esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Modeling  
The phase composition diagrams of the selected alloys are presented in Figure 3. They were obtained through thermodynamic modeling using the Thermo-Calc v.2022a software package with the TCNI10 database. These diagrams allow us to compare material structures with experimentally obtained results. According to the calculations, all investigated nickel alloys tend to form topologically close-packed (TCP) phases (P- and \(\sigma\)-phase) as well as the ordered Ni\({}_{2}\)(Cr,Mo) phase. These results correlate well with the literature data [9; 14; 31; 34; 35; 39; 42; 43; 45]. There is a possibility of formation of \(\alpha\)-Cr particles in the Hastelloy\({}^{\text{\textcircledr}}\) G-35\({}^{\text{\textcircledr}}\) alloy, and this was confirmed experimentally [42]. The more complex nature of the phase composition diagram of Hastelloy\({}^{\text{\textcircledr}}\) B-3\({}^{\text{\textcircledr}}\) alloy is due to its chemical composition; Ni\({}_{4}\)Mo and Ni\({}_{3}\)Mo intermetallic compounds can be formed as a result of high-temperature exposure. The presence of these intermetallic compounds as well as the possible formation of a \(\sigma\)-phase can negatively affect the ductility and corrosion resistance of the alloy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Alloy**} & \multicolumn{10}{c}{**Content, wt.\%**} \\ \cline{2-13}  & **Ni** & **Cr** & **Mo** & **Fe** & **Mn** & **Ti** & **Cu** & **Co** & **Al** & **Si** & **S** & **C** & **P** \\ \hline
**Hastelloy\({}^{\text{\textcircledr}}\) G-35\({}^{\text{\textcircledr}}\)** & 57.2 & 33.1 & 8.2 & 0.8 & 0.22 & 0.01 & 0.03 & 0.07 & 0.19 & 0.04 & 0.002 & 0.012 & 0.003 \\
**(UNS N06035) (\({}^{\text{\textcircledr}}\))** & 63.2 & 23.2 & 12.8 & 0.47 & 0.03 & 0.08 & & & 0.11 & 0.06 & 0.003 & 0.005 & 0.004 \\
**VDM\({}^{\text{\textcircledr}}\) Alloy 59** & 60.4 & 22.6 & 15.4 & 0.9 & 0.19 & & 0.01 & 0.01 & 0.23 & 0.02 & 0.002 & 0.002 & 0.002 \\
**(UNS N06059)** & 67.05 & 16.12 & 15.62 & 0.84 & 0.02 & & 0.01 & 0.01 & & 0.01 & 0.002 & 0.002 & 0.003 \\
**VDM\({}^{\text{\textcircledr}}\) Alloy C-4** & 67.1 & 1.6 & 28.5 & 1.41 & 0.68 & 0.01 & 0.1 & 0.05 & 0.33 & 0.04 & 0.001 & 0.002 & 0.004 \\
**(UNS N10675) (\({}^{\text{\textcircledr}}\))** & 67.1 & 1.6 & 28.5 & 1.41 & 0.68 & 0.01 & 0.1 & 0.05 & 0.33 & 0.04 & 0.001 & 0.002 & 0.004 \\ \hline \hline \end{tabular}  
* \({}^{(\text{\textcircledr})}\) Hastelloy\({}^{\text{\textcircledr}}\) G-35\({}^{\text{\textcircledr}}\) also contains about 0.01% W. \({}^{\text{\textcircledr}}\) KhN62M-VI also contains about 0.03% Nb. \({}^{\text{\textcircledr}}\) Hastelloy\({}^{\text{\textcircledr}}\) B-3\({}^{\text{\textcircledr}}\) also contains about 0.24% W.  
\end{table}
Table 1: Composition of alloys according to the manufacturers’ certificates.  
Figure 3: _Cont._
# Influence of Roll Diameter on Material Deformation and Properties during Wire Flat Rolling
## 3 Results
### Deformation Shape with Roll Diameter  
Figure 7a shows a comparison of the wire shape during wire flat rolling, and Figure 7b shows the variations in \(b\) and \(W\) with respect to the roll diameter. The \(b\) and \(W\) of the flattened wires increased as the roll diameter increased. [PERSON] and [PERSON] [27] reported that \(W\) increases with the roll diameter during wire drawing with a flat roll, which is consistent with the present result. As the spreading of the wire increased with the roll diameter, \(R_{A}\) decreased with the roll diameter, as shown in Figure 7c. Meanwhile, it is noteworthy that \(R_{A}\) was approximately half the value of \(R_{h}\) owing to the significant spreading effect of the workpiece during wire flat rolling.  
