esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Surface Properties and Surface Area of Activated Carbon Pellets  
An SEM analysis was conducted to determine the differences in the surface characteristics of the pellet, which was a mixture of activated carbon, distilled water, binder, and CMC at a certain ratio. The pellet ratio was the ratio of distilled water, binder, and CMC weight to activated carbon, which was mixed at a ratio of 7:1:0.1. Unlike the conventional activated carbon SEM images, the pores of the pellet containing the binder and CMC were confirmed to be blocked owing to the addition of the adhesive and CMC, which penetrated between the pores of the activated carbon and blocked the pores. For the same reason, a BET analysis was also conducted as it affected the specific surface area owing to clogging of the pores.  
A BET analysis was conducted to determine the difference in the specific surface area between the activated carbon and pellets. A BET analysis was conducted to determine the difference in the specific surface area between the activated carbon and pellets, and the results are presented in Table 3 and Figure 5. Figure 5 shows the adsorption isotherms for each sample, which confirm the presence of both micropores and mesopores. As shown in Table 3, the specific surface area of the pellets decreased from 3330 m\({}^{2}\)/g to 2073 m\({}^{2}\)/g. In addition, the percentage of micropores decreased from 86.5% to 85.2%. This indicates that the number of micropores decreased whereas the number of mesopores or macropores increased. The micropores at 2 nm and mesopores at 2 and 50 nm were compared for further analysis. To directly compare the specific surface areas of micropores and mesopores, the results for each were separated, and these results are presented in Table 4. The specific surface area of the micropores decreased by approximately 1657 m\({}^{2}\)/g from 2272 to 615 m\({}^{2}\)/g, whereas that of the mesopores increased by 73.79% (to 1427 m\({}^{2}\)/g). These results confirmed that the binder and CMC penetrated the micropores and clogged them in the activated carbon.
## 5 Conclusions  
We propose an automatic signal separation method for separating the 2-D mixed circular fringe patterns and considering the possible phase reversal condition. The proposed method utilizes the fractional Fourier transform to search for the best fractional orders so that the peak values corresponding to the single circular fringe pattern can be observed in the spectral domain. A filtering scheme is used to determine the precise center position of each fringe pattern. Then, the separated fringe pattern can be reconstructed based on the determined center position and maximum width. We have also demonstrated that both the digital and optical mixed fringe patterns can be successfully separated and most of the reconstructed signals have high correlation coefficients compared to the original ones. In addition, the proposed method can successfully separate the mixed fringe patterns with additive Gaussian noise when the SNR values is as high as 7 dB.  
In our future work, the sampling rate effects, and the minimum detectable fringe size of the proposed method, will be investigated. In addition, we will extend the capability of proposed method, so that the fringe pattern with arbitrary constant phase shift \(\varphi_{0}\) other than 180\({}^{\circ}\) can be detected as well.  
Developed the theoretical formalism, performed the analytic calculations and performed the numerical simulations, H.-T.C. and T.-Y.L.; set up the optical experiments for generating the optical fringe pattern, C.-H.C. and C.-Y.C. ([PERSON]); contributed to the writing, review, and editing of the manuscript, H.-T.C., C.C.H., [PERSON]-Y.C. ([PERSON]), and [PERSON]-Y.C. ([PERSON]); supervised the project and is in charge of the funding acquisition, [PERSON]Y.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the \"Intelligent Recognition Industry Service Center\" from The Featured Areas Research Center Program within the framework of Higher Education Sprout Project by Ministry of Education (MOE) in Taiwan.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# The Carbon Sequestration Potential of Degraded Agricultural Land in the Amhara Region of Ethiopia
## Results
### Temporal Development of Carbon Sequestration by Land Use Type and Thinning Scenarios  
Next, we were interested in the carbon sequestration potential of the identified reforestation areas according to the three land use types including the determined soil carbon and nitrogen starting conditions at the time of reforestation.  
