esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Stabilization Using Waste Paper Fly Ash: Precautions for Its Correct Use
## 2 Materials and Methods
### Experimental Methods: Specimen Preparation and Testing
#### 2.2.4 Compressive Strength  
Spanish guidelines for roads and bridges (PG3, 2014) for a stabilized soil type 3 (S-EST3) requires a minimum of 1.5 MPa in compressive strength at seven days, and a minimum binder content of 3 wt %, which is considered as the amount of cement. Additionally, for stabilized soil with WPFA, the considered amount is 5% by weight of dry soil. Preliminary testing showed that using less than 5% WPFA for stabilization appeared to be insufficient to meet the requirements for PG3 (results not presented here).  
To define the optimal mixture, samples were fabricated with different parameters like decreasing the water content, adding different delay times, or curing for a longer period (14 days). Six mixes were proposed to define the optimum mixture. Mix 1 was where the soil is mixed with 5% WPFA and Proctor water content, and cured for 7 days at 20 \({}^{\circ}\)C and 90% humidity. The condition in mix 2 was the same as mix 1, except that the samples were cured for 14 days instead of 7 days. In mix 3, the experiment introduced a delay time to the mix. After mixing the soil with WPFA and water, the mixture was set to rest for an hour. Then, it was poured into the mold and compacted. The curing time was considered to be 7 days. The amount of water was considered as Proctor water content. However, in mix 4, the amount of water was decreased. Finally, in mix 5, the amount of water decreased by one point of the Proctor (same as mix 4); however, instead of a one-hour delay time, a 30 min delay was applied. A control mix with cement also was fabricated to compare the result (mix 6). The mixes are shown in Table 2.  
After defining the appropriate mix, the development of strength was studied by manufacturing more samples using mix 5 and curing them for different ages (7, 28, 60, 180, and 360 days). For comparison, samples were made using cement with mix 6 and cured at the same ages as WPFA samples. For each age, four samples were made. A compressive strength test was conducted using a Toni Technik model 2020 (Germany), in accordance with EN 13286-41 [26].
## 5 Conclusions  
This paper proposes a novel SOS algorithm called the MQSOS algorithm that is based on a quantum state-based inter-group communication strategy. In the implementation process, the algorithm is first divided into several subgroups for independent iterative evolution, and the corresponding group communication is performed after each subgroup iterates to achieve the step size of inter-group communication. The quantum state-based inter-group communication strategy is used to iterate several organisms with better states in each subgroup and replace the organisms with poor states in order to enhance the convergence speed and improve the overall performance of the algorithm due to the increase of diversity. In order to verify the performance of the newly proposed MQSOS algorithm, the CEC2013 test suite was applied to compare the algorithm with other swarm intelligence algorithms. The experimental results indicate that the performance of the MQSOS algorithm was better than those of other intelligent algorithms (PSO, PPSO, original SOS, QUATRE, APSO, and OSOS algorithms). We also applied the algorithm to the location of wireless sensor nodes to form a new DV-hop algorithm called MQSOS_DV-hop, with the aim of improving the accuracy of DV-hop algorithm node positioning, and simulated the MQSOS_DV-hop wireless sensor location algorithm in Matlab. The experimental results show that the MQSOS algorithm had higher accuracy in wireless sensor network node location.  
Figure 6: Comparison diagram of positioning errors between the MQSOS_DV-hop positioning algorithm and other positioning algorithms.  
In the future, we will further study more accurate and efficient evolutionary algorithms, evolutionary programs, and communication strategies to improve the performance and efficiency of social intelligence algorithms. We also need to apply subsequent improved algorithms to different types of application scenarios, such as hierarchical routing, node deployment, clustering methods, and coverage issues in WSNs, in addition to applications in transportation, energy supply, etc.  
Conceptualization, S.-C.C. and J.-S.P.; Formal analysis, Z.-G.D.; Methodology, S.-C.C.; Software, Z.-G.D.; Supervision, S.-C.C. and J.-S.P.; Writing--original draft, Z.-G.D.; Writing--review and editing, J.-S.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.
## 2 Beidou-Binocular Cooperative Navigation Model
### Model Framework and Implementation  
In the design of the Beidou-Binocular Cooperative Navigation Model (BBCN), each submodule (Beidou navigation positioning module, binocular vision recognition and perception module, intelligent collaborative fusion and path planning module) needs to be integrated through a unified framework to achieve seamless fusion of multi-sensor data, real-time path planning, and dynamic obstacle avoidance. This section will describe in detail the system framework design of the BBCN model, the data flow, and the implementation process.
