esa-sceva/satcom-chunk-collection huggingface dataset:
# Parametric Analyses of the Response of Masonry Walls with Reinforced Plaster
## 3 Numerical Simulations
### Retrofitted Masorury--Optimal Orientation for the Anchors  
The function of anchors in the reinforced plaster technique is fundamental to guarantee the transfer of in-plane forces from the masonry to the plaster [42]. Good collaboration between the two materials depends on the stiffness of the anchors, which is influenced by the anchors' orientation. To investigate the effect of the orientation of the anchors, several models were implemented, varying the angle with respect to the horizontal from 0\({}^{\circ}\) to 45\({}^{\circ}\). The simulations were conducted using a reduced volume of masonry, made by three bricks, 10-mm mortar joints, and 50-mm plaster (Figure 16).  
The length of the anchor was 200 mm (25 mm inside the plaster), while its diameter was 10 mm. The mechanical properties of the materials are defined in Table 2. The size of the mesh was 5 mm, except for the nail, where 2.5 mm mesh was used. The anchors were introduced as embedded elements in both the masonry and the plaster. The interaction between the plaster and the wall surface was defined as normal hard contact and tangential behavior, with a friction coefficient of \(\mu=0.1\). The load was applied uniformly on the top face of the concrete plaster; the out-of-plane displacements of the plaster and the vertical and horizontal displacements of the bottom face of the brick were precluded.  
Figure 16: Geometry, loads, and boundary conditions.  
Figure 15: Damage of tension at failure for different weak-brick patterns: (**a**) R1 pattern, (**b**) R2 pattern, (**c**) R3 pattern, (**d**) no weak bricks.  
Figures 17 and 18 show the anchor deformation inside the masonry, the damage of the tension and compression, and the maximum stresses in the anchor for 45\({}^{\circ}\) and 0\({}^{\circ}\), respectively. In both configurations, the maximum stress occurred at the interface between the reinforced plaster and masonry prism, but in the 45\({}^{\circ}\) configuration, the stress values were lower. It is also evident that the damage, especially that of the tension, involved a larger area in the 0\({}^{\circ}\) configuration. Figure 19 shows how the anchor orientation influenced the vertical plaster displacement: by increasing the nail angle, the vertical displacement of the concrete cover decreased. This phenomenon may have two possible explanations: first, the shear area of the rotated nail was higher with respect to the horizontal one, and, second, as the orientation of the nail increased, the axial stiffness of the nail would collaborate to increase the stiffness of the anchor; in other words, the virtual work performed by the compressive force acting on the anchor reduced the final displacement, which led to a reduction in the reinforced plaster displacement. Nevertheless, by considering the drilling procedure to install the nails inside the masonry wall, zero degrees remains the better solution to guarantee proper installation.
## 1 Introduction
In this algorithm, agents should update the cost function whenever the network topology changes. Thus, it is necessary to control the overhead of DRL in MANETs. To reduce the overhead of DRL, our approach is to limit the number of nodes involved in DRL. This means that nodes are clustered into specific groups by considering parameters related to mobility resilience. Therefore, DRL can reduce the overhead by focusing on nodes identified as having high resilience to topology changes. Additionally, it is possible to select reliable paths using these clustered nodes in dynamic networks. For clustering, we apply UL prior to evaluating the reliability of paths through DRL. The main contributions of this study are as follows:  
* UL for Clustering: To reduce the computing overhead of DRL, we apply UL for node clustering. Each node is clustered into a group according to mobility patterns, the number of neighbors, and connection time. A general k-means technique is introduced for this purpose. * Routing Based on DQN: After clustering nodes, we select paths involving nodes between the source and destination. We use the DQN approach by rewarding reliable paths with higher values. * Multi-Objective Decision Making Based on Reward Function: To update reward values with multiple parameters in DQN, it is essential to adjust weighting factors. To avoid unreasonable weighting values, we employ the Analytic Hierarchy Process (AHP) [20] to set these values. * Practical Simulation: Practical simulations were conducted for scenarios using NS-3. We present comparative simulation results for PDR and energy consumption in the context of varying mobility speeds.