Figure 6: Comparison of the (**a**) \(\varepsilon_{max}\), \(\varepsilon_{min}\), and \(\varepsilon_{ave}\) along the horizontal and vertical directions of the flattened wire and (**b**) \(\mathit{SIF}_{h}\) and \(\mathit{SIF}_{v}\) of the wire against roll diameter. Theoretical strain was obtained using Equation (3).  
Figure 7: Comparison of (**a**) shape change, (**b**) \(b\) and \(W\), and (**c**) \(R_{A}\) against roll diameter.
# Nano-Interaction Mechanism Between Crumb Rubber and Asphal Components: A Molecular Dynamic Study
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.2 Lame Constant  
In molecular dynamics calculations, based on the multidimensional stress-strain relationship, the Lame constants \(\lambda\) and \(\mu\) can also be obtained, which are called Lame's first parameter and second parameter, respectively. There are some differences in the significance of the two in terms of different use conditions. In the hydrodynamic and fully elastomeric environments, \(\mu\) represents dynamic viscosity and shear modulus, respectively, while \(\lambda\) has no exact physical meaning and is related to the material compressibility. A larger Lame constant generally indicates a better mechanical performance of the material.  
A comparative analysis of the results of the asphalt model Lame constant for different dosages and temperatures is shown in Figure 18.  
Overall, the Lame constants \(\lambda\) and \(\mu\) of base asphalt showed a decreasing trend with increasing temperature. As mentioned above, asphalt gradually transforms from elastic material to elastic-plastic, plastic, and liquid viscous as the temperature increases. It reduces its resistance to compression deformation and shear deformation (or viscosity) gradually. It was also shown that in evaluating the mechanical properties of base asphalt, the two Lame constants agreed with the bulk and shear modulus.  
After mixing CR, the Lame constant change rule under different dosages was more complicated. However, overall, 20% of the CRMA dosage was better. This may be related to the construction of the model; simplifying the model means the CR molecular number is less, resulting in CR doping after the stress-strain anisotropy increases, ultimately caused by the calculation of the results of the variability increases. However, compared with the base asphalt, there was a certain decrease in the Lame constant \(\lambda\) at 10% and 30% dosage. The Lame constant increased at 20% dosage, especially the \(\mu\), which grew significantly at medium and high temperatures. This was more consistent with the change rule of shear modulus at 20% dosage.
# Low-Latency Bit-Accurate Architecture for Configurable Precision Floating-Point Division
## 1 Introduction
Some techniques are applied in the hardware architecture design:  
* Leading Zero Detection module to transfer subnormal inputs; Exception Judgement module to check exception conditions;
* Finite State Machine module to reduce the number of multipliers and to perform basic fast division steps;
* Quotient Selection Unit module to finish the critical part of our proposed Predict-Correct algorithm and to gain the most approximative 32-bit quotient per cycle;
* Rounding Unit module to ensure the accuracy of unit at last place of quotient. The remaining paper is structured as follows. In Section 2, two classical division implementations employing very high-radix algorithm are reviewed and higher radix division implementation is discussed. In Section 3, a novel Predict-Correct algorithm based on fast division and its mathematical arguments are presented. In Section 4, general architecture of the proposed multi-precision FP division and its main techniques are detailed. In Section 5, the results of our hardware implementation are reported and compared with prior works. Finally, in Section 6, conclusion is drawn.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 3 Case Study
### Data Acquisition  
Figure 7 shows the construction layout of the servo support system for the project. The servo support system combines servo control technology with traditional steel supports to control wall deflections. Specifically, the system uses load cells to monitor axial force and controls jack displacement to adjust the support force in real time. By detecting changes in axial force, it precisely adjusts the jack displacement to control wall deflection and adapt to environmental changes. The dataset for this study is sourced from measurements at four displacement points on the CX18 inclinometer and the servo axial forces of four nearby steel supports. The inclinometer employed is a high-precision instrument designed to measure small displacements with a measurement error less than \(\pm\)0.5 mm. In the temporal dimension, the monitoring data span from 22 October 2019 to 26 June 2020. However, due to sparse data after 25 January 2020, the monitoring data from 22 October 2019 to 25 January 2020 are selected as the dataset, lasting a total of 96 days. Figure 8 shows the variation trends of axial forces and wall deflections during the excavation process. As excavation progresses, wall deflection gradually increases. At the same time, the axial forces of the supports fluctuate around a fixed value since installation. There appears to be a link between these fluctuations and wall deflections. For example, when axial forces fluctuate, wall deflections tend to increase. This suggests that adjusting axial forces may help control or reduce wall deflections during excavation.
