esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, it is necessary to optimize the algorithm based on existing work and design a security protocol with lower computational complexity, without reducing the security strength, to meet the requirements of real-time communication in UAV scenarios. In addition, the key security of existing ECC-based authenticated key-agreement systems (e.g., those in [22; 23]) is primarily based on temporary random numbers that are generated during the session. Once the attacker gains possession of the temporary information, the public information can be used to calculate the session key directly. Because an onboard computer is a low-power terminal with limited resources, the pseudo-random numbers that it generates are usually predictable to a certain extent. As the attack possibilities gradually increase, the security risk of using common authenticated key-agreement protocols in UAV communication scenarios also gradually increases. In response to the above problems, this study proposes a lightweight security-enhanced authentication key-agreement protocol (SE-UAVAKA) that is suitable for communication between UAVs and ground stations. Compared with existing authentication key-agreement protocols, this study makes the following contributions:  
1. An authenticated key-agreement protocol with low computational complexity and only four scalar multiplication operations is proposed, and the performance analysis shows that our protocol has a lower computational cost than representative protocols. 2. A key-agreement scheme is designed to compute the session key by combining the identity identifier and a temporary session random number. This scheme ensures the security of the session key when information on the temporary session random number is leaked. 3. A formal analysis and ProVerif simulation [29] are performed to verify the performance of the proposed protocol. Both the theoretical analysis and experimental verification show that the proposed protocol has higher security properties and a lower computational cost than representative protocols. The remainder of this paper is organized as follows: Section 2 presents a theoretical analysis of the shortcomings of the existing protocols. Section 3 introduces the proposed authenticated key-agreement protocol. Section 4 presents the theoretical analysis, simulation verification, and performance and security comparison of protocol security. Finally, Section 5 summarizes the study. The main contributions of this paper and the schematic diagram of the key chapter structure are shown in Figure 1.
###### Abstract  
This study was carried out in order to study the flexural behavior of fiber-reinforced ultra-high-performance concrete (UHPC) containing hybrid microsteel straight fibers and natural fine aggregates under four-point flexural loading. The experimental results revealed that the fiber pullout mechanism had a progressive pullout (collapse) mode. A highly flexural crack developed when the fiber pulling mechanism was explicitly triggered, leading to the failure of most beams. The maximum load in beams reinforced by 1.2, 1.6, and 2.0% exceeded that in beams without longitudinal reinforcement by 56, 73, and 94%, respectively. Further, bar reinforcements at 125, 115, 95, 85, and 75 mm depths led to increases of 56, 55, 73, 96, and 94% in beam load capacity, respectively. In addition, bar reinforcement at 115, 95, 85, and 75 mm depths reduced the beams' ductility by 40, 23, 35, and 39% compared to those with 125 mm depth. All studied UHPC beams had an uncracked phase that extended to a curvature of about \(7.5\times 10^{-6}\) rad, which occurred at about 10 kNm. The use of the design of experiments was exploited in this investigation to develop a prediction model for the ultimate moment capacity of UHPC beams. This prediction model took into account the sectional and material properties of UHPC beams. To carry out this analysis, a database of 25 beams, developed by other investigators, as well as the present authors, was utilized. With a mean prediction-to-test ratio of 0.92, this prediction model had a reasonable performance capacity. In turn, this model was used to generate isosepsonsive surface contours that could be used for UHPC beam design.  
ultra-high-performance concrete (UHPC); moment capacity; RC beams; hybrid fiber; ductility +
Footnote †: journal: Applied Sciences
# Ensemble Learning of Multiple Deep CNNs Using Accuracy-Based Weighted Voting for ASL Recognition
## 2 Materials and Methods  
### Datasets and Image Preprocessing  
This paper uses two datasets, the ASL Alphabet [33] and ASLA (American Sign Language Alphabet) [34]. The ASL Alphabet dataset includes 29 classes, comprising 26 alphabetic characters A-Z and three other characters: space, delete, and nothing. Each class has 3000 images, each of which is 200 \(\times\) 200 pixels in size. The ASLA dataset is similar to the ASL Alphabet except for the backgrounds, as shown in Figure 1. Each class has 7000 images, each of which is 400 \(\times\) 400 pixels. The two datasets are split into 85% training images and 15% testing data.  