## 3 Results and Discussion
### Mechanical Properties  
The mechanical (flexural and tensile) properties of materials provide information on how they will behave under load. Table 9 shows the tensile and flexural properties of the PHBV samples and a commercial PHBV (8% HV) sample. The PHBV tensile strength ranged between 10.1 and 13.7 MPa and [PERSON]'s moduli ranged between 0.57 and 1.12 GPa. The commercial PHBV had a strength of 25.9 MPa and modulus 0.75 GPa. Tensile strength values from the literature were 27.1 MPa (injection molded 8% HV) [66], 29.14 MPa (solution cast 5% HV) [67], and 14.1 MPa (solution cast 12% HV) [68]. While the Young's modulus values were 3.5 GPa for injection molded (8% HV) [66], 2.19 GPa (solution cast 5% HV) [67], and 0.82 GPa (solution cast 12% HV) [68]. In a separate study, the tensile strength and Young's modulus of 20 MPa and 0.8 GPa for PHBV-20% HV were reported [69]. An inverse relationship was between strength and modulus values and HV content. The PHBV samples were shown to have a flexural strength of 18.1 to 27.5 MPa and moduli of 0.80 to 1.54 GPa. Literature values for flexural strength for PHBV(8% HV) were 34.5 MPa [66] and 28.2 MPa [70] and the flexural moduli for PHBV(8%HV) were 3.2 GPa [66] and 1.3 GPa [70]. The low mechanical properties for the pilot plant PHBV samples were due to their high HV content. A negative linear relationship between flexural strength and HV% (\(R^{2}\) = 0.68), flexural modulus and HV% (\(R^{2}\) = 0.78), tensile strength and HV% (\(R^{2}\) = 0.69), and Young's modulus and HV% (\(R^{2}\) = 0.83) were observed. The strength and flexibility of these polymers was comparable to LDPE which makes them applicable for the packaging industry [5; 8]. It has been reported that an increase in HV content (0 to 30%) reduces the melting point, crystallization rate, tensile strength, and stiffness of the PHBV [71]. PHBV shows a comparable modulus to polyethylene terephthalate (PET), higher than to that for low density polyethylene (LDPE) and polypropylene (PP) [71]. For comparative purposes, the tensile strength and Young's moduli for PHB were 40 MPa and 3.5 GPa, for LDPE were 10 MPa and 0.2 GPa, and PP were 38 MPa and 1.7 GPa PP [69; 72]. Low content HV PHBV has applications where rigidity is required such as bottle caps, while high content HV PHBV would be suitable as flexible packaging and bottles [71]. The molar mass distribution of polymers, PDI, is known to influence their properties. Polymers with higher PDI contain some shorter chain polymers that can act as a plasticizer and therefore reduce their mechanical properties [60]. A negative correlation between
# Load Forecasting with Machine Learning and Deep Learning Methods
## 3 Methodology
### Modeling
#### 3.2.5 Long Short-Term Memory
\[h^{[t]}=g_{1}\Big{(}w_{og}\Big{[}h^{[t-1]},x^{[t]}\Big{]}+b_{og}\Big{)}g_{2} \Big{(}c^{[t]}\Big{)} \tag{15}\]  
where \(w_{og}\Big{[}h^{[t-1]},x^{[t]}\Big{]}\) and \(b_{og}\) are the weights of the information previously retained together with the new data, kernel, and bias, respectively. \(g_{1}(\cdot)\) is the activation function corresponding to this set of information. Sigmoid activation function is commonly used to select which of the previous values are retained. \(g_{2}(\cdot)\) is the activation function of the cell state. The tanh activation function is employed to give weights to these retained values. LSTM can be advantageous for electricity loads forecasting. The main reasons are as follows:  
* Temporal modeling: It is specifically designed to model temporal dependencies in data. * Context preservation: It can capture short-term dependencies and accommodate irregular or missing data points due to its long-term memory. * Scalability: It can effectively capture complex patterns from extensive historical data. * Robustness: It is robust against noise and outliers in data.
## 5 Conclusions  
The SFA-ViT model proposed in this paper introduces three improvements to the ViT model, each implemented by a corresponding sub-model. The data augmentation model expands the dataset for agricultural pest and disease classification and detection by incorporating the effects of environmental, growth, and filming factors, thereby reducing the model's reliance on real data. The classification model designs a multi-scale parallel structure based on the ViT model, and adopts the knowledge distillation technique to promote the fusion and optimization of the feature information of each scale, which obtains a better classification effect of pests and diseases. The detection model integrates the attention maps from the ViT model across multiple scales, further augmenting the model's perceptual capacity and achieving effective target localization.  
