esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved SPEA2 Algorithm with Local Search for Multi-Objective Investment Decision-Making
## 4 Conclusions  
How to determine the investment plan scientifically and create the greatest value with limited funds has attracted more and more attention. The traditional way of investing is based on single objective decision-making, that is, profit maximization. However, due to the influence of many factors, profit-based optimization often fails to measure the whole scheme comprehensively. This paper establishes a multi-objective evaluation index system based on profit, project risk and environmental cost, and makes multi-objective optimization decision for enterprise investment projects. Facing the high complexity of investment decision-making space, traditional multi-objective optimization methods pay too much attention to global search ability because of pursuing convergence speed and avoiding falling into local optimum, while local search ability is insufficient, which makes it difficult to converge to Pareto optimal boundary. To solve this problem, this paper uses an improved SPEA2 algorithm to optimize the multi-objective decision-making of enterprise investment. In this method, an external archive set is set up separately for local search after genetic operation, which guarantees the global search ability and also has strong local search ability. At the same time, the crossover operator and individual update strategy are improved to further improve the convergence ability of the algorithm while maintaining a strong diversity of the population. For the investment optimization problem discussed in this work, we need to know the unit profit, investment risk and investment environment consumption under different investment conditions. The optimal result obtained in this paper is the annual investment amount for different investment products. Of course, there may be other constraints on investment optimization. This work presents a general methodology to solve these investment problems. The experimental results show that the improved method has faster convergence speed than the traditional NSGA2 and SPEA2 algorithms, and the optimization results are closer to the Pareto optimal boundary. Through the optimization of investment by this method, a series of alternative Preto optimal investment schemes can be provided for decision makers, which can optimize environmental costs and profits within a certain risk tolerance range. However, the algorithm can continue to optimize in the following aspect: the speed of this algorithm for a large-scale investment portfolio optimization problem is not ideal, how to further optimize the genetic and mutation operations to improve the search speed and search efficiency is worth further study in the future.  
[PERSON] performed the experiments and wrote the manuscript, [PERSON] carried on data analysis and visualization of results.  
This research was funded by the Doctoral Innovation Fund of Donghua University (71371046).  
The authors declare that they have no conflicts of interest.  
Figure 7: Mean convergence comparison of 300 experiments.
# An Energy Balance Clustering Routing Protocol for Intra-Body Wireless Nanosensor Networks
## 2 System Model
### Network Model  
In the EBCR protocol, the network topology of iWNSNs can be represented as \(G=(V,E)\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) represents a set of all nano-nodes and nano control nodes, and \(n=|V|\) is the number of nodes. The proposed routing framework (shown in Figure 1) is a multi-layer topology based on the distance between nano-nodes and nano control nodes. In order to improve the stability of the network and achieve high energy efficiency, each layer is divided into multiple clusters to avoid repeated clustering. The cluster head node (Cluster Header, CH) of each layer is selected based on the residual energy of the nano-nodes. The cluster head collects data from the cluster member node (CM) and then forwards the summary data to the NC. The clusters of each layer can communicate with adjacent upper or lower clusters.  
The network model has the following assumptions:  
* The positions of the nano-nodes and the nano control nodes are fixed, and the energy of NC is not limited.
* The terahertz communication of the physical layer of iWNSNs uses TS-OOK modulation, which is like the impulse radio ultra-wideband (IR-UWB) technology, which can accurately locate, and the nano-nodes can obtain the location information of other nodes by broadcasting a simple message.
* In the process of perceiving, processing and transmitting data, the energy consumption of nano-nodes is mainly concentrated in data transmission. The energy consumed by the nano-nodes due to the perceiving and processing data is ignored in the energy consumption model.
## 4 Results and Discussion
### GMM Clustering Based on Travel Time
#### 4.1.2 Analysis of Travel-Time Cluster  
As shown in Figure 6, the clustering results distinguished the distribution of intercity ridership on travel time greatly and effectively identified travel groups under different travel patterns. Among them, the short-travel-time group accounted for 20.1% and the long-travel-time group accounted for 79.9%. It can be seen that in intercity trips, the ridership of the long-travel-time group is the main group.  