# Polydimethylsiloxane (PDMS)-Based Flexible Resistive Strain Sensors for Wearable Applications
###### Abstract  
There is growing attention and rapid development on flexible electronic devices with electronic materials and sensing technology innovations. In particular, strain sensors with high elasticity and stretchability are needed for several potential applications including human entertainment technology, human-machine interface, personal healthcare, and sports performance monitoring, etc. This article presents recent advancements in the development of polydimethylsiloxane (PDMS)-based flexible resistive strain sensors for wearable applications. First of all, the article shows that PDMS-based stretchable resistive strain sensors are successfully fabricated by different methods, such as the filtration method, printing technology, micromolding method, coating techniques, and liquid phase mixing. Next, strain sensing performances including stretchability, gauge factor, linearity, and durability are comprehensively demonstrated and compared. Finally, potential applications of PDMS-based flexible resistive strain sensors are also discussed. This review indicates that the era of wearable intelligent electronic systems has arrived.  
wearable electronics; strain sensors; flexible substrate; PDMS 2018 8, 345; doi:10.3390/app8030345
## 3 Neural Network-Based Bridge Maintenance Cost Prediction Framework
### Selecting Input Indicators Based on Random Forest  
Random forest is a classification index test method based on decision trees. It is widely applied and achieves good results in the index selection of low feature dimensions and low sample size data [25; 26]. The VIM is used to express the importance of indicators, and the Gini index [27], which represents the probability that a randomly selected sample in the sample set has been classified by mistake, is used to calculate the VIM value.  
Suppose that there are \(n\) indicators \(X_{1}\), \(X_{2}\), \(X_{3}\)... \(X_{n}\), the Gini variable importance measure of each indicator \(X_{j}\) is calculated and represented by \(VIM_{j}^{(Gini)}\), which represents the average change of node splitting impurity in the \(j\)-th variable in all decision trees in the random forest [28]. The detailed calculation process of \(VIM_{j}^{(Gini)}\) is as follows:  
1. Calculate the Gini index of the node \(m\) in a certain category \(K\). The formula for calculating the Gini is: \[GI_{m}=\sum\limits_{k=1}^{K}P_{mk}^{\cdot}\big{(}1-P_{mk}^{\cdot}\big{)}\] (1) where \(K\) is the number of categories in the sample set and \(P_{mk}^{\cdot}\) is the estimated probability that the sample of node \(m\) belongs to the \(k\)-th category; when the sample belongs to one of categories, \(K=2\).
2. The decision trees in the random forest used in this study only had two branches. Therefore, the Gini index of node \(m\) could be calculated as: \[GI_{m}=2\hat{P_{m}}\big{(}1-\hat{P_{m}}\big{)}\] (2) where \(\hat{P_{m}}\) is the estimated probability that the sample belongs to any category at node \(m\).
3. Calculate the VIM value of node \(m\). The importance of indicator \(X_{j}\) at node \(m\); that is, the variable quantity in the Gini index before and after the branch at node \(m\) is: \[VIM_{jm}^{(Gini)}=GI_{m}-GI_{l}-GI_{r}\] (3) where \(GI_{l}\) and \(GI_{r}\) represent the Gini indexes of two new nodes split by node \(m\).
4. Calculate the VIM value of indicator \(X_{j}\) in the random forest. If the indicator \(X_{j}\) appears \(M\) times \(i\)-th in the tree, the importance of the indicator \(X_{j}\) in the \(i\)-th tree is: \[VIM_{ij}^{(Gini)}=\sum\limits_{m=1}^{M}VIM_{jm}^{(Gini)}\] (4) The VIM of the indicator \(X_{j}\) in the random forest is defined as: \[VIM_{ij}^{(Gini)}=\frac{1}{n}\sum\limits_{i=1}^{n}VIM_{ij}^{(Gini)}\] (5) where \(n\) is the number of decision trees in the random forest.
## 3 Implementation Details and Experimental Results
### Experimental Environment and Experimental Subjects  
All experiments in this study were conducted on a cloud server equipped with NVIDIA GeForce 4090 GPUs, using a CUDA parallel computing architecture within the Pytorch framework to construct the detection model. Python and OpenCV were utilized for data augmentation, and the environment configuration is detailed in Table 2. The optimizer Adam was employed in model training. The initial learning rate was set to \(1\times 10^{-2}\), and the weight decay rate was set to \(5\times 10^{-4}\). The epoch of the training process was set to 250, and the batch size was set to 16. All input images were resized to 640 \(\times\) 640 pixels to conform to the model.  