Besides, the model proposed in this paper achieves excellent results on the IP102 dataset (74.7% classification accuracy, 56.8% mAP\({}_{50}\)) and the Plant Village dataset (99.87% classification accuracy), which significantly outperformed the performance of the existing base network.  
The SFA-ViT introduces a multi-scale feature fusion mechanism on the basis of the ViT model, enhancing the model's adaptability to complex scenes. In the field of agricultural pest detection, SFA-ViT not only accurately identifies the types of pests and diseases but also precisely locates the positions of pests and lesions, thereby promoting the development of precision agriculture. Additionally, the multi-scale processing capabilities of SFA-ViT hold cross-disciplinary application value, such as in industrial monitoring, environmental monitoring, and medical image analysis.  
However, SFA-ViT exhibits weaknesses in terms of memory usage and computational efficiency. In future work, deep optimization of the model will be conducted, with a further focus on the research of classification and detection of pest and disease in agriculture.  
Conceptualization, M.X. and N.Y.; methodology, M.X.; software, M.X.; validation, M.X. and N.Y.; formal analysis, M.X.; investigation, M.X.; resources, M.X.; data curation, M.X.; writing--original draft preparation, M.X.; writing--review and editing, M.X. and N.Y.; visualization, M.X.; supervision, M.X.; project administration, M.X.; funding acquisition, N.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National key research and development plan of China, grant number 2016 YFD0600101.  
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### Agast  
The authors of [70] introduced the Adaptive and Generic Accelerated Segment Test (AGAST) detector, which uses specialized decision trees to enhance the performance. It can be used for any type of scene structure. The optimum trees are identified by using the whole binary configuration space. In FAST, the detector must be trained for a specific scene during the training step, but in AGAST, it adjusts to the environment dynamically while processing an image.
## 3 Results
### Spatial Distribution of the Sensitivity Coefficients  
Figure 7 shows the spatial distribution of susceptibility coefficients on the TP. The sensitivity coefficient of \(RS\) (\(S_{RS}\)) ranged from 0.303 to 0.587 and decreased from southeast to northwest on the TP. The sensitivity coefficient of \(T_{man}\) (\(\text{S}_{\text{Tmean}}\)) ranged from 0.025 to 0.172, which was similar to \(S_{RS}\). Different from \(S_{RS}\) and \(\text{S}_{\text{Tmean}}\), the sensitivity coefficient of \(U_{2}\) (\(S_{U12}\)) increased progressively from southeast to northwest and varied from 0.080 to 0.259. The sensitivity coefficient of \(RI\) (\(S_{RH}\)) ranged from \(-0.444\) to \(-0.019\) and thesmallest absolute value of the \(S_{RH}\) found in the southeast corner of the TP and increases to the north. According to above discussion, the sensitivity of these four factors to the \(ET_{0}\) on the TP was in the order: \(S_{RS}>S_{RH}>S_{U2}>S_{Tmean}\).
## 2 Methodology
### Inverse Distance Weighted (IDW) Averaging  
The elevation of Intermediate points in the surface at any given time step is estimated based on the weighted average inverse distance calculation in GoldSim, as presented in Equation (3):  
\[Z_{x}=\sum_{i=1}^{S}\frac{Z_{i}}{\left(d_{i}\right)^{m}}/\sum_{i=1}^{S}\frac{1}{ \left(d_{i}\right)^{m}} \tag{3}\]  
where, \(d_{i}\) is the Euclidean distance between point \(x\) (the point of interest) and point \(i\) in the XY plane, \(S\) is the number of all Defined points, \(m\) is the inverse distance order and \(Z_{x}\) is the elevation of Intermediate points on the surface at any given time. The formulation simply implies that the elevation of Intermediate point \(x\) is the average of the elevation of all Defined points. The closer the Defined point to \(x\), the greater impact it has on \(Z_{x}\).  
Parameter m quantifies the significance of closeness of Defined points to point \(x\) in determining \(Z_{x}\). Choosing higher values, e.g., \(m=3\) (cubed inverse distance) means that as the distance increases, the contribution of the farther points' elevation in the average is significantly lower than that of closer points, compared to the case where \(m=1\) (simple inverse distance). An example is illustrated in Figure 2 to show how parameter \(m\) affects the quasi-3D estimation result.
