esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 2 Related Work
### Algorithm Complexity Analysis  
The complexity of Algorithm \(1\) (DVA-TPCEA) is analyzed based on its individual components and their respective contributions to the overall time complexity. The key variables influencing the complexity include the population size \(n\), the number of decision variables \(D\), the number of objectives \(M\), the number of weight vectors \(|W|\), and the number of algorithm iterations \(T\).  
The time complexity of the decision variable quantitative analysis method is \(O(D\cdot n)\). The complexity required to measure the contribution of decision variables to the objectives is \(O(D\cdot|W|\cdot n\cdot M)\). Subsequently, grouping decision variables according to their contribution targets incurs a time complexity of \(O(D\cdot|W|)\). The main steps of the algorithm's loop include the reproduction of the population and the environmental selection process. Specifically, the time complexity of the reproduction process for both populations is \(O(M\cdot|W|\cdot n)\). The time complexity of the environmental selection process is \(O(M\cdot n)\), and the environmental selection process based on the metrics also incurs a complexity of \(O(M\cdot n)\). Therefore, the overall time complexity of the main loop is \(O(M\cdot|W|\cdot n\cdot T)\). Based on the above analysis, the complexity of the DVA-TPCEA algorithm is \(O(M\cdot|W|\cdot n\cdot max(T,D))\).
## 2 Materials and Methods
### Methodology
#### 2.2.1 Data Preparation  
The 500 m and 8-days PML-V2 data from the Google Earth Engine (GEE) platform ([[https://eartheengine.google.org/](https://eartheengine.google.org/)]([https://eartheengine.google.org/](https://eartheengine.google.org/)) (accessed on 23 August 2022)) were used to obtain the monthly gross primary productivity and vegetation evapotranspiration index [36]. The PML-V2 data is based on the Penman-Monteith-Leuning (PML) model, with MODIS and Global Land Data Assistance System (GLDAS) metrological forcing data as input data. The 8-day data of 95 flux towers were used to verify the PML-V2 data. The results show that the Root Mean Square Errors of terrestrial evapotranspiration (ET) and GPP are 0.69 mm d\({}^{-1}\) and 1.99 g Cm\({}^{-2}\)d\({}^{-1}\), respectively, and the Bias are \(-1.8\%\) and \(4.2\%\), respectively [36]. The PML-V2 data contains 5 bands in total, namely gross primary productivity (GPP), vegetation transpiration (Ec), soil evaporation (Es), vegetation canopy interception evaporation (Ei), and ice, snow, water evaporation (ET_water), where \(ET=E_{C}+E_{t}+E_{s}\)[36], among the three components, Ec is coupled with GPP, forming the two dominant processes in global water and carbon cycles. In this study, we chose bands GPP and Ec.
# Computational Study on Rogue Wave and Its Application to a Floating Body
## 1 Introduction  
A rogue wave, also called a freak wave, refers to a wave more than twice the surrounding significant wave height. The physical mechanism of the rogue wave can be explained by wave-current interaction, wave-wind interaction, geometrical seabed variation, and spatial-temporal focusing. The great wave height and unpredictability of a rogue wave mean that it can cause significant damage to ships and offshore platforms. Previously, a rogue wave was measured at the Draupner platform in the North Sea [1]. Its significant wave height was 11.9 m, the measured crest height was 18.5 m, and the measured trough height was \(-\)7.5 m. The wave height of the rogue wave was 26 m, and it was asymmetric about the still water level. The asymmetry rogue wave's crest height was extreme and unpredictable [1].  
[PERSON] et al. [2] generated rogue waves in a water wave tank and compared the measured results with a Peregrine-type breather solution [3] of the nonlinear Schrodinger equation (NLS) [4, 5]. The wave heights from crest to trough measured by the experiment and breather solution were almost 2.5 times larger than the average wave height around the perturbation. [PERSON] et al. [2] observed super rogue waves with the high-order breather solution of the nonlinear wave equation and experiments in a water wave tank. [PERSON] et al. [6] studied rogue waves in deep water using phase-resolved numerical simulations based on a high-order spectral (HOS) method. [PERSON] and [PERSON] [7] simulated the fully non-linear dynamics of a large breaking rogue wave on a tension leg platform using smoothed particle hydrodynamics (SPH). [PERSON] and [PERSON] [8] studied a rogue wave's impact on various mooring systems of an offshore platform using SPH. [PERSON] et al. [9] simulated a rogue wave through a Peregrine-type breather solution [3] and developed a fully coupled fluid-structure interaction (FSI) model to study a rogue wave overtopping.  
