esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Trajectory Prediction Model Based on AIS Data and ACDE-SVM  
### Trajectory Prediction Model  
When a ship is sailing, it mainly determines its navigational behavior by receiving the AIS data of a target ship to make timely and accurate collision avoidance decisions under harsh sea conditions and complicated encounter situations. In an actual voyage, the navigational behavior of different ships is mainly reflected in the time series changes in ship position, heading and speed. The navigational behavior of a ship at time \(t\) can be characterized as  
\[N_{t}=\{lon_{t},lat_{t},C_{t},v_{t},T_{t}\}, \tag{24}\]  
where \(lon_{t},lat_{t},C_{t},v_{t}\) and \(T_{t}\) represent the longitude, latitude, course, speed over ground and Unix time of the ship, respectively, at time \(t\). Aiming at the SVM characteristics of multivariable input and single variable output, the ship longitude and latitude parameters should be separately optimized. That is to say, in order to separately predict the longitude and latitude of the ship characterizing the nature of the ship, two SVR prediction models need to be established. The input characteristics of the two models are the same and the output characteristics are different. Generally speaking, the future navigational behavior of a ship is often the result of the interaction between current and previous navigational behaviors. Therefore, in order to improve the accuracy of trajectory prediction, the navigation behavioral of the past four moments, \(N_{t-3},N_{t-2},N_{t-1}\) and \(N_{t}\) and the time stamp of the next moment, \(t+1\), were used as input variables and the longitude and latitude of the next moment were used as the model output variables respectively:  
\[\begin{array}{l}Input=\{N_{t-3},N_{t-2},N_{t-2},N_{t},T_{t+1}\}\\ Output_{1}=\{lon_{t+1}\}\\ Output_{2}=\{lat_{t+1}\}\end{array} \tag{25}\]  
where \(N_{t-3},N_{t-2},N_{t-1}\) and \(N_{t}\) represent the navigational behaviors at times \(t-3\), \(t-2\), \(t-1\) and \(t\), respectively, from Equation (24).  
The model uses the root mean square error (\(RMSE\)), mean absolute error (\(MAE\)) and maximum absolute error (\(E_{MAX}\)) to measure the prediction accuracy, which are calculated as:  
\[RMSE=\sqrt{\frac{1}{S}{\sum_{t=1}^{S}(y_{t}-Y_{t})^{2}}}, \tag{26}\]  
\[MAE=\frac{1}{S}{\sum_{i=1}^{N}}\big{|}y_{t}-Y_{t}\big{|}, \tag{27}\]  
\[E_{MAX}{=\max}\big{|}y_{t}-Y_{t}\big{|}, \tag{28}\]  
where \(S\) represents the total sample size and \(y_{t}\) and \(Y_{t}\) represent the predicted and actual values, respectively.
## 3 Results and Discussion  
### Hydration  
In this study, the setting time and initial heat of hydration were measured to investigate the initial hydration characteristics of CNT-reinforced cement composites. Table 3 shows the setting times of CNT-reinforced cement composites. The setting times decreased as the CNT content increased. When the CNT content increased to 0.5%, the initial setting time was reduced by 6 min and the final setting time by 10 min. The initial setting and final setting were accelerated by 2.4% and 3.0%, respectively, compared to CRC00. Although the reduced time was short, the setting times decreased in proportion to the CNT content.  
Figure 7 shows the heat flow and cumulative heat releases of CNT-reinforced cement composites. In general, the hydration of OPC is divided into four periods: the initial period, induction period, acceleration period, and retardation period [45]. As shown in Figure 7a, the heat flow was not changed in the induction period regardless of the CNT content. As the CNT content increased, the acceleration period quickened, and the peak increased. In addition, the retardation period also decreased as the CNT content increased. This indicates that the addition of CNTs accelerated the hydration reaction of cementitious materials because the mix proportions except for the CNT content were the same in this study. This result was also reported by other researchers [18; 28]. The authors estimated that the CNTs inside the paste accelerated the hydration of OPC because it acted as nucleating agents [18; 28]. As the CNT content increased, the hydration reaction of cement accelerated, and the 48-h cumulative heat increased (see Figure 7b). In the case of CRC01, its cumulative heat was higher than that of CRC00 after 12 h; however, its 48-h cumulative heat was the same as that of CRC00. Therefore, the cumulative heat of CRC00 is likely to be higher than that of CRC01 after 48 h. As the amount of binders and water used in each specimen were the same, it was found that the addition of CNTs can accelerate the hydration reaction; however, the cumulative heat may decrease with the increase in CNT content. [PERSON] and [PERSON] [46] investigated the hydration kinetics of CNT-reinforced pastes with a W/C ratio of 0.4. They reported that the 60-h cumulative heat of a specimen with a CNT content of 0.6% compared to the binder was lower than that of a specimen with a CNT content of 0.3%, even though the addition of CNTs accelerated the hydration reaction.