## 3 Results
### Comparison of Predicted Demand and Available Capacity
Some facilities are underutilized, with capacity far exceeding demand, while others occasionally face demand that approaches or exceeds capacity, highlighting potential inefficiencies in resource allocation and the need for model refinement.
# Deep Learning-Based Occlusion Handling of Overlapped Plants for Robotic Grasping
## 3 Materials and Methods
### Grasp Detection  
We developed a novel method for detecting the robotic grasp of a segmented plant cutting based on Conventional Image Processing (CIP). In general, a grasp specifies how a robot end-effector (gripper) can be arranged to safely grip an object and lift it without slipping it off. A grasp holds the information about the grasping point, the grasping orientation, and the opening width of the robot gripper [32]. Since the objective of the intended robotic grasping application is to grasp the plant cuttings classified as Target Cuttings and Singularized Cuttings (see Section 3.1), the developed method was designed to detect the grasps for full plant cuttings and not for the Occluded Cuttings or Remains.  
Figure 5: Instance segmentation masks produced by AdaptIS for various point proposals.  
Since the plant cuttings are small and delicate, a small two-finger gripper or tweezers with adequate distance between the tips can serve as a gripper for grasping the plant cuttings from flat surfaces. The oriented rectangle [33] is used here for the grasp representation, which indicates the position and orientation of a two-finger gripper before closing on an object:  
\[g\ =\ \{x,\ y,\ \theta,\ h,w\} \tag{2}\]  
where \((x,y)\) is the center of the rectangle in the image coordinate system, \(\theta\) is the orientation of the rectangle with respect to the horizontal axis of the image coordinate system, \(h\) is the height of the rectangle or, here, it is the thickness of each finger that is a constant value, and \(w\) is the width or the distance between the two robotic gripper fingers. The grasp representation with a pair of points [34], where each point is the end position of each finger of the gripper, was also used during the grasp detection process to find the optimal oriented rectangle. To succeed in an actual grasp of a plant cutting, the predicted grasps must meet the following criteria:  
* The center of the gripper (center of the oriented rectangle) must be on the stem of the plant cutting.
* The orientation of the predicted grasp should be aligned with the direction of the plant cutting's stem.
* The grasping point must have sufficient distance to the plant leaves to accommodate the open gripper and to avoid collision with leaves.  
The pipeline of the developed algorithm for grasp detection is shown in Figure 6 and the algorithm is explained in detail in the following subsections.  
Figure 6: Pipeline of the proposed algorithm for detection of the robotic grasp of a segmented plant cutting.
## 4 Applications of AI in Modern Communication Networks
### Traffic Prediction and Load Balancing  
AI is instrumental in predicting network traffic patterns and optimizing load balancing across networks, ensuring that traffic is routed efficiently to avoid congestion and reduce bottlenecks [119]. By analyzing historical data and real-time traffic flows, machine learning algorithms can forecast future network demands, allowing for proactive adjustments in network configuration [120]. This predictive capability helps in anticipating peak traffic hours, unexpected surges, and network failures, enabling better resource allocation [121].  
Additionally, AI enhances load balancing by dynamically distributing network traffic across multiple servers or paths based on the predicted traffic patterns [122]. This prevents any single node from being overwhelmed, ensuring consistent network performance even during periods of high demand. AI-driven load balancing algorithms can learn from past traffic data and adapt to new patterns, offering more flexibility and efficiency compared to traditional static load balancing methods [123].  