Travel time can reflect the daily travel distance of intercity ridership and the scope of intercity activities somehow. In this regard, the differences in travel distance between different intercity ridership groups are analyzed, as shown in Figure 7. It is important to note that travel distance in this paper is defined as the distance in rail travel distance between the boarding and alighting stations. The average travel distance for the entire intercity ridership is calculated to be 32.2 km. Specifically, the short-travel-time group has an average travel distance of 18.7 km, while the long-travel-time group averages 35.6 km.  
To further investigate the differences in travel patterns between intercity ridership groups, the short-travel-time group and the long-travel-time group were compared based  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Weight** & **Mean** & **Standard Deviation** \\ \hline Gaussian distribution 1 & 0.1854 & 32.90 & 6.83 \\ Gaussian distribution 2 & 0.8146 & 68.93 & 15.59 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The estimated parameters of each travel-time clustering component.  
Figure 6: The clusters of travel-time ridership.  
Figure 7: Distance distributions of different travel-time groups.  
on travel purposes. Additionally, the top 10 destination stations with the highest proportions of intercity ridership were identified, as shown in Figures 8 and 9. For the short-travel-time group, the primary destinations are Yanghu Weltland (34.9%), Yangguang (15.5%), and Yanghu ECO Town (12.4%). Yanghu Weltland and Yanghu ECO Town form a comprehensive area combining leisure and residential features, which is the closest well-developed community in Changsha to Xiangtan. And Yangguang Station is surrounded by numerous residential communities. This indicates that the main travel purpose for the short-travel-time group is driven by residential demand. For the long-travel-time group, the primary destinations are Wuyi Square (22.6%), Huangxing Square (12.0%), and the Railway Station (11.8%). Wuyi Square and Huangxing Square are well-known leisure hubs in Changsha, China, while the Railway Station serves as a major transportation hub. This suggests that the main travel purpose for the long-travel-time group is leisure.  
Figure 8: Travel destinations of the short-travel-time group.  
Figure 9: Travel destinations of the long-travel-time group.
## 5 Opportunities and Challenges
### Challenges  
(1) Cross-domain and cross-vendor interoperability: The high complexity of the AI-driven 6G framework depends on seamless coordination among multiple vendors across various network domains and IT areas, requiring standardized and open APIs. However, the gap between the networking and AI industries makes standardization difficult. Moreover, the integration of AI models across multiple domains (such as the core, RAN, and terminal) from various vendors complicates data and model coordination. These interoperability issues can lead to concerns around data privacy and security, as sensitive information may be exchanged between domains with different security protocols, increasing the risk of unauthorized access.  
(2) E2E AI orchestration and management: The AI capabilities of a network are highly complex, with differing characteristics across data, models, and computing resources in various domains, making unified management extremely challenging. Furthermore, exposing network or cloud capabilities to third parties requires great caution and a well-defined architecture to ensure security and accountability. Additionally, the orchestration of AI components must be integrated with both cloud and network management systems. The key issue here which requires further research and discussion is whether a single management system should oversee both network and cloud-based AI or whether separate orchestration and management systems are needed.  
(3) AI trustworthiness and explainability: In the 6G system, the AI-driven solutions need to be transparent and secure to avoid security risks in resource allocation and network optimization. Moreover, explainable AI techniques are crucial for making AI decisions more interpretable and thus enhance the reliability of 6G networks.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Medical Imaging Techniques for BC Diagnosis
#### 2.1.4 Histopathology  
Histopathological evaluation is a microscopic examination of the organ's tissue [8]. It is considered the gold standard for BC assessment as it provides the phenotypic information which is crucial for BC diagnosis and treatment. At the same time, there are constraints regarding histopathology images for breast cancerous cells in terms of high coherence, high intra-class and low inter-class differences.
# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 2 Materials and Methods
### Clustering/Cluster Analysis  
Cluster analysis, often known as clustering, is a machine learning technique that involves grouping data points with similar characteristics into clusters. Clustering aims to divide a dataset into distinct groups, where the data points within each group, or cluster, exhibit greater similarity to one another compared to those in different groups. Clustering is an unsupervised learning method where the algorithm identifies patterns and structures in the data without receiving explicit instructions on grouping the data.  
This study thoroughly examines the K-means ++, DBSCAN, Birch, and agglomerative clustering methods and the effect of genetic optimization on them.
## 4 Relay Based QoS-Aware Routing Protocol for I2O WBAN
### QoS Metric Modeling  
#### 4.3.1 Network Lifetime  
The stability period and total network lifetime are defined as the lifespan of the network until the first in-body sensor node is energy depleted, including the time duration of the network, until all in-body sensor nodes are energy depleted, respectively [36]. Assuming the number of in-body sensor nodes is \(N\) and each node is initially equipped with energy \(E_{0}\). The main object of I2O WBAN is to maximize the network lifetime \(T\), which can be formulated via linear programing as [21]:  
\[\textit{Objective:Max}\ T=\sum_{r}t_{r} \tag{16}\]  
where \(r\) and \(t_{r}\) denotes the current round and summation of rounds before all in-body sensor nodes energy deplete, respectively. The energy consumption per bit consists of sensing, processing and transmitting energy for an in-body sensor node represented by \(E_{sen}\), \(E_{pro}\) and \(E_{trans}\)[21]. The remaining energy of the I2O WBAN network after each round can be defined as network residual energy \(E_{i}\). In terms of constraints, the I2O WBAN is subject to:  
\[t_{r}\geq\frac{E_{i}}{\sum_{i}k\left(E_{sen}^{i}+E_{pro}^{i}+E_{trans}^{i}+nE_{ amp}^{i}d_{SR}\right)},\ \forall i\in N \tag{17}\]  
\[E_{o}\geq E_{i},\ \forall i\in N \tag{18}\]  
\[E_{i}\to 0,\ \forall i\in N \tag{19}\]  
\[\sum_{i}f_{SR}>\sum_{r}f_{RC},\ \forall i\in N \tag{20}\]  
where \(f_{RC}\) and \(f_{SR}\) represent the data flow directions from relay \(R\) to the coordinator \(C\) and from in-body sensor node \(S\) to the corresponding relay \(R\), respectively. The Constraint (17) illustrates the network energy consumption per round. Constraints in (18) and (19) are the energy requirements, the network residual energy \(E_{i}\) reduces after each round and is finally exhausted. Constraint (20) demonstrates that the data flow should be transmitted from node \(S\) to the coordinator via a corresponding relay \(R\). Moreover, violation of (20) leads to heavy traffic conditions resulting in transmission delay and packet dropping.
# Exploring the Dynamics of Global Plate Motion Based on the Granger Causality Test
## 1 Introduction
The hypothesis includes the following: (1) the mid-ocean ridge is the outlet for the rise of mantle material, forming a new oceanic crust and promoting the expansion of the crust on both sides; (2) when mantle convection occurs inside the Earth, the solid lithosphere is driven to move; (3) the crust in the subduction zone sinks, carried by the cold, heavy, downward-moving mantle, inducing movement of the oceanic crust behind. Nevertheless, some problems have not been resolved by the hypothesis. Various aspects need to be improved and developed. For example, in global plate motion, the question is, which one is the dominant force, the pull effect existed at the subduction zone or the push effect existed at the mid-ocean ridge? Few articles have discussed this issue [28; 29; 30; 31]. [PERSON]'s study suggests that the forces acting on the downgoing slab control the velocity of the oceanic plates and are an order of magnitude stronger than any other force [32]; [PERSON] found that the stress generated by a subduction zone can drive an entire plate to move in the horizontal direction through the hypothesis test [33]; and [PERSON] [34] believed that the strength of subducted slabs probably regulates the velocity of plate tectonics. In contrast, some people hold opposing views about this hypothesis [30; 35]. Although many geophysicists have discussed this issue, there is no unified understanding and no definitive answer. Meanwhile, although some researchers found that the subduction force is the main force of plate motion, it is not known if this conclusion applies to all global plates, or only to a few plates. The output of an earthquake is the most direct result of relative plate movement and results from the sudden release of energy in the Earth's lithosphere. Thus, analyzing the series of earthquakes occurring at the border areas between plates may help us understanding the geodynamic mechanism and comprehend the relationship between the subduction and expansion effects. Therefore, based on plate motion and continental drift theory, this paper uses earthquake events data and the Granger causality test method to quantitatively analyze the interaction and energy transfer relationship between plate boundaries from the perspective of statistical physics. The objective of this paper is to explore the relationship between the pull effect and the push effect of plate motion, as well as the ductile mantle convection theory, to provide a new view for seismic energy transfer research and earthquake prediction.