## 5 Smart Radio Environment (SRE) with IRS
### Achieving a SRE through IRS  
IRS is a revolutionary technology with the potential to provide efficient use of spectrum in a radio environment by the software defined control of the propagating signals and to meet the requirement of emerging and future technologies [59]. Present wireless networks are designed and optimized at both the transmitter and receiver to compensate for certain effects of wireless channels. An IRS is essential in designing a programmable wireless environment as opposed to traditional uncontrollable wireless channels and thus serve a vital role in the sixth generation (6G) network [103]. The concept of SRE goes beyond reflecting incident signals and supports a wide range of applications such as reducing interference, enhancing security, and extending the range of communication to mention a few. To meet the requirement of SRE, wireless channels should be taken into account as part of the network design that needs to be optimized to support diverse performance metrics and quality of service requirements [78].
## 4 Results and Discussion  
### Modified Compact Tension Specim (MCTS)  
The crack path and other fracture parameters for seven modified compact tension specimens with different geometrical parameters depending on the hole location as shown in Table 1 are computed in this section. To investigate the influence of a hole on the fatigue crack growth path, a series of specimens with various holes were provided by varying their sizes and positions, as seen in Table 1. Both FRANC2D/L and ANSYS Mechanical were used to simulate each geometry. The studied material in this study is 7075-T6 aluminum alloy, which is commonly used in the aerospace industry for structural parts. The material has [PERSON]'s modulus of 72 GPa, a Poisson's ratio of 0.3, and fracture toughness of 29.66 Mpa m\({}^{0.5}\). Figure 3 shows a schematic representation of the specimen,  
Figure 2: FRANC2D/L procedure for crack propagation.  
which has a thickness of 4 mm with a plane stress state. The horizontal distance from the hole's origin to the center of the semi-circle is h1, and the shortest distance from its edge to the symmetrical axis of the specimen is v1. The corresponding values for a hole with a diameter of d2 are h2 and v2, respectively, which were adopted from the experimental data reported by [32].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{6}{c}{**Hole Parameters (mm) [32]**} \\ \cline{2-7}  & **d1** & **h1** & **v1** & **d2** & **h2** & **v2** \\ \hline G1 & & & & & & \\ G2 & 8 & 6.5 & 3 & & & \\ \hline G3 & 8 & 10.5 & 3 & & & \\ \hline G4 & 8 & 14.5 & 3 & & & \\ \hline G5 & 2 & 6.5 & 3 & & & \\ \hline G6 & 4 & 6.5 & 3 & & & \\ \hline G7 & 5 & 6.5 & 3 & 5 & 6.5 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical parameters for MCTS.  
Figure 3: 2D and 3D Schematic representation of specimen.  
Table 2 shows the variation in the mesh density in both software FRANC2D/L and ANSYS Workbench.
# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
## 2 Methodology  
### Experimental Road Sections  
The field experiment for this study was conducted along a 10 km segment of Zhongyuan Avenue, a major urban arterial road in Harbin, China (Figure 1). The tested route spanned from the intersection of Songpu Bridge and Zhongyuan Avenue to the junction of Zhongyuan Avenue and Xiang'an North Street. Zhongyuan Avenue was selected as the test site due to its representative traffic conditions, including high traffic volume, frequent acceleration and deceleration maneuvers, and significant congestion during peak hours, which are typical characteristics of urban arterial roads in China. Additionally, this section has been identified by local traffic authorities as a high-risk zone for traffic accidents, making it an ideal location for analyzing car-following behavior and collision risks. The roadway features a two-way, six-lane configuration divided by a central median, providing diverse driving scenarios for data collection. The experiment was conducted under clear weather conditions, with moderate temperatures and optimal visibility, ensuring minimal environmental interference.