For each grid point (0.083\({}^{\circ}\)-9 km by 9 km), we simulated the carbon accumulation over 150 years. As starting conditions for soil carbon and nitrogen, we used the grid point specific initial carbon values as determined by the procedure described in Section 2.3.3. The simulations involved the three thinning scenarios. The mean development of soil and aboveground carbon of the 81 simulation points in the bare land area, the 135 points within the grass land, and 160 grid points of shrub land area is shown in Figure 3. Soil carbon increase over 150 years could be shown to be highest on bare land, but to start from the lowest initial soil carbon values of about 27 t C ha\({}^{-1}\), as opposed to an average initial soil carbon across all grass land and shrub land grid points of about 59 t C ha\({}^{-1}\) (Table 1). The development of aboveground carbon shows a slower development on bare land and fast growth for shrub land and especially grass land (see Figure 3d-f). In contrast to the soil, the impact of the thinning intervention is visible as a direct drop of aboveground carbon immediately after each thinning and at a lower aboveground carbon stock after 150 years with increasing thinning intensity.  
Figure 3: The simulated development of gross soil carbon (**a–c**) and gross aboveground carbon (**d–f**) over 150 years after reforestation under different management scenarios (unthinned = **a,d**; thinning = 1 = b,e; and thinning 2 = c,f), aggregated per land use type from all simulation points (Bare land n = 81, Grass land n = 135, Shrub land n = 160).  
Figure 2: (**a**) Aridity index and (**b**) potential reforestation area in the Amhara Region. Climate data (0.083\({}^{\circ}\) resolution) for deriving the Aridity index came from [PERSON] et al. [48].
# Ensemble-Learning-Based Prediction of Steel Bridge Deck Defect Condition
## 4 Results and Discussion
### Best Model  
According to Section 4.2, the best-performing model among the established ensemble learning models is XGBoost. To better reflect the superiority of ensemble learning models, this paper compares the XGBoost model with other traditional machine learning models. The most widely applied K-nearest neighbor (KNN) and support vector machine (SVM) models were chosen. Table 11 shows the evaluation results of the KNN and SVM models on the test set, and it can be seen that the XGBoost model performed well. The XGBoost model was compared with the KNN model and showed a 13% improvement in accuracy, a 12% improvement in the AUC, and a 7% improvement in the F1-score. The XGBoost model was compared with the SVM model and showed a 25% improvement in accuracy, the AUC improved by 13%, and the F1-score improved by 14%. Collectively, it seems that the ensemble learning model demonstrates significant performance improvements over the traditional machine learning models used for steel bridge deck defect condition prediction problems. In addition, the XGBoost model developed in this study was compared with the results of previous related studies, and the comparison results are shown in Table 12, from which it can be seen that the model developed in this study performed well.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{5}{c}{**Evaluation Metrics**} \\ \cline{2-6}  & **Accuracy** & **AUC** & **Recall** & **Precision** & **F1-Score** \\ \hline RandomForest & 0.9446 & 0.8999 & 0.9772 & 0.9647 & 0.9709 \\ ExtraTree & 0.9431 & 0.8881 & 0.9765 & 0.9638 & 0.9701 \\ AdaBoost & 0.8220 & 0.8768 & 0.8252 & 0.9841 & 0.8977 \\ GBDT & 0.9471 & 0.8902 & 0.9835 & 0.9615 & 0.9724 \\ XGBoost & 0.9495 & 0.9026 & 0.9994 & 0.9498 & 0.9740 \\ LightGBM & 0.9459 & 0.8928 & 0.9833 & 0.9605 & 0.9717 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation results on the ensemble learning model test set.  
Figure 4: Evaluation results of the ensemble learning models on the test set.  
In this study, to further test the performance of the best model, the trained model was applied to a new dataset to explore the generalization performance. The data from the 2020 and 2019 NBI databases were first processed as in Section 3.1 and then predicted using the trained XGBoost model in this study; the results are shown in Table 13. As shown in Table 13, the XGBoost model achieved good accuracy on both new datasets, indicating that the established XGBoost model has good generalization performance. In summary, the XGBoost model developed in this study can be used as an accurate method to predict the condition of steel bridge deck defects.
