esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 4 FE Analysis for Reproduction on Beam Loading Experiment
### Outline of Analysis Model  
Figure 7 shows the outline of the analysis models. Considering the symmetry of the beam, a quarter-cut model was used for analysis. To more precisely grasp the phenomena caused by differences in interlock conditions, the shapes of the threaded rebar and the anchoring nuts at beam end were reproduced using steel solid elements. Two-dimensional joint element was placed at the boundary between the steel element for main rebar and the concrete element. Thus, chemical adhesion and frictional resistance on smooth surface between steel and concrete were considered in the property of joint element. As the effect of interlock can be directly reproduced by normal stress between the concrete element and lung shaped steel element, it was possible to separate out the interlock effects from other bond components which are reproduced by the joint element.  
In the previous researches, FE analytical models for bond between rebar and concrete could be divided mainly into two approaches: induction of bond-slip behavior between rebar and concrete [22; 23; 24; 25], and averaged stress and averaged strain behavior as reinforcement concrete [26; 27; 28]. The both approaches were developed based on pull-out behavior of rebar from concrete, and stress transfer behavior between rebar and concrete was modeled including multiple bond factors: interlock, chemical adhesion, and friction. In this study, the authors reproduced interlock effects explicitly by reproducing the geometry of rebar, not an embedded truss or beam rebar model or a distributed fiber model for rebar.
# Tailored Nanoparticles as Vaccine Components
## 3 Types and Uses of Nanoparticles in Vaccines
### Liposomes  
Liposomes for vaccination purposes are prepared from biodegradable and nontoxic phospholipids with a reverse-phase evaporation process by dissolving phospholipids in an organic solvent, adding water and antigen, evaporating the solvent, and breaking up the resulting lipid aggregates into liposomes of defined size by sonication or by passage through filters, or both. Alternatively, they can be formed by dissolving lipids in a detergent and then removing the detergent by absorption (e.g., onto hydrophobic beads) or dialysis (if the critical micelle concentration is sufficiently high) [11; 19; 24]. Liposomes can encapsulate hydrophilic antigens into their aqueous core, and their membranes can incorporate viral envelope glycoproteins to form virosomes [9; 19; 24]. Inflexal(r) V for influenza [41] and Epaxal(r) for hepatitis A [42] are such virosome systems that are approved for human use.  
For the delivery of nucleic acid vaccines by liposomes, polycations such as polyethylene or cationic lipids can, on the one hand, help to condense the nucleic acid inside the respective liposome and, on the other hand, disrupt the cellular endosomal membranes, leading to more efficient delivery [9; 19; 24]. Such polycations can be combined with a calcium phosphate core that may contain the nucleic acid as a co-precipitate [43]. As they function as messenger RNA (mRNA) protectors and carriers, such cation-containing liposomes have been used in the development of SARS-CoV-2 vaccines by BioNTech/Pfizer and Moderna companies [31].  
As a synthetic alternative to phospholipids, liposomes from amphiphilic block copolymers (ABCs) were studied for the delivery of a DNA vaccine against the Zika virus (ZIKV). Therefore, plasmid DNA encoding a ZIKV antigen was enveloped with tetrafunctional ABCs made of four amphiphilic diblocks that were centered on an ethylenediamine moiety. Delivery was effective in a variety of cell and tissue types such as heart, lung, and muscle [27].
## 4 Experimental Evaluation
### Experimental Results and Analysis
#### 4.2.2 Time Overhead Evaluation  
In this paper, time overhead refers to the total computational time required to complete the entire federated learning process, measured in seconds. It includes the time for local model training on devices, model parameter transmission between devices and edge nodes, local aggregation at edge nodes, and global model aggregation at the central server.  