## 5 Parameter Optimization Method Based on DE Algorithm
### Population Initialization  
According to the process of DE algorithm, the population P is initialized firstly. It includes T solutions. Each solution contains a set of randomly generated parameter values, which are all uniformly distributed within the corresponding possible range. Table 2 shows the data type and value range of all parameters.
###### Abstract  
In this paper, we propose an optimal time and power allocation scheme in a wireless power supply full-duplex (FD) relay system, where we consider the number of relay antennas in the energy harvesting stage. At the same time, the energy efficiency optimization problem of the system is structured, where optimization issues related to time allocation factors and power allocation are established. For the FD dual-antenna and the FD single-antenna energy harvesting system, energy efficiency function is proven to be a concave function over the time-switch factor, and the optimal time-switching factor is theoretically obtained using the Lambert function. Then, according to the given value range of the optimal time switching factor, the optimal power distribution scheme is obtained by analyzing the derivative function of the system energy efficiency and using the properties of the Lambert function. The time-switching factor and transmission power are optimally selected at the wireless power supply FD relay. Results reveal that the performance of energy efficiency of the dual-antenna energy harvesting at the FD relay outperforms that of the single-antenna. Moreover, our results demonstrate that FD relay systems always substantially boost the energy efficiency compared with half-duplex (HD) relay systems.  
full-duplex wireless power communication network; energy harvesting; time allocation algorithm; power allocation algorithm; half-duplex relay +
Footnote †: journal:  
_Future Internet_
## 3 Proposed Optimal Orchestration Process  
### Framework Overview and Technical Contributions  
Our framework makes three significant technical contributions to the field of network orchestration: the Enhanced Sub-slice (eSS) pipeline, the Optimal Network Slice Service Orchestration (ONSSO) framework, and the Company Network as a Service (CNaaS) model.  
The Enhanced Sub-Slice (eSS) integrates the LazyPredict module, which dynamically selects optimal machine learning algorithms based on real-time network conditions, ensuring adaptive resource allocation that responds to changing network demands.  
The proposed ONSSO framework builds upon the eSS pipeline to provide autonomous orchestration capabilities. The framework employs Ray Tune for hyperparameter optimisation, enabling fine-grained control over resource allocation decisions. This integration significantly improves the framework's ability to adapt to dynamic network conditions while maintaining optimal performance.  
The third contribution introduces the CNaaS model, which transforms how enterprise networks utilise 5G capabilities. This model enables organisations to leverage customisednetwork slices without managing complex infrastructure, representing a significant advancement in network service delivery.
# Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
## 2 Dataset and Method
### Method
#### 2.2.4 Prediction Box Deletion and Selection  
Mask R-CNN uses the non-maximum suppression (NMS) algorithm to rank the predictors of a certain category by confidence level, and filters them by calculating the intersection-over-union (IoU) to obtain the object. The best position is obtained by calculating the IoU; however, the detection box with the second highest confidence in this algorithm may be mistakenly deleted due to its high overlap with the detection box with the highest confidence. The improved Soft DIoU-NMS can effectively solve this problem [37].  
The Soft DIoU-NMS algorithm first sorts the detection boxes according to the confidence level and selects the highest scoring detection box as the benchmark, and the remaining detection boxes are calculated using the first linear decay. The detection box with the highest confidence level is retained, and the next highest confidence level is used as the benchmark. In this way, the confidence level remains unchanged after processing. Finally, the desired effect is achieved by comprehensive culling. The improved algorithm is shown in Equation (7). Compared with the original NMS algorithm, the complexity of the updated algorithm is practically unchanged, and the implementation is equally simple.  
\[\rm S_{i}=S_{i},\,IoU-R_{DIoU}(\mu,B_{i})<\varepsilon \tag{7}\] \[\rm S_{i}=S_{i}\,(1-IoU(\mu,B_{i})),\,IoU-R_{DIoU}(\mu,B_{i})>\varepsilon\]  
where \(\rm S_{i}\) is the confidence score of the current category, \(\rm R_{DIoU}\) is the penalty term of the \(\rm D_{IoU}\) loss function, \(\rm B_{i}\) denotes all the compared detection boxes in the current category, and \(\mu\) denotes the detection box with the highest confidence among all the prediction boxes, generally taking \(\varepsilon=0.5\).