###### Abstract  
This study investigates the effects of basalt-polypropylene fibers on the compressive strength and splitting tensile strength of concrete and calculates the fractal dimension of the pore structure of concrete by using a fractal model based on the optical method. Test results reveal that hybrid fibers can improve the compressive strength and splitting tensile strength of concrete, and the synergistic effect of the hybrid fibers is strongest when the contents of basalt fiber (BF) and polypropylene fiber (PF) are 0.05% each, and that the maximum increments in compressive strength and splitting tensile strength are 5.06% and 9.56%, respectively. The effect of hybrid fibers on splitting tensile strength is greater than on compressive strength. However, hybrid fibers have adverse effects on mechanical properties when the fiber content is too high. The pore structure of basalt-polypropylene fiber-reinforced concrete (BPFRC) exhibits obvious fractal characteristics, and the fractal dimension is calculated to be in the range of 2.297-2.482. The fractal dimension has a strong correlation with the air content and spacing factor: the air content decreases significantly whereas the spacing factor increases with increasing fractal dimension. In addition, the fractal dimension also has a strong positive correlation with compressive strength and splitting tensile strength. Therefore, the fractal dimension of the pore structure can be used to evaluate the microscopic pore structure of concrete and can also reflect the influence of the complexity of the pore structure on the macroscopic mechanical properties of concrete.  
Keywords: basalt fiber; polypropylene fiber; hybrid fiber-reinforced concrete; mechanical properties; pore structure; fractal dimension +
Footnote †: journal: Applied Physics Letters
# Resilience in the Internet of Medical Things: A Review and Case Study
## 4 Key Capabilities for Resilience in IoMT
### Traffic Classification  
Traffic classification is another important capability of resilient IoMT networks. It is a process that categorizes the data transmission flow based on certain criteria, such as the level of QoS required for an efficient end-to-end communication [32] and also the level of security or privacy that is required in a communication channel [33]. Furthermore, a specific type of protocol (TCP or UDP) is required for packet transmission [34]. Many articles in the literature divide traffic classification into different categories. Ref. [35] utilizes traffic classification in a QoS-aware traffic classification (suitable for apps that require high QoS). Ref. [36] applies traffic classification in an elephant flow-aware traffic classification (suitable for data centers). Furthermore, ref. [37] utilizes application-aware traffic classification (suitable for the top 80 apps in the Google Play Store).
## 3 Experiments  
### Details of the Datasets  
In order to verify our methods, we conducted experiments on two datasets: Market1501 [28] and CUHK03 [50]. For the latter, we specifically used a version with a clear query-test separation [51]. We conducted experiments on both labeled (L) and detected (D) versions of CUHK03. Table 3 provides an overview of the datasets.  
Whereas Table 3 shows that Market1501 is significantly larger than CUHK03, the latter provides a considerably bigger challenge due to poor illumination conditions, which results in different persons looking similar. In addition, CUHK03 is considered to be one of the most challenging re-ID datasets by certain researchers [35]. This would explain the lower performance of our and other methods on CUHK03 when compared to Market1501.  
We also inspected SCHP-generated parsing masks of all the queries and marked samples where the parser committed considerable mistakes. Figure 4 illustrates typical parsing errors, where a parsing mask is obviously incorrect, or a significant number of subregions are mislabeled. For certain experiments, which are summarized in Tables 4 and 5, we excluded query images that included parsing errors, while labeling the remaining queries as clear queries (cq). We provide the results for both full and clear queries in order to show the effect of parsing errors on the overall accuracy, as well as to better investigate the accuracy of the proposed feature extraction and similarity ranking approaches. It should be noted that parsing errors have not been removed from the test subsets.
## 5 Verification Test
### Transmission Error Test  
As established in Figure 19 below, this test bench adopts high precision circular gratings for measuring transmission error of gear pairs under quasi-static loading, and the circular gratings were installed on the both ends of spiral level gear pair, which can measure the relative angular displacement. We calculated transmission error according to Equation (48):  
\[T\!E=\Delta\varepsilon_{1}-i_{T}\Delta\varepsilon_{2} \tag{48}\]  
where \(T\!E\) is transmission error, \(i_{T}\) is theoretical transmission ratio between input and output gear, and \(\Delta\varepsilon_{1}\), \(\Delta\varepsilon_{2}\) is rotation angle of input and output gear measured by circular gratings during the same period separately.  