From Figure 7(a), it is evident that FedAvg consistently maintained the highest time cost across all rounds, with FedAsync showing the second-highest time cost. FedAsyncISL performed better than FedAsync, and compared to the above three methods, the PPFL-SAGIN had the lowest time cost in all rounds. Figure 7(b) indicates that FedAvg had the highest median time and a wider interquartile range (IQR), suggesting greater variability. FedAsync had a median time of approximately 195-200 s with a moderate IQR. FedAsyncISL had a median time of approximately 185 s with a moderate IQR. The PPFL-SAGIN had the lowest median time with a relatively compact IQR, indicating more stable performance. The box plot confirmed that the PPFL-SAGIN was not only faster but also exhibited more stable performance compared to other algorithms.  
We compared the time overhead of the four methods based on the MNIST dataset and the UC Merced Land Use dataset, with the specific results shown in Figures 8 and 9.  
Figure 9 compares the time overhead of the four methods over 10 rounds based on the UC Merced Land Use dataset. From Figure 8(a), it is clear that FedAvg had the highest overall time overhead, slightly higher in the first round and rounds 7-10. FedAsync had the second-highest time overhead, showing a decreasing trend in the early rounds and a slight increase in the later rounds. FedAsyncISL had the third-highest time overhead, exhibiting a more obvious \"U-shaped\" trend. The PPFL-SAGIN had the lowest time overhead, with a trend similar to that of FedAsyncISL. As shown in Figure 8(b), FedAvg had a median time overhead of approximately 425 s with a relatively small fluctuation range. FedAsync had a median time overhead of 398 s with moderate fluctuation. FedAsyncISL had a median time overhead of approximately 374 s with a relatively compact distribution. The PPFL-SAGIN had a median time overhead of approximately 344 s with the smallest fluctuation. Overall, compared to the other three methods, the PPFL-SAGIN had the lowest time overhead and good stability.
## 4 Conclusions  
All the three heavy metals could be effectively removed by acclimatized _S. pasteurii_, and the Ca\({}^{2+}\) and urea concentration both played critical roles in removing the heavy metals. In the absence of Ca\({}^{2+}\), when the heavy metal concentration ranged from 50 to 800 mg/L, increasing urea concentrations in the nutrient broths was beneficial to the removal of Cd\({}^{2+}\) with a removal ratio of 89.9-99.7% at Curea = 2.0 mol/L, but had little positive effect on the removal of Cu\({}^{2+}\) and Pb\({}^{2+}\) with a removal ratio of 62.6-64.4% and 71.4-97.6%, respectively at Curea = 0.5 mol/L. After introducing Ca\({}^{2+}\) in the nutrient broths, heavy metal could coprecipitate with Ca\({}^{2+}\), resulting in a much more effective removal of heavy metals, and the removal efficiency of Cd\({}^{2+}\), Cu\({}^{2+}\) and Pb\({}^{2+}\) could reach 98.0-99.0, 78.1-82.1 and 98.0-100.0%, respectively, by adjusting the concentration of Ca\({}^{2+}\) and urea. The overall removal efficiency for Cd\({}^{2+}\) and Pb\({}^{2+}\) was better than that for Cu\({}^{2+}\) in aqueous solutions. The Cu\({}^{2+}\) deposit aggregated and cemented to form clusters, which was different from Cd\({}^{2+}\) and Pb\({}^{2+}\) deposits with relatively dispersed microstructure. For all the three heavy metal precipitates, the predominant mineral was identified as calcite, the most thermodynamically stable polymorph of CaCO\({}_{3}\). The heavy metals may be adsorbed on the cell surface, wrapped in calcite structure, or filled in the defect vacancy of CaCO\({}_{3}\) crystal when coprecipitated with Ca\({}^{2+}\), resulting in the not easily found XRD diffraction patterns.  
