esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prediction Based on BP Neural Network
### BP Neural Network Algorithm  
The basic components of a BP neural network include an input layer, a hidden layer, and an output layer [13]. Where the input layer can be set according to the actual problem itself, the attributes of the hidden layer, including the number of layers and the number of neuron nodes, need to be filtered and optimised according to the actual problem and the objective to be optimised. Different numbers of layers and nodes may cause the neural network to fall into an underfitting or overfitting state. Neuron nodes in different layers of a BP neural network are linked to each other through a functional relationship, but the same layer does not directly have this relationship. Neural networks at different levels are mainly composed of a number of neurons, i.e., 'M-P neurons'. In this paper, the BP neural network is applied to the optimisation of loading paths in the hydroforming of aluminium alloy variable diameter tubes. The internal pressure, axial feeds, and friction coefficient of the tube-forming process are used as input layer parameters of the model. Minimum wall thickness, maximum wall thickness, and the maximum forming height of the optimisation target are used as output layer parameters. The idea of modelling the control of the hydroforming process of the large section rate of change tubes based on the BP neural network is shown in Figure 7.  
Standard BP Neural Network Model  
The number of nodes in each layer needs to be determined before building the neural network model in addition to the determination of the transfer function. The design of nodes in each layer of a general BP neural network needs to be analysed specifically according to different practical problems. For the forming parameter prediction model of the aluminium alloy and the large section rate of the change tube hydroforming process  
Figure 7: (**a**) M-P neuron model. (**b**) Machine learning-based modelling of the large section rate of change tube hydroforming process control.  
A traditional BP neural network prediction model was constructed with the specific model parameters presented in Table 4. When predicting the forming results for the sample pool selected from Latin hypercube sampling had obtained in Table 5, the results for the minimum and maximum wall thickness forming parameters with respect to the \(R^{2}\) evaluation index are 0.827 and 0.867, and the resulting MAE and RMSE values are below 0.1. However, the results for the maximum forming height regarding the \(R^{2}\) evaluation index were 0.748, and the MAE and RMSE results were 0.482 and 0.672, which still have a large error.
# Cuckoo Coupled Improved Grey Wolf Algorithm for PID Parameter Tuning
## 5 Conclusions  
The cuckoo search optimisation coupled with the improved grey wolf optimisation algorithm for tuning the PID parameters proposed in this study is improved in three aspects: initial population, control parameters, and position equations. The improved algorithm can effectively avoid premature convergence, accelerate the iteration time, and improve parameter accuracy. Two simulation experiments are conducted. First, eight benchmark functions were used to verify the superiority of the improved algorithm. The experimental results show that the overall iteration speed is accelerated, and it is not easy to fall into the local optimal solution for multimodal benchmark function problems.  
By comparing the PID parameter tuning method proposed in this paper with other parameter tuning methods, it can be concluded that the proposed CSO_GWO method significantly enhances the dynamic performance of motors under PID control. The method's efficiency in tuning PID parameters allows swift computations, facilitating its application in practical motion control scenarios. Even in the presence of external influences, this approach substantially improves motor control performance, preventing the parameter tuning process from converging to a suboptimal region due to external disturbances. Fine-tuning the three key parameters of the PID algorithm achieves optimal motor dynamic performance.  
However, certain limitations persist. The computational complexity of this algorithm and its extensive calculation procedures render it unsuitable for real-time online adjustment of controller parameters. Moreover, when applied to more complex advanced control algorithms, the cumulative computational load of tuning parameters and the intricate advanced control algorithm significantly increase the overall process time. Consequently, future research needs to focus on further optimising the algorithmic procedures and evolving the proposed method to be compatible with a broader array of advanced algorithms.  
Conceptualization, B.X.; Methodology, K.C.; Formal analysis, X.L.; Investigation, S.L.; Data curation, X.Z.; Funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Equipment Advance Research Field Foundation, grant number: 80923010202.  
Not applicable.  
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. The codes used and/or analyzed during the current study are available from the corresponding author on reasonable request. The data are not publicly available due to the involvement of national defense and military projects in the project, it has not been fully disclosed yet.  
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.