By improving both traffic prediction and load balancing, AI ensures that networks can maintain optimal performance, minimize latency, and guarantee a smooth user experience, even under heavy load conditions. This dynamic approach to network management not only boosts efficiency but also supports scalability in growing communication infrastructures. To provide further insights, I conducted control experiments to evaluate the latency and energy consumption trade-offs for various AI models.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 2 Proposed Approach
### Compactness-Based Saliency Map
#### 2.2.2 Compactness-Biased Gaussian Model  
Center-prior introduces the location prior knowledge of the salient object. It is inspired by the observation that salient object always appears at the center of image. In [38; 39], center prior has been used in the form of Gaussian model:  
\[G(i)=\exp\left[-\left(\frac{\left(x_{i}-\mu_{x}\right)^{2}}{2\sigma_{x}^{2}}+ \frac{\left(y_{i}-\mu_{y}\right)^{2}}{2\sigma_{y}^{2}}\right)\right] \tag{9}\]  
where \(x_{i}\) and \(y_{i}\) are the central coordinates of the superpixel \(v_{i}\), \(\mu_{x}\) and \(\mu_{y}\) represent the coordinates of the image center. However, as Figure 5c shows, the center-biased Gaussian model is not effective and may wrongly highlight the background. Inspired by [12], we present the compactness-biased Gaussian model as follows:  
\[G_{com}(i)=\exp\left[-\left(\frac{\left(x_{i}-w_{x}\right)^{2}}{2\sigma_{x}^{2} }+\frac{\left(y_{i}-w_{y}\right)^{2}}{2\sigma_{y}^{2}}\right)\right] \tag{10}\]  
where \(w_{x},w_{y}\) are defined as:  
\[\left\{\begin{array}{l}w_{x}=\frac{\sum_{i=1}^{N_{FG}}ComSal(i)\cdot x_{i}}{ \sum_{i=1}^{N_{FG}}ComSal(i)}\\ w_{y}=\frac{\sum_{i=1}^{N_{FG}}ComSal(i)\cdot y_{i}}{\sum_{i=1}^{N_{FG}}ComSal(i) }\end{array}\right. \tag{11}\]  
where \(N_{FG}\) is the number of the superpixel in the foreground seeds. To avoid the disturbances of the background seeds, we merely choose the foreground seeds to participate in calculation. We set \(\sigma_{x}\)= \(0.15\times H\) and \(\sigma_{y}=0.15\times W\), where \(W\) and \(H\) respectively denote the width and height of the image. Figure 5d shows the effects of the proposed compactness-biased Gaussian model. As it is illustrated, compared to the general Gaussian model, the proposed model is more precise to highlight the salient regions and suppress the background regions.  
With the compactness-biased Gaussian model, we refine the initial saliency map generated by Equation (8):  
\[S_{Gauss\_Com}(i)=ComSal(i)\cdot G_{com}(i) \tag{12}\]  
We obtain the final compactness-based saliency map using the similarity \(a_{ij}\) as the weight of the linear combination:  
\[S_{fg\_com}(i)=\sum_{y_{j}\in FG}S_{Gauss\_Com}(j)\cdot a_{ij} \tag{13}\]
## 5 Machine Learning (ML) Techniques
### General Regression Neural Network (GRNN)  
The general regression neural network (GRNN) is a type of artificial neural network developed to solve regression problems, i.e., predict continuous values in response to a set of input variables. The GRNN was originally proposed by [PERSON] in 1991 as an extension of the probabilistic classification algorithm called probabilistic neural network (PNN) [48].  
GRNN is a feedforward neural network that stands out for its simplicity and ease of implementation. It is a variant of the RBF network, which uses radial basis functions to perform interpolation and extrapolation of data and consists of four main layers: the input layer, the pattern layer, the summation layer, and the output layer [48].  
In the input layer, the input variables are normalized and provided as input to the network. Then, in the pattern layer, the network compares the input patterns with stored  
Figure 5: RMSE in relation to the number of neurons in the hidden layer.  
Figure 6: MLP network implemented.  
training patterns. These training patterns consist of the input and corresponding output values' combination. Each training pattern has an associated activation function that measures the similarity between the input pattern and the training pattern. Typically, the Gaussian activation function is used where the output is a measure of the distance between the input pattern and the training pattern.  