**Author Contributions:** Conceptualization, Y.W. and X.L.; methodology, Y.W.; validation, Y.W., J.T. and X.L.; formal analysis, X.L.; investigation, K.L.; data curation, X.L. and K.L.; writing--original draft preparation, X.L.; writing--review and editing, Y.W. and J.T.; supervision, Y.W.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 51678311.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We would like to express our sincere gratitude for the financial support of the National Natural Science Foundation of China (grant 51678311) to this study. We also sincerely thank the reviewers for their helpful and constructive suggestions and the editors for their careful and patient work.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Springback Prediction of Free Bending Based on Experimental Method
## 1 Introduction
[24] proposed a numerical approximate springback prediction and compensation method by considering the section distortion of a spatially variable curvature tube, establishing the curvature and torsion mapping functions of the central axis of the tube before and after springback. After springback compensation, the position deviation of each node was less than 1.4%, and the average position deviation was 0.80%. [PERSON] et al. [25] considered the interference of section deformation, established the mapping relationship between characteristic parameters and cross-section distortion, and predicted the springback angle. The experimental results showed that the average error of the springback angle of this method was 4.73%, and the three different analytical models were 38.92%, 14.39% and 14.22% respectively. This method significantly improved the prediction accuracy of the springback angle. To sum up, many scholars predicted the springback of free bending based on complex finite element and numerical analyses. Although these studies can predict springback well, they ignore the complexity of the influencing factors during the process of free bending, such as the gap between the bending die and the tube; the friction between the guide mechanism and the tube; the friction between the bending die and the spherical bearing; the bearing flutter; and the installation error of the bending machine, etc. Moreover, different materials have different _U-R_ relationships, and the analysis and calculation of a single material is often not applicable to other materials. Therefore, an experimental method is proposed in this paper to avoid complex numerical calculations, as experimental data are the most real data. _U-R_ experiments were carried out for Q235 carbon steel and aluminum 6061 to obtain the real _U-R_ curve. _U-R_ experiments showed that the radius R decreases with the increase of bending mode offset U, and different materials have different _U-R_ relations. The springback experiment was carried out according to the real _U-R_ data. Through the compensation of the transition section and the arc section, it was found that the compensation of the arc section can solve the springback phenomenon. A springback compensation curve capable of accurately predicting the springback was established.
# Agriculture 4.0: Is Sub-Saharan Africa Ready?
###### Abstract  
A fourth agricultural revolution, termed agriculture 4.0, is gradually gaining ground around the globe. It encompasses the application of smart technologies such as artificial intelligence, biotechnology, the internet of things (IoT), big data, and robotics to improve agriculture and the sustainability of food production. To date, narratives around agriculture 4.0 associated technologies have generally focused on their application in the context of higher-income countries (HICs). In contrast, in this perspective, we critically assess the place of sub-Saharan Africa (SSA) in this new technology trajectory, a region that has received less attention with respect to the application of such technologies. We examine the continent's readiness based on a number of dimensions such as scale, finance, technology leapfrogging, institutions and governance, education and skills. We critically reviewed the challenges, opportunities, and prospects of adopting agriculture 4.0 technologies in SSA, particularly with regards to how smallholder farmers in the region can be involved through a robust strategy. We find that whilst potential exist for agriculture 4.0 adoption in SSA, there are gaps in knowledge, skills, finance, and infrastructure to ensure successful adoption.  
agriculture 4.0; internet of things (IoT); precision agriculture; robotics; smallholders; sub-Saharan Africa (SSA) +
Footnote †: journal: Applied sciences
## 7 Conclusions, Limitations, and Future Work
### Limitations and Future Work  
The first of the key limitations of this study is the absence of a control group, which makes it challenging to isolate the direct impact of ITS-CAL on students' learning outcomes. Without a comparison against students who did not use ITS-CAL, we cannot rule out the possibility that the observed improvements were influenced by external factors such as self-motivation, prior experience, or additional learning resources. Future studies should include a control group to better assess ITS-CAL's effectiveness. Additionally, a longitudinal study could be conducted to track students' learning progression over a longer period and evaluate the system's long-term impact.  