## 5 Experimental Results
### Comparison of Outcomes
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Classifier Type** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & \(F\)**-Measure** & **MCC** & **ROC Area** & **PRC Area** & **Class** \\ \hline LIBLINEAR & 1.000 & 1.000 & 0.787 & 1.000 & 0.881 & 0.000 & 0.500 & 0.787 & Y \\  & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.500 & 0.213 & N \\ \hline LIBSVM & 1.000 & 0.011 & 0.997 & 1.000 & 0.999 & 0.993 & 0.995 & 0.997 & Y \\  & 0.989 & 0.000 & 1.000 & 0.989 & 0.995 & 0.993 & 0.995 & 0.991 & N \\ \hline SVM-RBF & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & Y \\  & 1.000 & 0.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & N \\ \hline LR & 1.000 & 0.951 & 0.822 & 1.000 & 0.924 & 0.000 & 0.650 & 0.822 & Y \\  & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.585 & 0.265 & N \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy by class: packets received (yes (Y)/no (N)). LR: logistic regression; MCC: [PERSON]’s correlation coefficient; PRC: precision–recall curve; ROC: receiver operating characteristic. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Using Traditional Training Set**} & \multicolumn{6}{c}{**Using Cross-Validation Set**} \\ \cline{2-9}
**Traffic Dataset** & \begin{tabular}{c} **Accuracy** \\ **(SVM-RBF)** \\ **\%** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBSVM)** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBSVM)** \\ \end{tabular}  & \begin{tabular}{c} **Logistic** \\ **Regression** \\ **\%** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(SVM-RBF)** \\ **\%** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBSVM)** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBLINEAR)** \\ \end{tabular}  & \begin{tabular}{c} **Logistic** \\ **Regression** \\ **\%** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBSVM)** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBLINEAR)** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **Regression** \\ **\%** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBLINEAR)** \\ \end{tabular}  & \begin{tabular}{c} **Logistic** \\ **Regression** \\ **\%** \\ \end{tabular}  & \ \begin{tabular}{c} **Accuracy** \\ **(LIBLINEAR)** \\ \end{tabular}  & \begin{tabular}{c} **Accuracy** \\ **(LIBLINEAR)** \\ \end{tabular}  \\ \hline Lap5 N & 99.8 & 94.7 & 44.2 & 62.3 & 100 & 99.7 & 78.6 & 82.5 \\ Lap4 N & 98.7 & 93.5 & 63.1 & 66.5 & 99.7 & 97.3 & 84.3 & 86.6 \\ Lap3 N & 97.5 & 89.5 & 61.5 & 65.1 & 99.8 & 93.7 & 87.6 & 85.1 \\ Lap2 N & 99.1 & 92.7 & 57.8
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 5 Offline Experiments
### Discussion of Offline Experiments
#### 5.4.3 Our Hazard Function-Based Framework vs. Classification/Regression Framework  
Our framework achieves much higher prediction performance for the ad discontinuation prediction problem by introducing the hazard function instead of the conventional classification/regression framework. We assume that there are two reasons due to the property of the discontinuation problem: (1) the data imbalance and (2) the time dependence of the discontinuation. Regarding the data imbalance, most of the ad creatives were discontinued early. If the classification/regression framework is trained on such data, it may output only biased predictions. Regarding the time dependence of the discontinuation, the probability of the ad creative discontinuation increases as time goes on. The classification/regression framework does not have the assumption of time dependency, while the hazard function-based framework is known to have the assumption.  
We consider that the hazard function appropriately captures the property of the two reasons derived from the ad creative discontinuation problem. In contrast, the classification/regression framework does not seem to sufficiently handle the problems of the data imbalance and the time dependence. In particular, the classification- and regression-based long-term models may output only discontinuation, which may be why the F1 score is low. Furthermore, as ad creatives tend to be discontinued due to their decreasing effectiveness of serving over time, our framework, which can consider the discontinuation probability at the previous time interval, is considered to function effectively.
# An Improved SIFT Underwater Image Stitching Method
## 1 Introduction
We employ a weighted smoothing algorithm to achieve the stitching of the target image. The weights assigned to the two images intended for merging undergo a gradual transition from 0 to 1 in either the horizontal or vertical direction, thereby achieving a seamless transition in the fused image. In our experiments, we present compelling evidence demonstrating that our proposed method consistently yields excellent results, attesting to its effectiveness in the domain of underwater image stitching. The primary contribution of this paper is the development of an enhanced SIFT underwater image stitching method strategically designed to minimize the ghosting artifacts commonly associated with stitching. Our approach excels even in areas characterized by blurred texture features, where traditional feature extraction methods struggle. By significantly increasing the number of accurate matching pairs, we enhance the matching accuracy, improve the alignment of feature points between images, reduce stitching errors, and ultimately elevate the quality of underwater panoramic images. In a series of ten un  
Figure 1: Various stages in the improved SIFT underwater image stitching technique. derwater image stitching experiments, our proposed method outperformed the traditional SIFT algorithm, increasing the non-reference underwater image quality measure (UIQM) by an average of 6.04%. Furthermore, when compared to references [11; 12; 13], our method enhances the UIQM index by 4.73%, 9.01%, and 11.06%, respectively. The remainder of this paper is structured as follows: We describe the related works in Section 2. In Section 3, we describe our proposed improved SIFT underwater image stitching method. Section 4 describes the experiments. Finally, we present our conclusions in Section 5.