The model's testing phase involved using a concrete road at Kunming University of Science and Technology to evaluate the accuracy and efficiency of both the crack identification and localization and the image segmentation models, as depicted in Figure 8. For road image acquisition, we utilized a DJI Mavic3 UAV equipped with a Raspberry Pi 4B and a binocular camera, and the shooting parameters are detailed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset Name** & **Number** & **Image Size** & **Example Images** \\ \hline CRACK500 [8] & 500 & 2560 \(\times\) 1440 pixels 2992 \(\times\) 1946 pixels 34 \\ Cracktree200 [34] & 206 & 800 \(\times\) 600 pixels 34 \\ CFD [35] & 118 & 480 \(\times\) 320 pixels 34 \\ AEL [36] & 58 & 311 \(\times\) 462 pixels 34 768 \(\times\) 512 pixels 34 700 \(\times\) 1000 pixels 34 \\ GAPs384 [37] & 1969 & 1920 \(\times\) 1080 pixels 34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on public datasets.  
Figure 7: The dataset annotation method.
# A Soft-Voting Ensemble Classifier for Detecting Patients Affected by COVID-19
## 2 Materials and Methods
### The Proposed Approach  
The pipeline aimed at training and testing both the individual models and the soft-voting ensemble models is graphically represented in Figure 4. Summarizing, its first step is aimed at pre-processing the chest CT dataset. Specifically, the dataset has been pre-processed to uniform the depth of the CT scans and to remove irrelevant contents for the problem in hand. Then, with the aim of reporting an accurate estimate of the models' prediction performance, the dataset has been split to implement a 5-fold cross-validation. Subsequently, five individual models have been independently built, tuning a hyperparameter of the network. To avoid overfitting, an online image augmentation process has been enforced while training the networks. Performances of each model have been independently assessed according to the 5-fold cross-validation. Then, the soft-voting ensemble models have been built using the individual models. Details of these steps are described in the following.
## 3 Results and Discussion
### Microstructure  
3.2.1. Microstructure in the As-Sintered Condition  
Figure 4 shows a light microscopic overview image of a transverse section (a) and a longitudinal section (b) of a specimen after sintering.  
In both cases, increased porosity can be seen, which appears to be randomly distributed. However, very close inspection in details 2 and 3 reveals isolated pore bands in the horizontal direction, as sometimes described for the MBI process in literature [13; 38]. On the bottom side, the porosity seems to be locally increased. Qualitatively, no significant difference between the porosity in the transverse section and the longitudinal section is visible. As a first impression, the visible residual porosity of the specimen is comparable in quantity to sintered components from other processes, such as MIM [39; 40; 41].  
A more detailed examination with SEM in Figure 5 shows that other phases are found in addition to the porosity, that appear as eddy triple-point phases in the image.  
Figure 4: Light optical microscopic overview of the porosity in an as-sintered specimen in (**a**) transverse section and in (**b**) longitudinal section with details 1–4.  
Figure 5: Backscattered electron image of the microstructure of an as-sintered specimen in transverse section in different magnification.  
The etched microstructure in Figure 6 identifies this phase as delta ferrite, as it was predicted by thermodynamic modeling. The amount of delta ferrite was determined by image analysis to be 10 vol.-% in the center of the specimen. The dark grey seam around the delta ferrite could possibly be sigma phase. However, this was not finally proven. Additionally, the appearance of chromium nitrides in the material could not be approved.  
The EDX mapping in Figure 7 reveals the distribution of alloying elements in a randomly picked area of an as-sintered specimen. It can be seen that Al-rich oxides are present at the inner surfaces of pores. Additionally, certain areas are enriched in molybdenum. These are possibly delta ferrite regions. However, seams with even more Mo are visible around the Mo-rich areas. These seams were already visible in the etched microstructure in Figure 6 and might indicate the presence of a sigma phase. Sigma phase in duplex steels usually consists of Fe-Cr-Mo [42].
# A High Throughput Hardware Architecture for Parallel Recursive Systematic Convolutional Encoders
## 3 Results  
### BER Performance Analysis and Implementation Results of some RSC Codes  
In our previous work [16], we showed the \(BER=BER(\frac{E_{b}}{N_{0}})\) curves of some RSC encoders with \(R=\frac{1}{2}\), where \(E_{b}\) is the average energy per bit, and \(N_{0}\) is the power spectral density of a white Gaussian noise process. These curves were produced through a Matlab(r) simulation including:  
* RSC encoder
* Binary phase shift keying (BPSK) modulation
* Additive white Gaussian noise (AWGN) channel
* Soft-viterbi decoder  
Such RSC encoders were synthesized on Zynq 7000 xc7z010 clg400-1 FPGA by exploiting the architecture described in Section 2.3. Table 1 shows the RSC codes chosen and their results in terms of number of maximum clock frequency, input data rate and FPGA sources. To estimate the maximum clock frequency, input and output registers were included as shown in Figure 5, and \(f_{clk}=100\ MHz\) clock constraint was imposed. Such registers are not considered in the reported slice registers results of Table 1.  