The second limitation of this study is that student learning was assessed solely through a final exam, without a pretest-posttest comparison or complementary performance-based evaluations. While the final exam provides a standardized measure of students' programming proficiency, it does not capture individual learning progress or coding practices developed throughout the course. Future research should incorporate pretest-posttest comparisons to measure knowledge gains at an individual level. Additionally, alternative evaluation metrics such as code quality analysis, debugging efficiency, and project-based assessments could provide a more comprehensive picture of student learning. These additional measures would enhance the validity of the findings and offer deeper insights into the impact of ITS-CAL on programming skill development.  
The third limitation of this study is that, while external tool usage was strictly controlled during the ITS-CAL evaluation, we did not measure students' exposure to AI-assisted learning outside of the exam environment. Although proctors ensured that students did not use external generative AI tools (e.g., ChatGPT) during the exam, it remains possible that students' prior experience with such tools influenced their overall programming ability. Future studies could incorporate survey-based self-reporting or tracking mechanisms to better understand students' AI tool usage before the experiment. Additionally, comparative studies could be conducted to evaluate the effects of ITS-CAL under different learning conditions (e.g., with or without prior exposure to AI-assisted learning tools).  
Conceptualization, C.-H.L.; methodology, C.-H.L.; software, C.-Y.L.; validation, C.-H.L.; resources, C.-Y.L.; data curation, C.-Y.L.; writing--original draft preparation, C.-H.L.; writing--review and editing, C.-H.L.; visualization, C.-H.L.; supervision, C.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to school privacy policies.  
The authors declare no conflicts of interest.
## 2 Datasets and Pre-Processing Steps  
The datasets used in this analysis are summarized in Table 1 and include XCO\({}_{2}\) from satellite (GOSAT) and model simulations (CarbonTracker and Goddard Earth Observing System (GEOS)-Chem), biospheric CO\({}_{2}\) fluxes from Simple Biosphere Model, version 3 (SiB3) used in GEOS-Chem, and related surface environmental parameters including surface skin temperature (Temp), self-calibrating Palmer drought severity index (scPDSI), burned area (BA), and GPP. Detailed information about data set versions and their characteristics will be described in the following sections. The spatial and temporal scope of our analysis covers land areas from 45\({}^{\circ}\)S to 60\({}^{\circ}\)N, from June 2009 to May 2016.
# Precipitation-Use Efficiency and Its Conversion with Climate Types in Mainland China
## 4 Discussion
### Transformation Characteristics of PUE  
The transition zone exhibited the highest PUE. PUE reached its highest value of 2.2 g\(\cdot\) m\({}^{-2}\cdot\)mm\({}^{-1}\) in the area where the annual precipitation was 414 mm. The unimodal PUE distribution, which first increases and then decreases with increasing precipitation, has been confirmed by other studies. [PERSON] et al. [8] indicated that in American temperate grasslands with 200-1200 mm precipitation, PUE first increased and then decreased with increasing precipitation, peaking at 475 mm. PUE in extremely arid and extremely humid regions is low. [PERSON] et al. [5] reported that the PUE initially exhibited a rising trend but subsequently decreased as precipitation increased from 200 to 1200 mm. PUE peaked at 400-500 mm. [PERSON] et al. [6], [PERSON] and [PERSON] [7], and [PERSON] et al. [11] also showed that in regions with annual precipitation less than 600 mm, PUE increased with increasing precipitation. In humid regions with annual precipitation above 650 mm, PUE decreased with increasing precipitation, and when the annual precipitation was above 1500 mm, PUE was approximately constant. [PERSON] et al. [9] reported that the spatiotemporal PUE pattern in alpine grasslands in northern Tibet initially increased in the arid region and subsequently decreased along the precipitation gradient toward the humid region, reaching a peak at approximately 500 mm precipitation.  
The PUE distribution pattern with precipitation in this study is consistent with the above studies, and different precipitation thresholds of the maximum PUE conversion point may be partially attributed to the different research methods and study regions. In addition, the distribution patterns of annual PUE with \(\Delta\)T, \(\Delta\)SM, and \(\Delta\)U changes in different precipitation climate types also indicate that the PUE transition interval coincides with the northern edge of the monsoon. Therefore, the results of this paper are reasonable.  