# Retinal Nerve Fiber Layer in Patients with Alcohol Use Disorder
## 3 Results
Finally, in the GCL, we observed that all quadrants were reduced in the AUD patients compared to the controls (Table 5) with the exception of the superior and inferior temporal area, for the left eye. For the right eye we only found differences in the inferior nasal quadrant. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Quadrants (\(\mu\)m)** & \begin{tabular}{c} **Control Subjects** \\ _n = 21_ \\ \end{tabular} & \begin{tabular}{c} **SD Control** \\ **(\(\pm\))** \\ \end{tabular} & \begin{tabular}{c} **AUD Patients** \\ _n = 21_ \\ \end{tabular} & \begin{tabular}{c} **SD Alcohol** \\ **(\(\pm\))** \\ \end{tabular} & \
\begin{tabular}{c} **Difference CI** \\ \end{tabular} &
\begin{tabular}{c} _p Value_ \\ \end{tabular} \\ \hline Superior & & & & & & \\ \hline Right & 122.57 & 22.16 & 124.05 & 16.10 & \(-\)13.56 to 10.6 & 0.806 \\ \hline Left & 125.14 & 20.18 & 120.76 & 23.31 & \(-\)9.22 to 17.98 & 0.519 \\ \hline Inferior & & & & & & \\ \hline Right & 129.48 & 22.09 & 129.52 & 23.99 & \(-\)14.43 to 14.33 & 0.995 \\ \hline Left & 128.62 & 21.09 & 128.86 & 20.18 & \(-\)13.11 to 12.64 & 0.970 \\ \hline Temporal & & & & & & \\ \hline Right & 72.29 & 72.29 & 67.43 & 67.43 & \(-\)1.4 to 11.11 & 0.124 \\ \hline Left & 74.71 & 74.71 & 68.43 & 13.53 & \(-\)1.95 to 14.52 & 0.131 \\ \hline Nasal & & & & & & \\ \hline Right & 83.00 & 16.60 & 85.05 & 16.34 & \(-\)12.32 to 8.22 & 0.689 \\ \hline Left & 74.86 & 15.13 & 69.67 & 17.00 & \(-\)4.84 to 15.23 & 0.302 \\ \hline \hline \end{tabular} Mean (SD) value of the average peripapillary RNFL thickness (\(\mu\)m) for each of the 90\({}^{\circ}\) quadrants of the control and patient groups. Confidence intervals for the difference in means (Difference CI). Significance of difference \(t\)-test: \(p\)-value.  
\end{table} 
Table 4: Peripapillary RNFL thickness (\(\mu\)m) in each of the 90\({}^{\circ}\) quadrants around the optic disk. Figure 5: Box diagram corresponding to macular thickness. Values are expressed using median, percentiles, and extreme values. No significant correlations were found between RNFL thickness and alcohol consumption (using the SDUs) in any of the quadrants explored. However, we did find significant correlations between retinal thickness in the macular area, and the inferior and nasal peripheral areas (see Tables 6 and 7), the nerve, and peripheral temporal and nasal areas (see Tables 8 and 9), and cognitive impairment. \begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Inferior Peripheral**} & **TEDCA** \\ \hline \multirow{3}{*}{Inferior peripheral} & Pearson’s Correlation & 1 & \(-\)0.352 \({}^{*}\) \\ \cline{2-4}  & Sig. (bilateral) & & 0.035 \\ \cline{2-4}  & n & 42 & 36 \\ \hline \multirow{3}{*}{TEDCA} & Pearson’s Correlation & \(-\)0.352 \({}^{*}\) & 1 \\ \cline{2-4}  & Sig. (bilateral) & 0.035 & \\ \cline{2-4}  & n & 36 & 36 \\ \hline \end{tabular} \({}^{*}\) Correlation is significant at the level 0.01 (bilateral).  
\end{table} 
Table 6: Correlation between cognitive impairment and macular area in the inferior peripheral quadrant.
# Dilated Multi-Temporal Modeling for Action Recognition
## 3 Methods
### Module Design
#### 3.2.2 Partial Channel Modeling  
We employed an partial channel modeling approach to efficiently integrate temporal information at varying scales. This enables our model to combine the information from multiple scales in a way that retains the relevant details while minimizing the amount of additional computation required. This is particularly useful for applications where computational efficiency is a priority, such as real-time video action analysis or large-scale data processing.  
The partial channel modeling strategy involves separating the feature map along the channel dimension into different parts for the purpose of modeling different-scale temporal information. Partial channels are utilized to extract multiple-scale temporal information using dilated convolutions with varied dilation rates. The utilization of partial channels in the extraction of multiple-scale temporal information, as opposed to utilizing multi-blocks, is an efficient strategy that does not bring additional computations.  
