \"eye closure degree\" are recognized as the two major feature dimensions. Samples can be effectively distinguished and categorized along these two dimensions.  
Similarly, we utilize FMDR to compress the dimensions of the YaleB dataset to two dimensions as illustrated in Figure 6. It can be observed that, \"size of shadow coverage area\" and \"angle of the shadow\" are considered to be the most discriminative major components that effectively identify every sample.  
Figure 5: The dimensionality of the samples from the ORL dataset is reduced to two dimensions using FMDR. The original image of the selected sample circled in red is presented nearby. Two feature dimensions identified by FMDR are “facial orientation angle” and “degree of eye closure”. Along the \(x\)-axis, the degree of eye closure of the samples increases gradually from right to left. From bottom to top on the \(y\)-axis, facial orientation changes from right to left.  
Figure 6: The dimensionality of the samples from the YaleB dataset is reduced to two dimensions using FMDR. The original image of the selected sample circled in red is presented nearby. Two feature dimensions identified by FMDR are “size of shadow coverage area” and “angle of the shadow”. Along the \(x\)-axis from left to right, the shadow coverage area decreases from large to small. Along the \(y\)-axis from bottom to top, the angle of the shadow changes from right to left.  
In summary, FMDR effectively identifies the principal component features of data while reducing the dimensionality of samples, thus providing an accurate low-dimensional representation.
# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
## 4 Proposed Method
### Stacked-LSTM
#### 4.2.3 Source Signal Waveform Recovery  
The 1-D deconvolution is used to perform 512-dimensional feature decoding on the separated communication source signal encoding to obtain a one-dimensional time-domain waveform:  
\[\mathbf{s}^{*}=\mathbf{h}_{decoder}\big{(}\mathbf{x}_{sep}\big{)} \tag{14}\]  
In the formula, \(\mathbf{h}_{decoder}(\cdot)\) is the decoder.
## 4 Optimization Process
### Accuracy of Response Surface  
Root mean square error (RMSE) and coefficient of determination (R\({}^{2}\)) were used to evaluate the regression models. Both methods are generally used to assess the correctness of a regression model, and the formula is as follows [22]:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}} \tag{11}\]  
\[\text{R}^{2}=\frac{\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{N}(y_{i}- \overline{y_{i}})^{2}} \tag{12}\]  
where \(y_{i}\) is the function value of an experimental point's response variable, \(\hat{y_{i}}\) is the function value of the regression model and \(N\) represents the number of design points for the regression model evaluation.  
Figure 7 schematically shows the goodness of fit for the three regression models. Each figure (circle, diamond) for the two regression models is a generalized distribution for four output variables (see Table 5). The \(x\)-axis and \(y\)-axis denote normalized test and predicted data, and it can be judged that the prediction is better the more it matches the x = y line. It can be judged that the NPR method better predicts the data. Based on this result, the regression model to be used for optimization was selected as NPR.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 3 Simulation Results and Analysis
### Area  
A specific layout technique was employed to ensure recovery from all possible cases of DNU and MNU. In a DNU, if sufficient charge is injected at nodes (S1-S0) and (S1-S3), the stored value may flip. Therefore, the drains of the NMOS and PMOS transistors that could cause an SNU were separated by at least 1.62 \(\upmu\)m [16; 17]. Due to this arrangement, the proposed cell is resistant to all potential cases of soft errors. Of the 20 transistors used, 16 are sized at the minimum dimensions allowed by the fabrication process. However, due to the use of the largest number of transistors among the compared cells, the proposed design has the largest area, measuring 28.28 \(\upmu\)m\({}^{2}\).