## 4 Discussion
[PERSON] and [PERSON] [32] reported that the PAH recovery range was 71-130%. In the current experiment, the PCB recovery range using standard solutions was 100.4-128.5%, which was higher at 79-110% than the values reported by [PERSON] and [PERSON] [32]. With method 4, the PAH recovery range reported by [PERSON] et al. [33] was 52.8-101.4%, which was higher than the range of 30-80% obtained in the current study by using solutions. The PCB recovery range was very similar to that reported by the authors of method 4. The range was 92.17-124.9%, which was similar to that of 92.6-102.6% reported by [PERSON] et al. [33]. Additionally, the recovery ranges for individual PAHs, regardless of the analytical method used, fluctuated more than those of the individual PCB congeners. Additionally, inter alia, significantly elevated concentrations of heavy PAHs were noted (Figure 4). Primarily, method 2 more effectively removed impurities thanks to extraction combined with lipid alkaline hydrolysis using a methanolic KOH solution. The reasons for these discrepancies, both in the case of PAHs and PCBs, should be sought by evaluating the interference between the analyte and the compounds that were not removed during extract preparation in methods 3 and 4, i.e., fatty acids that passed into the extract. The sorbents used in this study were insufficient for simultaneously isolating PCBs and PAHs from complex organic matrices, which resulted, among other things, in overestimating the concentrations of some analytes. Effectively purifying extracts is possible using of modern sorbents. For example, it has been shown that PSA sorbentspermit the removal of all polar organic acids, polar pigments, sugars and fatty acids from extracts [46], while C18 permits the elimination of non-polar interfering substances, such as lipids (Table 5).
## 2 Materials and Methods
### Characterization
#### 2.2.2 Physical and Mechanical Property Measurement  
The average density of the aluminum matrix composites (AMCs) was investigated by Archimedes principle from 5 measurements. The measured (actual) density and the theoretical density were calculated to confirm the porosity of the specimens. The theoretical density was calculated using rule of mixtures, as in the equation below.  
\[\rho_{theoretical}=\rho_{matrix}*V_{matrix}+\rho_{reinforcement}*V_{reinforcement}\]  
where \(\rho\) is the density of the matrix and reinforcement and \(V\) is the volume fraction of each material.  
A tensile test was performed using a universal testing machine (5882 model, INSTRON, Norwood, MA, USA) with \(5\times 10^{-3}\) s\({}^{-1}\) head speed. Subsize dog-bone-shape specimens were prepared (1.5 mm thickness, 2 mm width, and 5 mm gage length). A hardness test was performed using a Rockwell hardness tester (HR-210 MR, MITUTOYO, Kawasaki, Japan) following the ASTM E18 standard. The average hardness values were calculated after measuring specimens 5 times each. An indenter was used with 1/16 (\(\Phi\) 1.588 mm) steel balls at B scale.
## 4 Experiment
### Experimental Results
#### 4.2.5 Experiment 5: Data Analysis  
To further analyze the effectiveness of the CTS algorithm, we recorded the recovery of minority correct answers by CTS compared to majority voting, shown in Table 8. As seen from the previous results, the accuracy of each ensemble algorithm on the Hilly dataset is lower compared to other datasets. However, Table 8 shows that the proportion of correct answers recoverable by CTS is the highest among all datasets, further demonstrating that our proposed strategy can accurately recover minority answers in challenging decision-making scenarios, reducing the drawbacks of majority voting.  