The ASL Alphabet and ASLA datasets have motion, non-motion, and complex background properties, which bring challenges for ASL recognition.  
The images are preprocessed using gray data normalization, median filtering, reshaping, and label encoding to reduce the noise and environmental effects. Gray data normalization compresses all pixel data into 0-1 space intervals and changes the image channel to one. Furthermore, it reduces the effects of light in the images and makes them scale invariant, meaning that the mean and variance are the same for all features. The median filter removes background noise from the image. The images are reshaped to 227 \(\times\) 227 pixels for AlexNet and LeNet and to 224 \(\times\) 224 pixels for GoogleNet, VGGNet, and ResNet50. Decimal labels are encoded into one-hot vectors to conveniently compare the results in fully connected layers by the label encoding method.
## 3 Applying Predictive Models to an eLearning Messaging System
### The Development Phase
#### 3.1.1 The Historical Database  
Predictive models benefit from a rich historical database with numerous observations and variables. In many predictive modeling settings, there is one outcome (dependent) variable and multiple input (independent/predictor) variables. The historical database must include known values for the outcome variable. If, for example, the task is to predict future students' final course grades, the historical database must include course grades of those who have completed the course. Figure 1 illustrates this by listing numerical values for the outcome variable, **Y**, in the historical database.  
Decisions regarding input variables for the historical database must be made. Availability is a key consideration, but so, too, is whether the values of the predictor variables will be known at the time of the eventual prediction [35]. It's possible, for example, that the grade earned in Course A (an input variable) is a good predictor of the grade earned in Course B (the outcome variable) because A provides background skills or knowledge for B. Unless A is an enforced prerequisite for B, the grade in A cannot be used to predict the grade in B for those students who have not yet taken A. Thus, input variables are typically excluded from a predictive model's historical database when they cannot help to predict, even if there is strong theoretical justification for their inclusion. Among those variables expected to meet this criterion, potential input variables might come from interviews with students, instructors, or other experienced practitioners. Input variables might also be suggested by the academic literature. Although each input variable need not enjoy a deeply-reasoned rationale for inclusion in a predictive model, spurious correlation is more likely to be avoided if input variables have plausible behavioral, psychological, economic, pedagogical or other links to the outcome variable. The hypothetical historical database in Figure 1 might contain, for example, \(k=43\) fields for the input variables.
## 3 Results
### X-ray Diffraction  
X-ray diffraction analysis of the samples was performed at 3 and 6 months to characterize the changes that may have occurred in the mixed soil (Figure 8). Changes in the gypsum, calcite, and ettringite were studied to better understand the compounds formed over time in the mixed soil depending on the hydraulic binder employed, the addition of the industrial waste, and the solution used for the sample preparation. The X-ray diffraction analysis showed that more gypsum was generated for the lime samples made using the sulfate solution than for the cement samples made using the same solution. However, more gypsum was detected in the cement samples than in the lime samples when distilled water was used. Moreover, more gypsum was observed in the lime and cement samples made without industrial waste. Regarding the calcite compound, the biggest quantity of calcite was recorded for the lime samples with industrial waste. Concerning the change over time, the amount of gypsum seemed to have increased for the lime samples made using the Na\({}_{2}\)SO\({}_{4}\) solution from 3 to 6 months. Additionally, a slight increase in ettringite was observed for the samples without industrial waste at 6 months when those samples were made using the sulfate solution. Lastly, a decrease in the calcite compounds seemed to have occurred from 3 to 6 months in the lime samples made using the sulfate solution.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Binder**} & **Industrial** & **Sulfate** & **Curing Age** & \multicolumn{3}{c}{**Weight Loss (\%)**} \\ \cline{4-7}  & **Waste** & **Solution** & **Curing Age** & **dh** & **dx** & **dc** \\ \hline \multirow{2}{*}{Cement} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & 7 days & 6.984 & 2.089 & 8.674 \\  & & & 3 months & 7.126 & 2.044 & 9.594 \\  & & & 6 months & 7.030 & 1.934 & 10.400 \\ \hline \multirow{4}{*}{Yes} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} & 7 days & 5.847 & 2.187 & 15.484 \\  & & & 3 months & 6.254 & 2.077 & 16.262 \\  & & & 6 months & 6.481 & 2.041 & 15.276 \\ \hline \multirow{4}{*}{Yes} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & 7 days & 6.984 & 2.089 & 8.674 \\  & & & 3 months & 7.127 & 2.009 & 9.485 \\ \cline{1-1}  & & & 6 months & 7.673 & 2.114 & 9.737 \\ \hline \multirow{4}{*}{Yes} & \multirow{2}{*}{Yes} & 7 days & 5.847 & 2.187 & 15.484 \\  & & & 3 months & 5.897 & 2.074 & 17.763 \\ \cline{1-1}  & & & 6 months & 5.714 & 1.610 & 17.447 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relative values from DTA-TG tests for cement samples.  