We separate the feature map into three parts in the channel axis:  
\[X_{1},X_{2},X_{3}=Separate(X,\gamma), \tag{4}\]  
where _Separate_ denotes the operation that divides the input by the channel dimension, and \(\gamma\) is a hyperparameter that divides the feature map into three parts: \(X_{1}=X_{[0:\gamma C)}\), \(X_{2}=X_{[\gamma C:2\gamma C)}\), \(X_{3}=X_{[2\gamma C:C)}\).  
The first and second parts focus on capturing short and long temporal semantic information, respectively. The third part is used to balance the trade-offs between the other parts and to maintain a good overall representation of the original input. This separation is performed through depth-wise convolutional operations on the first and second parts with different dilation rates as described above, without any operation on the third part, followed by concatenation. These operations can be described as follow:  
\[\begin{split} Y_{1}&=Conv(X_{1},G_{1}(X_{1}),D_{1} ),\\ Y_{2}&=Conv(X_{2},G_{2}(X_{2}),D_{2}),\end{split} \tag{5}\]  
where \(G_{1}\) and \(G_{2}\) are two branches with the same structure to generate dynamic kernels for the following convolution; \(D_{1}\) and \(D_{2}\) are the dilation rates 1 and 2 for the convolution.  
It is simply concatenated with the first, second and third parts in the channel axis to keep the original shape of the feature map:  
\[Y=Concatenate(Y_{1},Y_{2},X_{3}). \tag{6}\]  
The resulting feature maps are then fed into subsequent modules of the network for further processing.  
In summary, the use of different dilated convolutions allows the network to capture information at multiple scales, each one of them focusing on a different rate of motion, while retaining relevant details and minimizing computational cost compared with using multi-block modeling. This strategy is useful for applications where computational efficiency is a priority, such as real-time video analysis or large-scale data processing.
## 3 Results and Discussion  
### Micro-Hardness and Metallography  
The micro-hardness profile along the radius of the rebar shown in Figure 4 gives a good picture of the effect of the heat treatment process that the material has undergone. It is further useful to check if the product meets the tolerances specified and to compare the specimens with specific microstructures produced by heat treating in the lab [40]. The micro-hardness was measured using a LECO Micro-Hardness tester with a load of 500 g and a dwell time of 15 s.  
The microstructure was investigated in the transverse as well as in the longitudinal cross-section of the rebar, which after polishing were etched with Nital for optical microscopy. Figure 5 shows the microstructures observed at different positions on the transverse cross-section of the rebar. On the top of Figure 5 where the positions of the individual microstructures are marked, a clear demarcation can be seen about 3 mm beneath the surface of the rebar. The core shows a mixture of ferrite and pearlite whereas near the surface tempered martensite can be seen inside prior austenite grains. In the transition zone where the cooling rate was not fast enough for martensite formation, a bainitic microstructure is observed. The microstructures obtained from the longitudinal cross-section were similar to those of the transverse cross-section and are not shown here. The longitudinal cross-section was further used to investigate any difference in microstructure on either sides of the transverse ribs that might have arisen due to the water being sprayed from one direction only inside the quenching chamber of the manufacturing unit. No differences were observed in the grain sizes, hardness, as well as in the thickness of the tempered martensite layer.  
\begin{table}
\begin{tabular}{l l} \hline Radiation & Cr K\({}_{\alpha}\) \\ Wavelength & 0.229 nm \\ Spot size & 1 mm \\ Voltage & 30 kV \\ Current & 8 mA \\ Exposure time & 80 s \\ Tilt angles, \(\psi\) & \(\pm 42^{\circ}\), \(\pm 35.4^{\circ}\), \(\pm 28.2^{\circ}\), \(\pm 19.5^{\circ}\), \(0^{\circ}\) \\ Bragg angle, \(2\theta\) & 156.4\({}^{\circ}\) \\ Young’s modulus & 206.6 GPa (cf. Section 3.3) \\ Poisson’s ratio & 0.273 (cf. Section 3.3) \\ Diffracting plane & Ferrite (211) \\ \hline \end{tabular}
\end{table}
Table 2: X-ray diffraction parameters for measuring residual stresses.  
Figure 4: Hardness profile along the radius of the rebar.