[PERSON] et al. [10] simulated a focusing wave by varying the wave maker velocity using computational fluid dynamics (CFD). [PERSON] et al. [11] simulated a breaking rogue wave using the CFD-HOS coupling method and assessed the three-dimensional velocity field underneath a breaking wave. [PERSON] et al. [12] studied the behavior of a wave energy converter system under a rogue wave loading using CFD and experiment, and [PERSON] et al. [13] studied a breaking rogue wave impact on a ship using CFD. [PERSON] et al. [14] simulated oblique focused waves and interaction with a fixed body and compared them with experiments.  
The objectives of the present study were (1) to generate rogue waves using a developed wave directional focusing library and (2) to apply it to a floating object. Open-source CFD libraries, termed OpenFOAM, were used to simulate the rogue waves. To capture free-surface wave flows, interFoam, which is a basic application solver in OpenFOAM, was selected.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Comparative Analysis of Queueing Managements  
In WSNs, data transfer from the source node to the destination is carried out through several intermediate nodes, so each node may have multiple data packets stored in it. However, depending on the application requirement, it can be categorized into several types of packets (e.g., real-time or non-real-time transmissions). [PERSON] (2018) proposes a data scheduling technique based on latency and loss constraint, where the input data are prioritized with latency and loss constraint conditions, and data with high priority are scheduled based on data priority, which guarantees that high-priority data are delivered instantly. The increasing number of real-time applications makes the requirements for network traffic and bandwidth management critical. [PERSON] (2018) describes a fair queuing with the scheduling of Weighted Fair Queuing (WFQ) and Low Latency Queueing (LLQ), which divides the available bandwidth across queues of traffic based on weights to ensure that the important traffic receives a higher priority than the less important traffic.  
To evaluate the performance of queueing management, several parameters (e.g., the amount of traffic, type of traffic, services with mixed traffic, and the number of queues on the router) are analyzed for the following queuing mechanisms ([PERSON] et al., 2018): First-In First-Out (FIFO), Custom Queuing (CQ), Priority Queuing (PQ), Weighted Fair Queuing (WFQ), Class Based Weighted Fair Queuing (CBWFQ), and Low Latency Queuing (LLQ). The study of ([PERSON] et al., 2018) investigates the following scheduling algorithms: Round Robin (RR), Strict Priority (SP), Weighted Fair (WF), and Weighted Round Robin (WRR). Note that each queuing algorithm has its advantages when handling certain types of traffic. Network performance metrics (i.e., throughput and endpoint latency) and queuing metrics (i.e., peak queue size, average queue length, average queue time, and total packet loss) can be applied to evaluate the performance of scheduling algorithms.  
Moreover, given the growth and demand for smart networks and intelligent devices, Quality of Service (QoS) is considered as an important parameter to support data quality in IoT. Three queuing algorithms such as FIFO, PQ, and WFQ are analyzed and compared by using a QoS mechanism to control and observe the congestion in the IoT network ([PERSON] et al., 2017). A scalable load business flow model is performed and analyzed in ([PERSON] et al., 2018). The algorithm can be used to flexibly adjust the queue weights according to the real-time metrics monitored by the network to ensure the quality of service of various disaster recovery services.
# Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap smear Images
###### Abstract  
Cervical cancer can be prevented by having regular screenings to find any precancers and treat them. The Pap test looks for any abnormal or precancerous changes in the cells on the cervix. However, the manual screening of Pap smear in the microscope is subjective with poorly reproducible criteria. Therefore, the aim of this study was to develop a computer-assisted screening system for cervical cancer using digital image processing of Pap smear images. The analysis of Pap smear image is important in the cervical cancer screening system. There were four basic steps in our cervical cancer screening system. In cell segmentation, nuclei were detected using a shape-based iterative method, and the overlapping cytoplasm was separated using a marker-control watershed approach. In the features extraction step, three important features were extracted from the regions of segmented nuclei and cytoplasm. RF (random forest) algorithm was used as a feature selection method. In the classification stage, bagging ensemble classifier, which combined the results of five classifiers--LD (linear discriminant), SVM (support vector machine), KNN (k-nearest neighbor), boosted trees, and bagged trees--was applied. SIPaKMeD and Herlev datasets were used to prove the effectiveness of our proposed system. According to the experimental results, 98.27% accuracy in two-class classification and 94.09% accuracy in five-class classification was achieved using the SIPaKMeD dataset. When the results were compared with five classifiers, our proposed method was significantly better in two-class and five-class problems.  
cervical cancer; Pap smear; watershed transform; random forest; ensemble classifier Article
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 2 Materials and Methods
### Parameter Optimization  
We used the Pytorch framework in Ubuntu to create the model and accelerated using GPU. The Adam algorithm was employed for optimization during model training, and the maximum number of model iterations was set to 50. Due to the massive size of the training set, the computational performance of the model would have been reduced if sub-datasets were traversed directly during each iteration. Thus, the mini-batch algorithm was introduced into the experiment. It undertakes calculations while making adjustments by using small samples for calculation and by making several gradient descents in a traversal. The calculation efficiency is thus increased while the time complexity is decreased. Typically, a power of 2 between 64 and 512 is chosen as the mini-batch size. Mini-batch sizes of 128 were used in the experiment because they struck a balance between speed and precision. Furthermore, a GCN was used to learn the graph structure globally, which is necessary when learning the hierarchical features of a secondary structure. The dropout approach was implemented during training to prevent local optimization of model parameters. By randomly discarding some neurons, the random correction of the extracted features and the avoidance of overfitting were achieved. We used the AUC as the model evaluation metric. The AUC evaluates the classification ability of a model; here, the ability of HPNet to correctly predict the presence of binding sites in RNA. In general, larger AUC values are better.