Figure 6 shows the BER curves resulting from the Matlab(r) simulation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**L**} & \multirow{2}{*}{**Generators**} & \multirow{2}{*}{**Paral. (k)**} & \multirow{2}{*}{**Puncturing**} & **Code** & \(f_{clk_{MAX}}\) & \(R_{IN}\) & **Slice** & **Slice** \\  & & & & & & & & (MHz) & (Mb/s) & **LUTs** & **Regs** \\ \hline RSC\_1\_1 & 3 & \(\left(1\ \ \ ;\ \ \ \ \frac{1+x^{2}}{1+x^{2}}\right)\) & 1 & No & \(\frac{1}{2}\) & 770.4 & 770.4 & 2 & 2 \\ \hline RSC\_1\_2 & 3 & \(\left(1\ \ \ ;\ \ \ \frac{1+x^{2}}{1+x^{2}}\right)\) & 2 & \(\left[1\,1\,1\,0\right]\) & \(\frac{2}{3}\) & 640.6 & 1281.2 & 2 & 1 \\ \hline RSC\_1\_3 & 3 & \(\left(1\ \ \ ;\ \ \ \frac{1+x^{2}}{1+x^{2}}\right)\) & 3 & \(\left[1\,1\,1\,1\,1\,0\right]\) & \(\frac{3}{5}\) & 648.9 & 1946.7 & 3 & 2 \\ \hline RSC\_2\_1 & 4 & \(\left(1\ \ \ ;\ \ \ \frac{1+x+x^{3}}{1+x^{2}+x^{3}}\right)\) & 1 & No & \(\frac{1}{2}\) & 784.9 & 784.9 & 2 & 2 \\ \hline RSC\_2\_2 & 4 & \(\left(1\ \ \ ;\ \ \ \frac{1+x+x^{3}}{1+x^{2}+x^{3}}\right)\) & 2 & \(\left[1\,1\,1\,0\right]\) & \(\frac{2}{3}\) & 781.8 & 1563.7 & 3 & 3 \\ \hline RSC\_2\_3 & 4 & \(\left(1\ \ \ ;\ \ \ \frac{1+x+x^{3}}{1+x^{2}+x^{3}}\right)\) & 3 & \(\left[1\,1\,1\,1\,1\,0\right]\) & \(\frac{3}{5}\) & 613.1 & 1839.3 & 4 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recursive systematic convolutional (RSC) encoders rate, occupation, code rate properties.  
Table 1 shows that an increment of the parallelism degree leads to augment of the input data rate \(R_{IN}\), but it required a higher number of sources, especially slice LUTs.  
Section 3.2 presents a deeper analysis of the \(R_{IN}[k]\) trend through a case study.
## 5 Conclusions  
Systematic analysis of fresh and irradiated U-Mo dispersion fuel was carried out using APT. Specimens of different burnup were studied to elucidate microstructural changes taking place in U-Mo fuel due to irradiation. The as-received or as-fabricated U-Mo showed a network of \(\gamma\)-U\({}_{2}\)Mo, \(\alpha\)-U phases, and Mo-enriched \(\gamma\)-U-Mo from the parent \(\gamma\)-U-Mo matrix, along with the presence of U-C phase along the grain boundaries with uniform uranium enrichment across all phases. However, irradiation led to predominantly Mo-enriched \(\gamma\)-U-Mo phase with the presence of the bubble superlattice structures enriched with fission products. This work is the first of its kind in providing an in-depth chemical analysis on the localization of fission products formed in LEU metallic fuel irradiated to different burnups using APT. The local distribution of the bubble superlattice structures observed in APT complement previous TEM findings. The measured content of fission products increased with the increase in the burnup, as expected from fuel behavior. Mass spectrum from both samples showed that the bubbles are decorated with solid fission products along with some trace amounts of gaseous Xe elements. This study revealed the chemical identity of the fission products, which can provide fundamental insight on fission products' formation in metallic fuels at high burnup.  