# AOA-Based Three-Dimensional Positioning and Tracking Using the Factor Graph Technique
## 2 System Model
Note that \(\tilde{g}\left(\boldsymbol{\phi}_{k},\theta_{k}\right)\) is not theoretically formulated in this paper, but realized by a practical 3D location detector. Within the proposed FG structure, all messages passed through are approximated to be Gaussian, including the noise of observation. Therefore, it is assumed that each element from \(\mathbf{e}_{k}\) follows \(\mathcal{N}\left(0,\sigma_{e}^{2}\right)\). Unfortunately, \(\sigma_{e}^{2}\), which is needed to perform EKF, cannot be directly measured at the sensors. The theoretical bound of the observation result is determined by the CRB. In other words, the smallest achievable \(\sigma_{e}^{2}\) can be calculated based on the variances of AOA measurements.
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
###### Abstract  
With the development of ship intelligence, the frequency of upgrading the marine engine room simulator, which is essential for crew training, has increased. Traditionally, the data processing unit (DPU) of the marine engine room simulator is upgraded by manually downloading the firmware. This makes the hardware maintenance high-cost. In this paper, we first propose a WAN-based firmware upgrade system to enable secure over-the-air upgrades of DPUs and reduce operation and maintenance costs. A distributed hardware structure is given to manage DPU in the simulator via the Internet. We have designed two methods of firmware upgrades, automatic upgrades and remote upgrades. In automatic upgrades, the DPU can download new firmware upgrades from the web server through the router. By designing a series of mechanisms including code rollback, code backup and code confirmation, the In-Application Programming (IAP) technique is realized through the Internet. Firmware upgrades have good fault tolerance mechanisms to ensure that the emulator can still work in the event of an upgrade error. In remote upgrades, we upgrade the DPU firmware through the remote control center. We assessed the performance of the system by measuring the success rate of DPU upgrades, upgrade time and performance after the upgrade. The results show that the DPU upgrade success rate is close to 100% and performance is as good as expected. The results show that the remote firmware upgrade system proposed in this paper is reliable and practical.  
simulator; DPU; firmware upgrade; code backup; over-the-air +
Footnote †: journal: Applied
## 2 Related Work  
### V2V-Based Computation Offloading  
The authors in [17] analyzed the efficiency of a computation offloading algorithm in the vehicular cloud and improved the performance by using the computing resources of idle vehicles. To reduce the completion delay of the task and to improve the utilization ratios of vehicle resources, a two-stage cooperative offloading scheme was proposed in [18]. In the first stage, a cluster is formed among multiple vehicles and the selected cluster head is used for resource allocation. In the second stage, the fuzzy logic principle is exploited to screen cluster heads and SVs, to optimize the response time and completion delay. The authors in [19] proposed a cluster-based cooperative offloading algorithm, which applies the fuzzy logic principle to determine the appropriate cluster heads. Moreover, a stable matching method is designed to optimize energy consumption and task latency. The authors in [20] proposed fully exploring the idle resources of vehicles by utilizing the gathering period of vehicles in an urban environment. The task execution time is optimized with a Max-Min fairness scheme which is solved by the particle swarm optimization algorithm. The authors in [21] proposed a multi-hop V2V collaborative task offloading scheme, which makes full use of the multi-hop vehicular ad hoc networks to support computation offloading. The reliability model of multi-hop routing is built and the optimal offloading strategy with the lowest relaying and computing cost is executed by the binary search algorithm.  
Although these works have provided some effective methods to solve the problem of optimizing the completion delay and resource utilization rate in V2V-based computation offloading, they all work under the assumption that vehicles are selfless and volunteer to provide cooperation, which is not practical and thus limiting the applications of these works.
# Green IoT: A Review and Future Research Directions
## 2 Methodology  
To write this literature review on energy-efficient and eco-sustainable strategies for future massive green IoT networks, a systematic process was used to gather information. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method was used to formulate the research questions (RQ) that guided the study.  