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 4 Experiments
### Implementation Details  
Training Details: Our method is implemented in the open-source framework PyTorch (v2.5.1), employing a convolutional backbone network analogous to that of RAFT, which outputs feature dimensions of 128. We stack six Transformer blocks. Among these, the final upsampling layer utilizes a convex upsampling technique, while the preceding upsampling layers employ bilinear interpolation for original image enhancement. We train our model on an NVIDIA A100 GPU 80 GB with a batch size of 12. For training, the model is initialized from scratch with random weights. Following previous methods, we first train our model on the baseline and validate its performance on a test set with the same distribution. We employ the AdamW optimizer with a weight decay of 0.00005 and use the PyTorch OneCycle learning rate scheduler, setting the maximum learning rate to 4 \(\times\) 10\({}^{-4}\) and training for 500,000 steps.  
Assessment Metrics. For quantitative evaluation, we adopt the commonly used metric in PIV estimation, i.e., average endpoint error (AEE), which is defined as the average \(\ell_{2}\) distance between the final predicted and ground-truth optical flow over all pixels,  
\[AEE=\left\|V_{es,N}-V_{gt}\right\|_{2} \tag{9}\]
## 4 Case Study and Results
### Line Loading  
Figure 11 shows the deviations of the power flow through the line with the highest power loading, obtained with P2P and MVDC configurations. Deviations were calculated with respect to the power flow of the base case: \(\triangle\)line loading (%) = selected case loading (%) \(-\) base case loading (%). Results show that P2P configuration presented a much higher line power loading than the base case, in order to maximise the DG usage as well as demand shift. On the other hand, MVDC configuration not only presented lower DG curtailment and demand shift (as shown in previous subsection), but it also maintained the low line power loading during high demand (scenarios 1-16) and further decreased the loading during high DG output (scenarios 17-26). This was due to the fact that the MVDC interface allowed a power exchange between multiple feeders.  
Figure 10: The distribution of total DG curtailment within the study period.  
Figure 9: The comparison of total DG curtailment at different system configurations.
# Measurement and Analysis of Downhole Drill String Vibration Signal
###### Abstract  
Downhole drill string vibration data can provide an effective reference for research drill string vibration during drilling. In this paper, the research group used a storage-type downhole vibration measurement equipment equipped with an orthogonal, three-axis accelerometer to measure and collect drill string vibration signals during drilling in an oil well. Based on the installation characteristics of the sensor, the relationship between the acceleration measurement value of the sensor and the center acceleration value of the drill string is obtained. Then the time-domain signals representative of the vibration in igneous rock drilling is analyzed. It can be found that the occurrence of stick-slip vibration can be judged by the regular wave packets in the time-domain signal, while the time-domain signal of whirl is disorderly. The main frequency of stick-slip vibration in the low-frequency band is 0.1221 Hz and the period of stick-slip vibration is very close to 10 s through Fast Fourier (FFT) and Short-time Fourier transform (DTFT) methods. In the process of whirling, two frequencies, respectively, 0.05341 Hz and 155.5 Hz, play a major role. The frequency 0.05341 Hz is very close to the reciprocal of the period of 20 s when the peak energy spectrum density appears, indicating that the occurrence of whirl is very likely to be related to the natural frequency of the drilling tool. Through further time-frequency analysis, it also can be found that the occurrence of whirl and stick-slip is greatly related to the use of torsional impactors and jars.  
vibration measurement; igneous rock; stick-slip; whirl; frequency analysis +
Footnote †: journal: Applied Sciences
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Impact of Different Number of Nodes on Transmission Performance  
Figures 8 and 9, respectively, illustrate the impact of the number of communication nodes on the delivery ratio and average delivery delay under varying proportions of selfish nodes. The results demonstrate that the delivery ratio is positively correlated with the number of nodes. As the number of nodes increases, the delivery ratio rises, while the average delivery delay decreases. With a cooperation threshold of 0.2, the delivery ratio is lower, and the average delivery delay is larger. This occurs because nodes with lower cooperation levels are selected for relay during the initial flooding process, hindering rapid delivery to the destination node and causing significant packet loss.  