# Detection of Wheat Yellow Rust Disease Severity Based on Improved GhostNetV2
## 2 Materials and Methods
### Methods
#### 2.3.2 Problems with the Original Model  
Although GhostNetV2, which introduces a long-range attention mechanism, has low-cost operation and high performance, the following deficiencies still exist for the wheat yellow rust detection problem that need to be addressed in this study:  
1. The Ghost module generates two sets of feature mappings, where the second set of feature maps is obtained by using linear transformation of the first set of feature maps. Because there are many similar features in the two sets of feature maps and the channel structure is consistent, the model can only learn the main information of one set of feature maps during the training process, while the other set of information is ignored.
2. Although the original model is a network specially designed for mobile devices, with few model parameters, low calculation, and fast inference speed, the time required for model training is much higher than other lightweight network models.
3. The similarity of disease severity and the similarity of disease and physical damage make it difficult for the model to identify healthy and diseased wheat and the severity of diseased wheat, so there is a need to improve the model's detection capability.
# A High-Resolution Ultrasonic Ranging System Using Laser Sensing and a Cross-Correlation Method
## 1 Introduction
Research focused on quantitative analyses for ultrasound that uses acousto-optic diffraction began with [PERSON] and [PERSON] [31] in 1936. [PERSON] and [PERSON] [32] proposed the criterion for the Raman-Nath diffraction regime. [PERSON] [33] made analytical and numerical solutions for the extended Raman-Nath equations when the light was diffracted by ultrasonic pulses. With the development of these theories, some optical detection methods have been proposed. The Schlieren method is one of the optical detection methods used for visualizing sound fields [34; 35; 36]. It offers high measurement speeds for visualizing entire acoustic fields in real time, without disturbing the ultrasound fields. In addition, light diffraction tomography makes it possible for the noncontact detection of the characterizations of ultrasound, such as sound pressure, phase, and ultrasonic power [37; 38]. It is also suitable for air-coupled transducers [39]. However, all studies are aimed at characterization of ultrasound and sound field visualization. The acousto-optic effect would have great potential if used in ultrasonic ranging. The aim of this work is to show that the optical detection for ultrasound based on acousto-optic diffraction is helpful for improving the ultrasonic ranging resolution. The ultrasound transmitted from a transducer is referred to as a reference signal when it first passes through the laser beam. When the echo passes through the laser again, it generates another signal, carrying the distance information. As the optical detection is a noncontact detection, the reference signal is stable and clean compared to conventional methods. A cross-correlation method is used to determine the time interval between the reference signal and echoes, which directly reflects the distance between the laser beam and the surface of the target. This paper is organized as follows: The principles of acousto-optic diffraction and the pulse-echo ultrasonic ranging method are introduced first. Then, a pulse-echo ultrasonic ranging method based on a laser sensing system is established. The TOF according to the cross-correlation method, sound velocity, and ranging resolution is discussed. Ranging results of five targets are compared with the coordinate measuring machine. An aluminum test piece is used to evaluate the thickness detection results compared to the micrometer. The ranging uncertainty of the proposed system is discussed at the end of the document.
## 3 Results and Discussion
### Tensile Strength Analysis of Specimens  
Figure 9 displays the tensile strength and strength ratio of concrete specimens cast in three different environments over varying curing times. It is observed that the tensile strength of specimens in different environments shows a similar growth trend to the compressive strength: early-stage strength increases rapidly, while later-stage growth slows down. However, the tensile strength is less than one-tenth of the compressive strength. This could be due to the fact that, after hardening, the mortar's internal bonding forces are denser and can effectively resist compressive stress. In contrast, the internal bonding  
Figure 8: Change in compressive strength/strength ratio of test blocks with curing time under different pouring environments: (**a**) strength; (**b**) strength ratio.  
forces are weaker under tensile stress, resulting in significantly lower tensile strength compared to compressive strength. Similar to compressive strength, the tensile strength of specimens in different environments shows a noticeable decrease as the environment becomes more complex. This may be because the medium in the casting environment affects the specimen's properties as it becomes incorporated into the mortar during casting. In a dry environment, the impact on tensile strength is minimal; in pure water, the effect is relatively weak, with a 28 th-day tensile strength loss of only about 3%. However, in a slurry environment, the impact is more pronounced, with a 28 th-day tensile strength loss exceeding 20%, representing about 78.8% of the strength in the dry environment. Consistent with compressive strength, the tensile strength ratios (strength losses) for specimens cast in dry, pure water, and slurry environments indicate that the medium in the casting environment significantly affects the specimen's strength. As curing time increases, the strength of the specimen gradually increases, partially offsetting the strength impact of the casting environment.