Our research shows that transition regions with limited rainfall have the strongest NPP and PUE processes, and the response of the carbon fluxes in these fragile regions deserves more attention.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 3 SSA-LSTM Based Dam Deformation Prediction Model
### Implementation Framework  
The SSA algorithm is used to determine the hyperparameters of the LSTM model, after which the optimized hyperparameters are used for model training and prediction by means of parameter transfer to optimize the performance of the model. This combination can improve the prediction accuracy and stability and effectively handle the complex temporal relationships in dam monitoring data. The implementation framework of the SSA-LSTM-based dam deformation prediction model proposed in this paper is shown in Figure 3. The specific process is as follows:  
1. Data preprocessing. To improve the predictive capability of the dam monitoring model, it is necessary to clean and transform the prototype observation data in advance. The first step is to clean up outliers and attempt to eliminate or reduce noise interference in the data. If the cleaning effect does not meet the requirements, various data denoising methods can be tested by considering the data structure. It may even be necessary to combine multiple denoising methods to achieve the desired cleaning effect. Commonly used data denoising methods include mean filtering, Gaussian filtering, and wavelet denoising. The Symlet wavelet filtering denoising method is based on the principle of wavelet analysis and threshold processing. It involves wavelet decomposition and reconstruction of data to achieve noise removal. Gaussian filtering, on the other hand, uses the shape of a Gaussian function to smooth the data and reduce the effect of noise through weighted averaging of the neighborhood around each data point. After clearing outliers, any missing parts of the data can be interpolated. Additionally, normalizing the data is necessary to standardize the baseline values and dispersion of different features in the sample matrix. By following these preprocessing steps, a more accurate and reliable database can be provided for the dam monitoring model.
2. Parameter optimization. Using the four equations mentioned in Section 2.2, the discoverer, joiner, and vigilant positions are updated in the iterations, and the fitness values are calculated. At the end of the final iteration, the global optimal sparrow position is output, and the optimal parameters of the LSTM model are obtained.
3. Model training and prediction. The training and prediction datasets are used to train and predict the LSTM model, with hyperparameters determined by SSA. The accuracy of the model's predictions is assessed by comparison with the true values from the test set. Additionally, the optimal hyperparameters are applied to different models for visual comparison to further evaluate the model's performance. Detailed descriptions of each model's prediction, along with model evaluation metrics, are considered to identify the optimal model.
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 5 Results
### Standard Phantoms Contrast  
The ultrasound image of the phantom is shown in Figure 5. Cysts presence as long as the expected reflectivity can be observed in both results--DAS: Figure 5a and RiB: Figure 5b. However, the contrast of the proposed beamformer is far better.  
The primary objective of the experiments is to determine the most accurate value for \(\kappa\) to achieve the best ultrasonic image fitting to the expected reflectivity values provided by the phantom manufacturer. As the image resulting from RiB displays linear behaviour with respect to \(\log\kappa\), see Appendix A, the tested values follow the same progression. The selected value for RiB image processing is \(\kappa=\)10%.  
Based on this classification and the parameter selected value, the result of the analysis for both beamformers is shown in Figure 5c in form of a boxplot. This way outliers  
Figure 5: Results obtained using Array A of a CIRS 040 GSE phantom. (**a**) DAS, (**b**) Rib10% and (**c**) comparative boxplot.  
displayed. Boxplots are paired, the leftmost is the result of DAS, whereas RiB output are the rightmost. Focusing on the difference of the median values for the regions, compared to the median value of the background, contrast ratio gain is evidenced. Several conclusions can be obtained: both methods display progressive scale of grey values corresponding to each reflectivity expected value for the cysts. However, DAS beamformer displays no sensible difference while comparing 3 dB and 6 dB regions. Difference between regions measured using RiB beamformer displays a 3 dB variation, in coherence with the manufacturer specifications. The interquartile range (IQR) of the RiB regions is wider because this method enhances texture displayed and contrast.