The summation layer receives the activation values calculated in the pattern layer and performs a weighted sum of these values. The weights associated with the activation values are determined by the Gaussian activation function, which assigns a higher weight to training patterns closer to the input pattern.  
Finally, the output layer combines the outputs from the summation layer to generate a final response, which is the value predicted by the GRNN. The output is calculated by the patterns layer's output values' weighted average, where the weights are determined by the Gaussian activation function.  
One of the advantages of GRNN is its ability for fast and efficient learning. Once the network is trained with corresponding input and output patterns, it can rapidly predict output values for new input patterns without the need for an iterative process of weight adjustment [48].  
Additionally, the GRNN is known for being a robust neural network capable of handling noise and incomplete data. It also has a low number of hyper-parameters. In this case, it had only one, the smooth parameter, which for this model was set to 0.0039, making it easier to implement and adjust.  
However, it is important to note that the GRNN may be more suitable for simple regression problems with a limited number of input variables. For more complex problems or large datasets, other neural network architectures such as deep neural networks may be more appropriate.  
Figure 7 represents the architecture of the GRNN used in this research, considering the same inputs as the MLP and also predicting RSSI and SNR as output.
# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 3 Results
### Spatiotemporal Changes of Cultivated Land
#### 3.1.3 Landscape Pattern  
The spatial configuration characteristic of paddy and dry farmland are shown in Table 2 by the PD, LPI, and MA indices. For paddy field, the PD index indicated a declining trend while LPI index and MA showed an increasing trend during the study period. In detail, PD always indicated a growth trend while LPI increased during 1990-2010 and then decreased slightly from 2010 to 2015. MA showed a fluctuant upward trend, with a slight decline in 2010. For dry farmland, PD decreased in 1990-2000, with a slight increase in 2000-2010 and 2010-2015. LPI index of dry farmland grew from 1990 to 2000 and decreased since 2000. MA index showed an increasing trend during 1990-2010, especially during 1990-2000, and then indicated a declining trend during 2010-2015.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Time** \\ **Periods** \\ \end{tabular} } & \multicolumn{2}{c}{**Cultivated Land**} & \multicolumn{2}{c}{**Paddy**} & \multicolumn{2}{c}{**Dry Farmland**} \\ \cline{2-7}  & **CA(ha/y)** & **CR(\%/y)** & **CA(ha/y)** & **CR(\%/y)** & **CA(ha/y)** & **CR(\%/y)** \\ \hline
1990–2000 & 48,032.89 & 1.51 & 11,543.22 & 5.72 & 36,489.67 & 1.23 \\
2000–2010 & 11,762.21 & 0.32 & 3905.64 & 1.23 & 7856.57 & 0.24 \\
2010–2015 & 6764.26 & 0.18 & 22,575.24 & 6.34 & \(-\)15,810.98 & \(-\)0.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Change area and change rate of cultivated land during different time periods.  
Figure 3: Land-use distribution in different years. (**a**) 1990; (**b**) 2000; (**c**) 2010; (**d**) 2015.  
Three indices were applied to describe the aggregation features of paddy and dry farmland as is shown in Table 3. Larger values of COHESION and AI represent more aggregation, while a smaller value of SPLIT represents more aggregation. For paddy field, COHESION and AI index indicated a growth trend while SPLIT index showed a downward trend during the study period. In detail, AI showed a growth trend all the time while COHESION showed a slight decreased trend during 2000-2010 and indicated an increasing trend during other periods. SPLIT showed a decreased trend. The changes of these indices indicated that paddy in the West Songnen plain have become aggregated since 1990. For dry farmland, the changes of these indices are not as regular as paddy field. In general, COHESION and AI index indicated a growth trend while SPLIT index indicated a downward trend during the study period. COHESION increased in 1990-2000, with a slight growth in 2000-2010 and a slight decline 2010-2015. AI index of dry farmland increased from 1990 to 2010 and decreased since 2010. SPLIT index showed a decreasing trend during 1990-2000 and then showed an increasing trend during 2000-2015. Dry farmland in the West Songnen plain have become aggregated since 1990, but not as obvious as paddy. Dry farmland aggregated first and then was split slightly.