The test results of transmission errors after translation are shown in Figure 20 below. We can know that the absolute value and peak to peak of test results are larger than that of FEM results, but the variation trend and fluctuation period of the two are similar.  
Figure 19: Main components of circular grating equipment.  
Figure 18: Results comparison of test and FEM of root bending stress of the gear.  
We can further analyze the difference between test results with simulation results. We define the rotation speed of gear as \(\mathbf{w}_{1}=\mathbf{\tau}_{1}\), and the rotation speed of gear \(\mathbf{w}_{2}=i(t)\mathbf{\tau}_{2}\). \(i(t)\) is instantaneous transmission ratio. Relative speed of gear and pinion in meshing position can be represented as:  
\[\mathbf{v}_{12}=\mathbf{\tau}_{1}\times\mathbf{R}_{2}-i(t)\mathbf{\tau}_{2}\times\mathbf{R}_{1} \tag{49}\]  
For the meshing equation \(\mathbf{v}_{12}\cdot\mathbf{N}_{2}=0\), we can get \(i(t)\):  
\[i(t)=\frac{(\mathbf{\tau}_{2},\ \mathbf{R}_{2},\ \mathbf{N}_{2})}{(\mathbf{\tau}_{1},\ \mathbf{R}_{1},\ \mathbf{N}_{1})}= \frac{(\mathbf{\tau}_{2},\ \mathbf{r}_{2},\ \mathbf{n}_{2})}{(\mathbf{\tau}_{1},\ \mathbf{r}_{1},\ \mathbf{n}_{1})} \tag{50}\]  
We can know from above Equation (50), instantaneous transmission ratio \(i(t)\) changes with contact point changes, so it is not necessarily equal to theoretical transmission ratio \(i_{T}\). When load T is small, the fluctuation amplitude of \(i(t)\) is not obvious, and \(i(t)\) fluctuates around the theoretical value. When load T becomes larger, the fluctuation amplitude of \(i(t)\) increases with the deformation and the contact ratio of gear pair increases. One reason for the difference is that the instantaneous transmission ratio of spiral bevel gear pair is changing, while the test results used the unchanging transmission ratio for calculation. Another reason is that the measurement accuracy and signal interference of circular grating affect the correctness of test results.
## 5 Results and Analysis
### Comparison between Intelligent Segmentation and Manual Segmentation  
To provide a more intuitive comparison of the segmentation performance of the VGG16-UNet model, the tomato plants in the test set were segmented, and the segmentation results of six images were selected for visual display, as shown in Figure 11. Overall, despite the complex and variable growth background, the VGG16-UNet model shows a high consistency between the multi-objective segmentation results of the tomato plants and the manually segmented images. Therefore, it is selected as the default model for the multi-objective segmentation and removal point localization method of tomato plants in this paper.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Evaluation** & **Pretrained** & **Axillary** & **Lateral** & \multirow{2}{*}{**Main Branch**} & **Background** & **Mean** \\  & **Index** & **Weights** & **Buds** & **Branch** & & & \\ \hline \multirow{2}{*}{VGG16-UNet} & IOU & \multirow{2}{*}{False} & 65.69\% & 72.13\% & 84.09\% & 99.31\% & 80.31\% \\  & PA & & 78.59\% & 83.98\% & 91.35\% & 99.65\% & 88.39\% \\ \multirow{2}{*}{VGG16-UNet} & IOU & \multirow{2}{*}{True} & 75.91\% & 78.86\% & 87.01\% & 99.54\% & 85.33\% \\  & PA & & 87.47\% & 89.53\% & 93.15\% & 99.74\% & 92.47\% \\ \multirow{2}{*}{Res-UNet} & IOU & \multirow{2}{*}{False} & 59.58\% & 63.82\% & 80.11\% & 99.10\% & 75.65\% \\  & PA & & 70.80\% & 76.33\% & 87.73\% & 99.61\% & 83.62\% \\ \multirow{2}{*}{Res-UNet} & IOU & \multirow{2}{*}{True} & 67.67\% & 72.23\% & 83.32\% & 99.31\% & 80.63\% \\  & PA & & 80.09\% & 83.56\% & 90.11\% & 99.67\% & 88.36\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the performance of each model.  