Figure 8: X-ray diffraction analyses of the lime and cement samples at 3 and 6 months.
## 3 Results and Discussions
### Effect of Nanoparticles on Microstructural Properties
#### 3.5.2 Variable Pressure Scanning Electron Microscope (VP-SEM)  
Figure 6 presents the VP-SEM images for the untreated and treated soil samples after 14 days of curing. It is noticed that the natural soil samples (Figure 6a) showed a dispersed structure with a considerable number of visible voids. At the same magnification scale, both calcium and magnesium hydroxide treated soil samples (Figure 6b,c, respectively) exhibited a continuous and dense structure. This observation provides evidence for the improved UCS and reduced hydraulic conductivity of the  
Figure 5: XRD graphs for untreated and nanoparticles-treated residual soils.  
treated soil samples as the combined effects of flocculation and the formation of cementing gels such as calcium silicate hydrate, calcium aluminate hydrate, magnesium silicate hydrate, and magnesium aluminate hydrate enhanced the interlocking of soil particles and resulted in a denser soil structure. In addition, the denser soil structure inevitably restricted the water flow path as the tortuosity of water flow path increases due to reduced soil pores. This behavior is primarily responsible for the lower hydraulic conductivity values obtained for the treated soil samples relative to the natural soil sample.  
Figure 6: Variable-pressure scanning electron microscope (VP-SEM) images for untreated and nanoparticles-treated residual soils. (**a**) Natural soil (NS); (**b**) calcium hydroxide treated soil (CS); (**c**) Magnesium hydroxide treated soil (MS).
## 5 Experiments
### Evaluation of Joint Pruning and Quantization
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**Classification (Accuracy)**} & \multicolumn{2}{c}{**Segmentation (mIOU)**} & \multicolumn{2}{c}{**Style Transfer (FID)**} \\ \cline{3-6}  & & **DenseNet121** & **MobileNetV2** & **UNet** & **FRRNet** & **CycleGAN** \\ \hline \multirow{2}{*}{Baseline} & Full Model & 78.70 & 68.31 & 61.69 & 66.32 & 47.17 \\  & W8A8 & 79.04 & 68.26 & 62.03 & 66.43 & 49.89 \\ \hline \multirow{3}{*}{50\% Sparsity} & W8A8 & 79.01 & 67.58 & 60.44 & 64.81 & 58.12 \\  & W4A8 & 78.41 & 64.67 & 60.15 & 63.42 & 64.25 \\  & W4A4 & 75.99 & 59.25 & 56.12 & 59.04 & 92.71 \\ \hline \multirow{3}{*}{75\% Sparsity} & W8A8 & 76.17 & 61.02 & 56.15 & 61.28 & 85.89 \\  & W4A8 & 76.12 & 55.41 & 55.60 & 60.90 & 92.12 \\ \cline{1-1}  & W4A4 & 73.62 & 49.49 & 51.90 & 54.16 & 119.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Joint channel pruning and uniform quantization to learn low-precision structured subnetworks using our algorithms depicted in Figure 1 and discussed in Section 4.  