## 1 Introduction
They gave multiple examples where combining just two models can significantly improve the accuracy, with significant implications for improving the performance of the models in downstream tasks. Google has further proposed the GreedySoup weighting strategy for improving model accuracy [21]. Instead of selecting a single fine-tuned model that achieves the highest accuracy on the validation set, this approach combines multiple independent models, then adjusts the model weights; this strategy was also shown to be effective in improving the accuracy of the models. In summary, E3W proposed in this paper is obtained by combining several models that perform well in different categories of the data and adjusting the weights of the models, which provides a feasible and effective modelling approach. In comparative experiments with 13 models, the four models that performed best in different categories of the data set were selected; these four models were ERNIE, ERNIE + DPCNN, EGC, and Word2 Vec + TextCNN. Glove essentially involves the dimensionality reduction of a matrix; however, certain attributes are naturally lost in the process of dimensionality reduction. As agricultural text titles are inherently short, have limited information, and cover many categories, this can easily lead to some information being lost through dimensionality reduction, making it difficult to correctly classify. Based on the above reasons, we chose to combine word2 vec with TextCNN. The E3W model proposed in this paper first combines these four models, and then uses the GreedySoup weighting strategy to weigh the outputs of the four sub-models differently. The category with the highest calculated weight value is selected as the final output. Comparative experiments on a Chinese agricultural news data set demonstrate that E3W can achieve the most advanced results. The experimental validation analysis proved the effectiveness of the model in the classification of Chinese agricultural news. The main contributions of this paper are as follows:  
1. We propose the E3W model, which combines several sub-models and adopts the GreedySoup weighting strategy to adjust the model, achieving the best classification performance on a Chinese agricultural news data set to date;
2. By combining multiple sub-models, we solve the problem wherein traditional models cannot achieve the highest classification accuracy in all categories;
3. Applying the GreedySoup weighting strategy to combinatorial models solves the problem of weight assignment when multiple models are combined, and provides a solution to similar problems in other fields  
The remainder of this paper is structured as follows: Section 2 presents information on the four used sub-models. Section 3 describes the structure and operational procedures of the E3W model proposed in this paper. Section 4 describes the set of experiments conducted, as well as discussing and analyzing the obtained results. Finally, in Section 5, we discuss the strengths, weaknesses, and outlook of the proposed approach.
## 4 Experimental Results
### The Influence of Various Parameters
#### 4.2.2 The Influence of the Physical Speed Limitation  
The rotation speed of the driving wheel has physical limitations. However, previous studies did not analyze the influence of the speed constraint on the actual trajectory tracking process. Therefore, we deliberately added the speed constraint to the novel-type iterative learning controller to study the impact of the physical speed limitation on the trajectory tracking process. The specific controllers were as follows:  
**Situation 7:** Hybrid iterative learning controller without speed limitation:  
\[L_{1}^{s7}(k)=0.1\cdot L_{1}(k);\quad L_{2}^{s7}(k)=0.25\cdot L_{1}(k);\quad L _{3}^{s7}(k)=0.25\cdot L_{1}(k); \tag{51}\]  
**Situation 8:** Hybrid iterative learning controller with speed limitation:  
\[\left\{\begin{array}{c}L_{1}^{s8}(k)=0.1\cdot L_{1}(k);\quad L_{2}^{s8}(k)=0.25\cdot L_{1}(k);\quad L_{3}^{s8}(k)=0.25\cdot L_{1}(k);\\ u_{cr}(k)<u_{\max},ucl(k)<u_{\max}\end{array}\right. \tag{52}\]  
**Situation 9:** Hybrid iterative learning controller without speed limitation:  
\[L_{1}^{s9}(k)=1\cdot L_{1}(k);\quad L_{2}^{s9}(k)=0.25\cdot L_{1}(k);\quad L _{3}^{s9}(k)=0.25\cdot L_{1}(k); \tag{53}\]  
**Situation 10:** Hybrid iterative learning controller with speed limitation:  
\[\left\{\begin{array}{c}L_{1}^{s10}(k)=0.5\cdot L_{1}(k);\quad L_{2}^{s10}(k )=0.25\cdot L_{1}(k);\quad L_{3}^{s10}(k)=0.25\cdot L_{1}(k)\\ u_{cr}(k)<u_{\max},u_{cl}(k)<u_{\max}\end{array}\right. \tag{54}\]  
We used the above controller law to describe the tracking result and error of the specified trajectory. Comparing the above controllers, we found the following:  
1. Comparing Figure 7a2,c2, the speed solved by the single controller does not meet the physical limitations; however, adding the speed constraint to the controller can meet the physical limitations of the mobile platform.
2. Comparing Figure 7a3,b3 or Figure 7c3,d3, the controller with a velocity limitation can reduce the number of system iterations required and improve the convergence speed of the system.