Figure 10: Comparison of convergence rate and stability during verification of each model.
## 3 Results
### Reconstruction Results
#### 3.2.2 Training with the Image Domain Loss \(L_{Image}\)
It should also be noted that the differences between image and sinogram domain losses decrease as the sparsity level \(S\) increases, both in terms of the PSNR of the reconstructed CT images and that of the predicted sinograms. Contrary to the gradual decrease in the PSNR, the SSIM increases significantly at \(S=15\).
## 6 Implementation and Evaluation
### Performance Evaluation
#### 6.3.3 Reliability  
Reliability is a key performance indicator. In this subsection we first present the theoretical model for calculating the reliability of the two group communication approaches and then present the results obtained from our simulations.
## 5 Conclusions  
This article evaluated the performance of the OLSR, BATMAN, and Babel proactive routing protocols in a Wireless Mesh Network (WMN) using 11 Raspberry Pi 4 devices, with tests conducted at Al-Farabi Kazakh National University. The assessment covered bandwidth, PDR, and jitter in both indoor and outdoor scenarios.  
In outdoor testing, OLSR demonstrated the highest bandwidth of 2.9 Mbps, outperforming Babel and BATMAN, which achieved 2 Mbps and 1.1 Mbps, respectively. For indoor testing, Babel initially offered the highest bandwidth of 57.19 Mb/s with two nodes but showed a significant decline with increasing nodes, whereas BATMAN and OLSR also experienced reductions, with Babel generally outperforming the other protocols at larger node counts.  
Regarding PDR, BATMAN achieved the best performance with minimal decline, from 100% at two nodes to 42.8% at eleven nodes. OLSR displayed a more stable performance compared to Babel, which experienced the greatest decline, highlighting its sensitivity to network size.  
In jitter testing, OLSR exhibited the most stable performance, with jitter increasing from 0.281 ms to 2.58 ms as nodes increased. BATMAN showed moderate increases in jitter, from 0.334 ms to 2.834 ms, while Babel, starting with the lowest jitter of 0.199 ms, experienced the most substantial rise, reaching 7.28 ms, indicating the highest sensitivity to network size.  
Overall, OLSR proved to be the most stable in terms of jitter, BATMAN excelled in PDR, and Babel, despite its initial bandwidth advantage, displayed significant sensitivity to WMN size. This comprehensive analysis provides valuable insights into the relative strengths and weaknesses of these routing protocols in WMNs.  
Conceptualization, N.U. and A.B.; methodology, A.B.; software, A.A.; validation, A.A. and A.B.; formal analysis, N.U.; investigation, A.B.; resources, S.T.; data curation,  
Figure 9: Jitter of 3 routing protocols.  
S.A.; writing--original draft preparation, S.T. and D.T.; writing--review and editing, D.T.; visualization, A.A.; supervision, X.T.; project administration, N.U. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan under grant AP19674715.  
Data is contained within the article.  
The authors declare no conflicts of interest.
###### Abstract  
To gain insight into the differences between onshore and offshore atmospheric turbulence, pressure fluctuations were measured for offshore wind under different environmental conditions. A durable piezo-electric sensor was used to sample turbulent pressure data at 50 kHz. Offshore measurements were performed at a height of 100 m on Germany's FINO3 offshore platform in the German Bight together with additional meteorological data provided by Deutscher Wetterdienst (DWD). The statistical evaluation revealed that the stability state in the atmospheric boundary does not seem to depend on simple properties like the Reynolds number, wind speed, wind direction, or turbulence level. Therefore, we used higher statistical properties (described by so-called shape factors) to relate them to the stability state. Data was classified to be either within an unstable, neutral, or stable stratification. We found that, in case of stable stratification, the shape factor was mostly close to zero, indicating that a thermally stable environment produces closer-to Gaussian distributions. Non-Gaussian distributions were found in unstable and neutral boundary layer states, and an occurrence probability was estimated. Possible impacts on the laminar-turbulent transition on the blade are discussed with the application of so-called laminar airfoils on wind turbine blades.  
turbulence; super-statistics; piezo-electric flow sensor; ABL stability; laminar-turbulent transition +
Footnote †: journal: Applied Physics Letters