estimate their precision at 16 bits per element rather than 32 since neural networks can be quantized to 16-bit fixed-point through post-training quantization without significant accuracy degradation [50]. Table 4 shows that our method is able to achieve a higher performance density (8.92) than the highest of previous solutions (8.59) while having very competitive performance (92.53% versus 91.66% top-1 accuracy). When applying 50% pruning and 4-bit quantization, our method achieves the smallest memory footprint and highest performance density of 25.25, three times larger than the existing highest, while still maintaining sufficient accuracy (87.3%). Moreover, unlike methods such as [12; 51], our framework provides direct control over the target bitwidth and sparsity, which is more useful in practical scenarios under tight design constraints, as is the case when deployed on edge GPUs or FPGAs [52; 53; 54].
## 1 Introduction
Additionally, the radius of the effective area of the image (circular retinal area) varies depending on the picture's size. To overcome the challenges mentioned above, we had better to leverage the WSN and deep learning to construct the application of DR diagnosis and treatment services, the main contributions of this study are depicted as follows:  
1. To support health caregivers in the effective management and provide a proactive prevention of diabetic retinopathy, we propose a wireless sensor system architecture to perform the task of grading the DR in the ubiquitous environment and provide clinical services for DR diagnosis and treatment. The architecture employs portable retinal cameras, a blood glucose monitor, and a tablet computer as the data collection nodes of the wireless sensor network. A database server implemented with AI algorithms is responsible for processing the data transmitted via the wireless network and making decisions for various clinical roles (e.g., clinicians, patients, and hospital staff). 2. To solve the problems of irregular region distribution, fuzzy pixels, and low contrast, we propose a parallel deep learning framework (HybridLG) for learning both the local and global information of 2D fundus images. The framework includes a CNN backbone, a Transformer backbone, a neck network for feature fusion, and a head network. The CNN and Transformer backbones are adopted to extract local and global information, respectively. The neck network is adopted to fuse the local and global information and prepare for making final grading decisions. In addition, to deal with the problem of model performance affected by image diversity, we propose a model training strategy inspired by an ensemble learning strategy. It aims to improve the model generalization ability of the parallel framework. We use a fully connected layer to simulate this weighted voting process by adding weight parameters to each soft-voting model to identify the best result. 3. Considering the high computation complexity of the Transformer backbone, we propose a novel deep learning model named MobileViT-Plus and use it to implement the Transformer backbone of the HybridLG. Specifically, the MobileViT-Plus model is constructed by introducing a light Transformer block (LTB) into the MobileViT model. In the original Transformer block, multi-head self-attention involves computing the pairwise similarity between all pairs of input elements, which results in a quadratic computational complexity. To mitigate the computation overhead, in LTB, we use a k \(\times\) k depth-wise convolution with stride k to reduce the spatial size of some parameters before the attention operation. In addition, we use a pre-trained ResNet101 for implementing the CNN backbone of the HybridLG framework.
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 2 Path Planning Algorithms Under Sparse Map Conditions  
In extraterrestrial exploration missions, intelligent agents often perform aerial terrain scanning of the target area before landing, acquiring elevation maps of the region. These elevation maps only depict the basic features of the target area, such as the orientation of mountain ranges, the location of meteorite craters, designated landing spots, destinations, etc. However, they often fail to provide a comprehensive understanding of obstacles that may affect subsequent tasks of the intelligent agent, such as rocks, pits, and riverbeds. Therefore, it is crucial for intelligent agents to possess the capability to effectively plan paths under sparse map conditions to adeptly handle the challenges posed by unknown obstacles.  
This section first explores the research background of path planning in three-dimensional space under sparse map conditions, followed by an analysis of the performance of the traditional A* algorithm when applying different heuristic functions under these conditions. Lastly, this section proposes strategies for obstacle avoidance and corresponding path replanning in the face of unknown obstacles. This analysis aims to provide theoretical support for path planning for intelligent agents in missions like extraterrestrial body exploration when faced with sparse map conditions.