* _RQ1:_ What are the energy-efficient techniques that can be used to implement massive green IoT networks? This question aims to identify the various energy-efficient techniques mentioned in the literature, evaluate their advantages, and examine their trends to aid the implementation of massive green IoT networks.
* _RQ2:_ How can energy consumption be reduced in IoT devices without compromising performance and functionality? The objective of this question is to explore the integration of various software and hardware techniques and approaches to minimize energy wastage caused by inefficient resource utilization.
* _RQ3:_ What are the key challenges in designing and implementing green IoT networks? This question aims to investigate the symmetry between energy efficiency and key performance indicators, such as coverage and signal strength, in IoT networks. After identifying the research questions, the following keywords were established to create the research string (RS): \"Green IoT\", \"Energy efficiency\", \"Green communications\", \"Energy harvesting\", and \"Low-power design\". The research string (RS) is indicated as: RS = (Green communications OR Green IoT) AND (GIoT OR Energy harvesting OR Energy efficiency OR IoT) AND (Low-power design OR low-power devices) After constructing the RS, we started with the document search phase. The databases that were consulted for this study included Science Direct, IEEE, Scopus, Springer, Web of Science, Wiley Online Library, Association of Computing Machinery (ACM) and MDPI. These databases were chosen due to their wide range of publications and previous research. An effort was made to include all databases that have been used by authors in prior studies. Notably, no additional techniques were applied to expand the results, as the number of articles obtained from the aforementioned databases was sufficient. We implemented a three-stage process, as demonstrated in Figure 2.  
Figure 2: Research methodology of the article.
# On the Tortuosity-Connectivity of Cement-Based Porous Materials
## 5 Determining the Tortuosity-Connectivity Parameter  
### Darcy versus Hagen-Poiseuille Flows  
The tortuosity-connectivity parameter can be determined by comparing the Darcy flux with the Hagen-Poiseuille fluxes in a cylindrical pore. The following analysis, it should be noted, is valid only for homogeneous materials. Consider a homogeneous material, partially saturated with pore water which is assumed to be continuously and uniformly distributed. Then the equivalent pore related to the porous network which contains pore water can be chosen to be a cylindrical pore of constant section. In that case, the Darcy and Hagen-Poiseuille fluxes must coincide, which leads to the expression of the tortuosity-connectivity as a function of the permeability of the porous medium.  
Based on the obtained results, the Darcy flux in the equivalent pore can be expressed as:  
\[\langle J_{Darcy}^{i}\rangle_{\hat{\Omega}}=-\frac{\hat{k}_{l}}{v_{l}}\
abla \langle p_{l}\rangle_{\hat{\Omega}} \tag{62}\]  
where \(\hat{k}_{l}=\tau k_{l}\) is now a macroscopic function which describes the permeability of the complex and tortuous porous network.  
The Hagen-Poiseuille flux in the equivalent pore reads:  
\[\langle J_{H-P}^{i}\rangle_{\hat{\Omega}}=-\tau\frac{r^{2}}{8v_{l}}\
abla \langle p_{l}\rangle_{\hat{\Omega}} \tag{63}\]  
where \(r\) is the radius of the pore.  
Setting Equations (62) and (63) equal, the tortuosity-connectivity parameter can be written as a function of the permeability function:  
\[\tau(r)=\frac{8\hat{k}_{l}}{r^{2}} \tag{64}\]  
The tortuosity-connectivity for a fully saturated homogeneous material can be calculated from Equation (64) with \(\tau=R\), where \(R\) is the radius of the saturated equivalent pore associated to a fully saturated REV:  
\[\tau(R)=\frac{8\hat{k}_{l}}{R^{2}} \tag{65}\]  
The radii \(r\) and \(R\) are related according to the following expression:  
\[r^{2}=R^{2}\frac{\phi_{l}}{\phi} \tag{66}\]  
where \(\phi\) is the porosity of the REV.  
The tortuosity parameter can now be expressed as a function of the pore water content:  
\[\tau(\phi_{l})=\frac{8\hat{k}_{l}}{R^{2}}\frac{\phi}{\phi_{l}} \tag{67}\]The permeability is often expressed as the product of an intrinsic permeability coefficient \(\hat{K}\) (m\({}^{2}\)) and a permeability function relative to the \(l\)-phase \(\hat{k}_{rl}\), which takes values between 0 and 1. Equation (67) can then finally be rewritten as:  
\[\tau(\phi_{l})=\tau(\phi)\frac{\phi}{\phi_{l}}\hat{k}_{rl}(\phi_{l}) \tag{68}\]