In this section, we evaluated CTS and CTS-SR on common binary classification datasets by measuring accuracy and F1 scores, and compared them with popular ensemble learning algorithms as well as traditional single-classifier models in supervised learning. The experimental results demonstrate that our proposed algorithms achieved significant advantages on most datasets, improving accuracy by 4-6% across diverse test data. Furthermore, the Friedman-Nemenyi test was used to rank the ensemble algorithms and revealed significant performance differences, highlighting the superiority and generality of our proposed methods. Additionally, through Experiment 5, we further demonstrated the ability of CTS to recover correct answers compared to the traditional majority voting strategy, indicating that our proposed method effectively addresses the drawbacks of majority decision making and underscores the importance of confidence in ensemble learning.
## 4 Experimental Results and Discussion
### Dataset  
#### 4.1.1 Host Image  
We used the BOSS dataset [16], which consists of 10,000 grayscale images with 512 \(\times\) 512 resolution, as the training dataset. The first reason that we chose the BOSS dataset is that it is used broadly in deep learning for various applications and techniques. Also, it contains only gray images that are more convenient to use in our network because it uses only the Y component, although Figure 1 and its explanation assumed more available RGB color images. Besides, a standard dataset [17], which has 49 grayscale images with 512 \(\times\) 512 resolution and is used broadly as the evaluation dataset, was used as the evaluation dataset. We have down-sampled images in both datasets to 128 \(\times\) 128 resolution to use as the host images.
# Predictive Modeling of Signal Degradation in Urban VANETs Using Artificial Neural Networks
## 4 Supervised Learning for Estimating the Bit Error Rate (BER)
### Data Collection
#### 4.1.1 Experimental Environment for Data Collection  
The experimental environment used for data collection consists of two simulators: a network simulator and a traffic simulator. To enable comprehensive studies based on the protocols under analysis, we chose NS-3 (Network Simulator 3) [26] as our network simulator. NS-3 offers a wide range of network components that facilitate diverse investigations in the field of wireless network technologies [27]. Moreover, NS-3 supports essential standards such as IEEE 802.11 PHY/MAC [28], 1609/WAVE [29], and 802.11p [30; 31], enabling realistic studies on Vehicular Ad Hoc Networks (VANETs). Furthermore, NS-3 provides support for Dedicated Short Range Communication (DSRC) [32], a technology widely employed in North America for real-world vehicle communications testing.  
To incorporate a mobility model specific to vehicles, we opted to utilize SUMO (Simulation of Urban MObility) [33] as our traffic simulator. SUMO is a frequently employed tool in studies related to traffic analysis in road networks. Additionally, SUMO offers the capability to interact with external applications through a socket connection. This interaction is facilitated by TraCi (Traffic Control Interface), enabling simulations in a client/server mode. Figure 2 shows a visual representation of our experimental environment.
## 3 Artificial Intelligence Techniques for 6G Networks
### Semantic Interoperability  
Semantic interoperability is a crucial aspect of AI-enabled 6G networks, ensuring that different systems and devices can effectively exchange and interpret data, with shared understanding. In the context of 6G, which involves a diverse array of applications, devices, and communication protocols, achieving semantic interoperability is vital for seamless integration and functionality. This entails the use of standardized data formats, ontologies, and metadata frameworks to enable consistent data interpretation across heterogeneous systems. AI plays a pivotal role in enhancing semantic interoperability by leveraging machine learning and natural language processing to automate the translation and align ment of disparate data sources. As 6G networks aim to support advanced applications like autonomous vehicles, smart cities, and IoT ecosystems, robust semantic interoperability ensures that data flow smoothly and meaningfully between various components, thereby enhancing efficiency, reducing errors, and enabling more intelligent and responsive network behaviors.