3. Comparing Figure 7a2,a3,b2,b3 or Figure 7c2,c3,d2,d3, it can be found that the control signal is not smooth with the speed constraint since the control speed at the next moment can be reached considering the speed limitation. However, the velocity limitation can reduce the tracking error to significantly enhance the performance of the controller.
# Power Allocation and User Grouping for NOMA Downlink Systems
## 3 Resource Allocation
### Research Limitation  
In this paper, we assume that under ideal conditions there is no SIC detection error, and the multi-carrier NOMA system is a combination of OFDM and NOMA, without considering the inter-carrier interference. The DL algorithm proposed in this paper needs iterative training, so its complexity is slightly higher than that of FTPA and FPA. Our main goal is to maximize the sum rate of the system, so complexity is not covered.
## 2 Materials and Methods
### Determination of the Minimum Size of the Training Set  
At the second stage of the experiment, our objective was to observe how the performance of a model varies with the number of training data and in turn, to determine the minimum size of training set to achieve the best performance of the model. At first, all the reports (n(R) = N) of each type were divided into two groups: training pool (A) and test set (B). The test set was selected randomly and it consisted of 25% of all the reports of that type. The rest of the reports were included in the training pool. Using the best feature sets derived from the first stage of the experiment, multiple models were built by using variable number of training set from the training pool. Figure 1b shows all these steps for training set size j where:  
* N = total number of reports of a type
* n(A) \(\approx\) 0.75N = number of reports in the training pool
* n(B) \(\approx\) 0.25N = number of reports in the test set
* n(A\({}_{k}\)) = j = 25, 50, 100, 200,......, n(A) = number of reports in each training dataset \(\mathrm{k}\) = 1, 2, 3,......, n(A)/j = number of iterations for each training dataset, \(\mathrm{A}_{k}\).  
For each type of report, we first selected varying size of training set where the size was denoted by j and j = 25, 50, 100, 200,......, n(A). For each value of j, a total of n(A)/j training sets (\(\mathrm{A}_{k}\)) were created where the size of each training set, n(\(\mathrm{A}_{k}\)), was j and the sets were mutually exclusive. For each training set, \(\mathrm{A}_{k}\), a model was created using the best feature set and the performances of all such models were evaluated using the same test set (B) which was already set aside. The mean F\({}_{1}\)-score of all the models, for each training set size j, was selected as the metric to evaluate the performance for that training set size. Finally, variation of the models' performances with the varying size of training set were investigated.
## 3 Thermal Image Segmentation and Classification
### Classification System Testing and Results  
To validate the ability of the trained Kohonen network to segment the thermal image, a system for its evaluation was designed as follows. Five artificial spheres, each with an area of 150 pixels, were created, each of which fell into only one of the classes. As can be seen in Figure 17, the network was able to correctly classify these artificial anomalies in this particular case.  
In order to properly evaluate the functionality of the classifier, five testing infrared images were divided into 60 segments. The artificially created spherical temperature field anomaly for each class was moved across all segments, giving a total of 300 testing images for each class and 1500 testing images in total. The division of the image into segments when the artificial anomaly was included is shown in Figure 18.  
The results are summarized in the confusion matrix shown in Table 2; the first row represents the real class of the anomaly and the first column represents the class output by the network. The contingency table gives a clear picture of the network's ability to correctly segment the infrared images.  
The network achieved a very good accuracy for the first two classes, and only misclassified objects for the three hottest classes. The lowest obtained accuracy was for the classification of class N\({}_{5}\), which was the coldest class and mostly represented the surroundings of the engine, which are of a low temperature. This class also contained misclassified objects belonging to class N\({}_{4}\), which only represented relatively cold parts of the engine or its surroundings. The most important classes for the evaluation of the technical state of a turbojet engine are those which represent its hottest parts,  
Figure 16: The size \(S_{i}\) of each segmented area for five example points.  
Figure 17: Thermal anomalies correctly classified by the Kohonen network.  
that is, N\({}_{1}\), N\({}_{2}\), and N\({}_{3}\). The results could probably be improved by using a larger training dataset or different Kohonen layer topologies, however they can nevertheless be evaluated as very successful for the purpose of turbojet engine diagnostics.