## 4 Conclusions  
An optimization strategy for micro-milling process parameters based on multi-objective evolutionary algorithms was proposed. The optimization objectives including machining quality, machining efficiency, and specific cutting energy were trade-offs. The obtained process parameters were validated using micro-milling experiments. The specific conclusions are as follows:  
1. The power consumption characteristics of the micro-milling process were analyzed, and a process-parameter-based power consumption model was established. The analysis revealed that the power consumption of non-machining feed motion exhibits a quadratic relationship with the feed rate, while the non-machining spindle power consumption shows a quadratic relationship with the spindle speed. The spindle speed exerts the greatest influence on the variable power consumption.
2. The coefficient between SCE and MRR was \(-\)0.793, which indicates that enhancing machining efficiency can simultaneously improve machining sustainability.
3. Among the three indicators characterizing machining quality, surface roughness and surface fractal dimension exhibited a significant negative correlation (with a correlation coefficient of \(-\)0.832). However, their correlations with dimensional error were relatively small, being 0.57 and \(-\)0.628, respectively. After performing PCA, the variance extraction percentages for \(S_{u}\), \(D_{s}\), and \(D_{e}\) all exceeded 50%, indicating a good level of information condensation.
4. The optimization results of the process parameters demonstrate that the NSGA-III-based optimization method yields a better trade-off between machinability and sustainability, indicating that NSGA-III exhibits superior performance in handling multi-objective optimization problems and can obtain globally optimal parameters. The optimal combination of micro-milling parameters was \(n\) = 28,800 rpm, \(f_{z}\) = 2.6 \(\upmu\)m/t, and \(a_{p}\) = 62 \(\upmu\)m.  
Conceptualization, P.W. and Q.B.; methodology, P.W.; software, P.W.; validation, Q.B., K.C., L.Z. and Y.Z.; formal analysis, P.W.; investigation, P.W.; resources, Q.B.; data acquisition, P.W.; writing--original draft preparation, P.W.; writing--review and editing, Q.B. and K.C.; visualization, P.W.; supervision, Q.B. and K.C.; project administration, Q.B.; funding acquisition, Q.B. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the National Natural Science Foundation of China (grant no. 52075129).  
Not applicable.  
The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 21: Surface topographies of the thin-walled micro parts: (**a**) NSGA-II; (**b**) MOPSO; and (**c**) NSGA-III.
## 3 Results Analysis
### Land-Use Rising/Falling Tupu Analysis
#### 3.3.1 Land-Use Rising Tupu
Gutian County has the highest area of new increased cultivated land, 7.38 km\({}^{2}\), and Gutian County has the second highest area of new increased cultivated land after Xiapu County, 7.28 km\({}^{2}\); Fuding City has the highest area of new increased water bodies, 11.38 km\({}^{2}\), and it has the second highest area of new increased built-up land, 6.6 km\({}^{2}\); and the land-use change in Zhouning County is relatively stable. During the period of 2010-2020, the area of land-use change area decreased compared with the previous stage, but the spatial distribution range of the change area was wider. The area of new increased cultivated land increased significantly, reaching 111.52 km\({}^{2}\), accounting for 36.51% of the total area of new increased land; followed by the area of new increased built-up land, expanding by 27.17 km\({}^{2}\) compared with the previous period, indicating that urbanization and industrialization in Ningde City increased the demand for built-up land in this period; the area of new increased forest land and new increased grassland decreased by 39.42 km\({}^{2}\) and 16.65 km\({}^{2}\), indicating a slowdown in ecological land protection during this period. The area of new increased cultivated land in Gutian County was still the largest, reaching 21.94 km\({}^{2}\), and the area of new increased land in Fuding City followed with 16.4 km\({}^{2}\); it is worth paying attention to the fact that, except for Zherong County, the area of new increased built-up land in the rest of the counties and cities increased significantly compared with the previous period, among which the area of new increased built-up land in Jiaocheng City had the largest increase; in addition, the area of new increased forest land in all the counties and cities decreased significantly compared with the previous period, among which the decrease was the largest in Fu'an City; Xiapu County's new increased grassland area is second only to Gutian County, indicating that the returning farmland to forest and grassland project is selective to the terrain.