# Retinex-Based Relighting for Night Photography
## 2 Retinex Theory
### Single-Scale Retinex  
Land introduced the center/surround Retinex [3]. It is based on the idea that brightness is calculated as the ratio of the value of a pixel to the mean value of the surrounding points and that these surrounding points have a density proportional to the inverse of the squared distance. This operation is similar to high-pass filtering, and [PERSON] et al. applied it using the Gaussian convolution [21; 36; 37]. Here, we define one version of this filter called single-scale Retinex (SSR). Smoothing filters can compute the surrounding field on each pixel, such as the mean of the neighboring pixels for mean filtering.  
Let a \(D\)-dimensional, \(B\)-tone image be \(\mathbf{f}:\mathcal{S}\mapsto\mathcal{R}\), where \(\mathcal{S}\subset\mathbb{R}^{D}\) is the spatial domain, \(\mathcal{R}\subset[0,B-1]^{d}\) is the range domain, and \(d\) is the color range dimension (generally, \(D=2\), \(B=256\), and \(d=3\)), respectively. The SSR output image, which is the reflectance, \(\mathbf{R}:\mathcal{S}\mapsto\mathcal{R}\), is defined by  
\[\mathbf{R}_{\mathbf{p}}=\frac{\mathbf{f}_{\mathbf{p}}}{g*\mathbf{f}_{\mathbf{p}}}, \tag{2}\]  
where \(\mathbf{p}\in\mathcal{S}\) is a pixel position. Equation (2) is based on the center-surrounding model of Retinex theory [21; 36; 37]. \(\mathbf{f}_{\mathbf{p}}\) and \(\mathbf{R}_{\mathbf{p}}\) are intensity vectors of the input and output images, respectively. The operator \(*\) indicates a convolution, and here, we use Gaussian filtering (GF) as a traditional SSR representation defined by  
\[g*\mathbf{f}_{\mathbf{p}}=\frac{\sum_{\mathbf{q}\in\Omega_{\mathbf{p}}}\exp\left(-\frac{\|\bm {p}-\mathbf{q}\|_{2}^{2}}{2\sigma^{2}}\right)\mathbf{f}_{\mathbf{q}}}{\sum_{\mathbf{q}\in\Omega _{\mathbf{p}}}\exp\left(-\frac{\|\mathbf{p}-\mathbf{q}\|_{2}^{2}}{2\sigma^{2}}\right)}, \tag{3}\]  
where \(\Omega_{\mathbf{p}}\subset\mathcal{S}\) is the set of the neighboring pixels of \(\mathbf{p}\) and \(\sigma\) is a smoothing parameter for the Gaussian distribution. Equation (2) means that the algorithm satisfies _color constancy_.  
In SSR, a higher \(\sigma\) results in an output image with better color fidelity but a lower dynamic range. A smaller \(\sigma\) leads to an enhanced dynamic range but greater color distortion. For better balancing, the multi-scale Retinex (MSR) is also introduced [36]; however, MSR requires complex parameter tuning [47]. The simpler SSR was used because there is no need to consider the color distortion of the reflectance in this paper, as will be discussed later.
## 3 Results and Discussion
### Experimental Results of Thermo-Physical Properties  
#### 3.4.1 Density  
The density variation of concrete reinforced with different volume percentages of RWG varying from 0 to 50%, at 28 and 90 days of curing samples is illustrated in Figure 14. At these two ages, the dry density decreased respectively by around 5% with respect to RC. This gain in lightness is mainly due to the density of RWG which is lower by about 3% than that of sand. It is of 2500 kg.m\({}^{-3}\) for RWG and of 2680 kg.m\({}^{-3}\) for sand. The same behavior was observed by [12; 15; 19] who substituted sand with glass with the same size in concrete.