Conceptualization, M.B. and J.G.; methodology and data analysis, M.B.; writing--original draft preparation, M.B.; writing--review and editing, M.K.M., J.G., D.K., S.V.d.B., A.L., and B.M.; funding acquisition, M.B. All authors have read and agreed to the published version of the manuscript. This work was supported through the INL Laboratory Directed Research and Development (LDRD) Program under DOE Idaho Operations Office Contract DE-AC07-05 ID14517. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data can be accessible subjected to the review process at INL. Acknowledgments: The authors would like to acknowledge the support of staff members ([PERSON], [PERSON], [PERSON]) and the LEAP facility at the Center for Advanced Energy Studies--Microscopy and Characterization Suite (CAES-MaCS). The authors would also like to acknowledge the support of MFC staff ([PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]) for their support during sample transfer between different facilities at INL.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Speckle Filter Correlation with Soybean Yield  
When statistically examining the filters for the _VH_ index from the first harvest it is evident that the Refined Lee filter stands out as the most divergent compared to the other filters, as also shown in the correlation between filters (Table 5). The Refined Lee filter introduces scattered black pixels into the images, making them noisier. Although statistically robust, studies by [43] comparing the effectiveness of speckle noise filters demonstrated that the Refined Lee filter is more effective for detecting small, distinct features, making it less suitable for homogeneous crop cover. Therefore, for studies focused on crop yield, particularly in soybean fields where the vegetation cover is uniform and lacks sharp variations, the Refined Lee filter is not the optimal choice.  
When correlating pixel values using _VH_ across the development stages among the speckle noise filters, significant similarities were observed between the filtering algorithms. The Boxcar, Gamma, and Lee filters show correlations close to 1, suggesting no significant distinction in their effects on yield relationships (Table 5). This high correlation between Gamma, Lee, and Boxcar filters may be attributed to their adaptive nature, relying on different methods of local pixel distribution within the window, thus preserving image edges [44]. Nevertheless, in crops such as soybean, which present a homogeneous appearance with few abrupt variations, the influence of the filters is not significant in the analyses. Therefore, as there are no significant differences in choosing between these filters, the study proceeded with the Boxcar filter, since it is the simplest mathematically among the others [45] and is commonly used in agricultural studies [23; 46].  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Harvest 1** & **Harvest 2** & **Harvest 3** \\ \hline Stage 1 & 0.27 & 0.51 & \(-\)0.34 \\ Stage 2 & 0.41 & 0.50 & \(-\)0.17 \\ Stage 3 & 0.54 & \(-\)0.25 & 0.14 \\ \hline \end{tabular}
\end{table}
Table 4: Spearman correlation coefficient between EVI values and yield data at the three different growth stages and three harvests. Significant correlations at 5%.  
Figure 5: Spearman correlation coefficient between SAR data and soybean yield, including harvest, growth stages, speckle noise reduction filters, and SAR variables. Significant correlations at 5%.
## 5 HTS Tapes Studies Using Scanning Electron Microscope
### Measuring the Thickness of HTS Tape Layers  
Figure 10 shows sample photos of the SF12100-CF tape control sample with a 4 \(\upmu\)m silver layer, with the thicknesses of the individual HTS tape layers marked. Analogous measurements were made for the remaining samples. Table 4 presents the results of the layer thickness measurements for the four samples of the tested HTS tapes.  
Based on the measurements, it can be concluded that the thicknesses of the HTS tape layers differ from those given in the product specifications. In the control samples, the silver layers had different dimensions than in the samples subjected to the prospective short-circuit currents--both for the top and bottom layers. The superconductor layers also had different thicknesses. In the control samples, they were almost half as thick. In addition, a large inhomogeneity was observed in the thicknesses of all the layers in the samples.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer:**} & \multicolumn{4}{c}{**Average Thickness of the HTS SF12100-CF tape Layers**} \\ \cline{2-5}  & **4 \(\upmu\)m Ag** & **2 \(\upmu\)m Ag** & **2 \(\upmu\)m Ag** \\  & **(Control Sample)** & **4 \(\upmu\)m Ag** & **(Control Sample)** & **2 \(\upmu\)m Ag** \\ \hline silver & 6.48 \(\upmu\)m & 2.02 \(\upmu\)m & 2.33 \(\upmu\)m & 1.00 \(\upmu\)m \\ (top of the tape) & 1.60 \(\upmu\)m & 3.36 \(\upmu\)m & 2.67 \(\upmu\)m & 4.83 \(\upmu\)m \\ (Hastelloy & 107 \(\upmu\)m & 112 \(\upmu\)m & 107 \(\upmu\)m & 109 \(\upmu\)m \\ silver & 6.74 \(\upmu\)m & 2.21 \(\upmu\)m & 1.38 \(\upmu\)m & 0.98 \(\upmu\)m \\ (bottom of the tape) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average layer thicknesses in the HTS tapes.  
Figure 9: Cross-sectional photos of the HTS tapes taken using an electron microscope: (**a**) control sample; (**b**) sample exposed to prospective short-circuit currents.