Figure 8: The impact of the number of nodes on the delivery ratio.  
Figure 7: Impact of cooperation thresholds on the average delivery delay.  
In contrast, with a cooperation threshold of 0.8, the delivery ratio is higher, as nodes with higher cooperation levels are chosen for each relay transmission. However, due to the opportunistic transmission mechanism in integrated UAV-ICV networks, the type of communication nodes encountered is highly uncertain. Under this condition, using a higher cooperation threshold necessitates waiting for nodes with higher cooperation levels for relay transmission, thereby increasing the average delivery delay. Therefore, it is essential to elaborately set a reasonable cooperation threshold to enhance transmission performance.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 3 Lightning Analysis of PR Data  
Studies on lightning accompanied by rainfall are important for the analysis of microphysical processes and precipitation mechanisms. Some researchers investigated characteristics of the lightning activity using PR data of rain type, rainfall rate, and vertical reflectivity profile (Table 6).
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 4 Review
### Resource Allocation
Furthermore, cognitive radio techniques can \begin{table}
\begin{tabular}{l l} \hline \hline
**Definition** & **Value** \\ \hline Transmitter antenna diameter & 3.5 m \\ Receiver antenna diameter & 1 m \\ Satellite altitude & 1000 km \\ Atmospheric height & 100 km \\ Antenna efficiency & 0.9 \\ Frequency & 28.5 GHz \\ Bandwidth & 2.1 GHz \\ Receiver noise temperature & 500 K \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters for a LEO satellite for the example shown in Figure 2.  
enable intelligent resource management by dynamically sensing the network environment and adapting resource allocation to optimize performance and security. In addition to enhancing network performance, effective resource allocation can also contribute to reducing the environmental impact of ISTNs. By optimizing power consumption and minimizing the reliance on energy-intensive components, network operators can reduce their carbon footprint and promote sustainable operations. Furthermore, resource sharing and power control mechanisms can help conserve resources and reduce overall energy consumption. By implementing these strategies, communication networks can achieve a delicate balance between security and power efficiency in ISTNs. This is an active area of research, and future advancements in resource allocation algorithms are expected to play a key role in optimizing ISTN performance. While ITSNs hold promise for seamless global connectivity, achieving efficient resource allocation in these networks presents several challenges. The heterogeneous nature of satellite and terrestrial networks, with diverse propagation environments and link capacities, complicates the optimization process. Dynamic changes in network topologies and traffic patterns further increase the complexity. Additionally, imperfect channel state information and interference between different network components can degrade performance. To address these challenges, innovative approaches are necessary. Advanced optimization techniques, such as convex optimization and dynamic programming, can help find optimal solutions. Intelligent resource allocation algorithms that can adapt to dynamic network conditions are essential. Effective interference management techniques, including frequency planning and power control, can mitigate interference and improve performance. Accurate channel estimation is crucial for making informed resource allocation decisions. Collaborative resource allocation between satellite and terrestrial networks can further enhance efficiency and reliability. By overcoming these challenges and leveraging advanced techniques, ISTNs can deliver high-quality, reliable, and ubiquitous connectivity. This will enable a wide range of applications, from remote sensing and disaster response to global telecommunications and IoT services.
## 3 Methodology  
### Image Segmentation  
Image segmentation, a critical problem in computer vision, is frequently viewed as a subset of the picture classification task. Image segmentation, as opposed to image classification, aims to categorize pixel points inside an image, i.e., segmenting an image into areas based on the class to which the pixels in the image belong. Traditional image segmentation mainly includes pixel-based thresholding, and region-based, edge-based, and clustering-based methods [19]. These traditional image segmentation methods all have certain limitations in terms of segmentation effectiveness. For example, pixel-based thresholding methods are usually sensitive to lighting changes and noise in the image; region-based segmentation methods are more sensitive to texture information in the image; edge-based methods are sensitive to noise information in the image; and clustering-based methods are sensitive to grey-scale information in the image. In recent years, with the further development of computer vision, image segmentation methods based on deep learning have achieved better results. At present, the mainstream traditional image segmentation methods are usually used in image pre-processing or post-processing work due to the limited use of scenarios. Deep neural networks in the field of image segmentation have achieved many effective results so far. For example, the best-known network model among the early deep neural network-based image segmentation methods is the Fully Convolutional Network (FCN) [20], which segments an image into multiple pixel regions via a fully convolutional network. The FCN had certain shortcomings at the outset of its design, so a series of subsequent improvements emerged, such as the UNet [21], DeepLab series [22; 23; 24], etc. The above are all based on the traditional convolutional neural network architecture. With the introduction of the Transformer-based Visual Transformer (ViT) [25] architecture, many other Transformer-based architectures have also emerged in the field of image segmentation in recent years, such as Segmentation Transformer (SETR) [26] and the SegFormer [27] referenced in this paper.