esa-sceva/satcom-chunk-collection huggingface dataset:
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Experimental Details  
#### 3.3.1 Evaluation Metrics  
To comprehensively evaluate the performance of the Siamese-SAM model, we adopt a set of evaluation metrics, including precision (PR), recall (RC), overall accuracy (OA), Kappa coefficient (KAPPA), intersection over union (IoU), and F1 score (F1). These metrics provide a well-rounded assessment of the model's effectiveness in detecting changes in remote sensing images.  
Among these metrics, the F1 score and IoU are the primary indicators used to evaluate the model's performance. The F1 score focuses on the accuracy of the detected changed category, while the IoU [46] measures the overlap between the predicted changed area and the ground truth. In addition, PR, RC, OA, and KAPPA [47] are employed as supporting metrics to provide a more comprehensive and detailed evaluation. Their inclusion ensures a thorough understanding of the model's overall accuracy, class balance, and agreement with the ground truth.  
The equations for these metrics are as follows:  
\[PR=\frac{TP}{TP+FP} \tag{1}\]  
\[RC=\frac{TP}{TP+FN} \tag{2}\]  
\[F1=\frac{2\times PR\times RC}{PR+RC} \tag{3}\]  
\[OA=\frac{TP+TN}{TP+TN+FP+FN} \tag{4}\]  
\[E=\frac{(TP+FP)\times(TP+FN)+(FN+TN)\times(FP+TN)}{(TP+TN+FP+FN)^{2}} \tag{5}\]  
\[KAPPA=\frac{OA-E}{1-E} \tag{6}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{7}\]  
Figure 9: Visualization of the GZ-CD dataset. T1_img and T2_img are images at different times, and columns (**a**–**e**) are the corresponding instances.  
In this context, _TP_ (True Positive) refers to the correctly predicted changed area, while _FP_ (False Positive) represents the unchanged area that is mistakenly identified as changed. Similarly, _TN_ (True Negative) denotes the area correctly identified as unchanged, and _FN_ (False Negative) refers to the changed area that is mistakenly marked as unchanged. These definitions form the basis for calculating the evaluation metrics.
# TYCOS: A Specialized Dataset for Typical Components of Satellites
## 3 The TYCOS Dataset
### Image Annotation  
To ensure the success of space capture missions, three key components on the capture surface are annotated, including solar panel, satellite body, and radar. In addition, Labellmg [30] was utilized for marking the collected images by drawing rectangular bounding boxes around the key components. To attain high-quality spatial target annotations for each image, we engaged ten trained annotators and tasked them with annotating the three key components. This was followed with three rounds of cross-checking and correction to enhance the annotation quality. The entire annotation process, involving 25,504 images, was completed in approximately one month. The position of each key component was described with a bounding box, denoted as \((x_{max},y_{max},x_{min},y_{min})\), where \((x_{max},y_{max})\) represents the maximum coordinates of the bounding box vertices, and \((x_{min}y_{min})\) represents the minimum coordinates of the bounding box vertices. The annotated images and their corresponding labels were structured according to the standard COCO format dataset. The annotation results are depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**States** & **Classification** & **Statistic** & **Number of Instances in Dataset** \\ \hline \multirow{3}{*}{Illuminations} & Low illumination & 3415 & \multirow{3}{*}{9564} \\  & High saturation & 2968 & \\  & Normal illumination & 3181 & \\ \hline \multirow{3}{*}{Motion States} & Approaching states & 5236 & \multirow{3}{*}{15,940} \\  & Hovering states & 5191 & \\ \cline{1-1}  & Random roll state & 5513 & \\ \hline \multirow{3}{*}{Occlusion States} & Partial occlusion on the left side & 2657 & \multirow{3}{*}{6376} \\  & Partial occlusion on the right side & 2385 & \\ \cline{1-1}  & Complete occlusion & 1334 & \\ \hline \multicolumn{2}{c}{Total} & & 31,880 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The statistics of the number of different states in the dataset.  
Figure 8: Examples of annotation results using Labellmg.
## 2 Methods  
In this study, we developed an AI model to predict the need for dental SMs using X-ray images. The evaluation was conducted specifically for single-tooth loss, focusing only on a specific tooth in the radiographs, and the type of SM to be used was not assessed. The evaluation of space maintainer necessity was conducted using a dual-expert evaluation approach, considering factors such as the time elapsed since the extraction, the available space, the amount of bone covering the permanent tooth germ, the patient's dental age, and the sequential eruption pattern of the teeth. The process involved multiple stages, beginning with the preprocessing of dental X-rays into numerical data using OpenCV for compatibility with machine learning models. A CNN was then designed, trained, and tested using a labeled dataset to classify whether an SM is required and, if so, to identify the specific tooth. The methodology is detailed in the following subsections, which cover data preparation, image processing, model architecture, training and testing, and evaluation metrics. While CNNs are widely used in medical imaging, our study is innovative in applying deep learning specifically to SM prediction--an underexplored area in pediatric dentistry. Unlike existing models that focus on diagnosing dental conditions, our approach not only determines the need for an SM but also identifies the specific tooth requiring intervention. Additionally, we implemented standardized image preprocessing and a rigorous evaluation framework. Our work contributes to preventive dentistry, offering an AI-driven tool to assist clinicians in objective decision making.
# Progress and Summary of Photodarkening in Rare Earth Doped Fiber
## 3 Phenomena and Problems Caused by Photodarkening
### Output Power Reduction  
Because the absorption spectrum of the color center generated in the PD process covers the pump band and even the signal band of the fiber laser, the output power and slope efficiency of the fiber laser is significantly reduced. In 2007, [PERSON] et al. [2] measured the slope efficiency of a CW fiber laser oscillator by exposure to 45 W of pump  
Figure 4: Absorption spectra of fiber C before and after exposure to 200 mW of power at 1.12 \(\upmu\)m for 120 s. Adapted with permission from [4] © The Optical Society.  
power at 976 nm under different light aging conditions. The slope efficiency decreased considerably when a fiber with photon darkening was used as the laser medium (Figure 6).  
On the other hand, the deactivation of doped ions in the process of color center generation also reduces the laser efficiency. In 2013, [PERSON] et al. [70] studied the fluorescence characteristics of Yb-doped fiber before and after pump irradiation. It was found that the fluorescence intensity and lifetime of Yb\({}^{3+}\) decreased significantly during PD, which was remarkably related to the measured PD loss. To explain this phenomenon, a theory of non-radiative energy transfer from excited Yb\({}^{3+}\) ions to atomic defects (color centers) close to Yb\({}^{3+}\) ions was proposed. This energy transfer leads to the inactivation of Yb\({}^{3+}\) ions, resulting in additional loss of laser efficiency.  
Figure 5: Absorption spectra of an unphotodarkened LMA Yb-doped fiber (red curves) and of the same fiber at different states of PD at 7 min, 15 min, and 100 min (orange, green, and blue) of pumping the fiber by the light of 45 W at 976 nm. Adapted with permission from [2] © The Optical Society.  
Figure 6: Output characteristics of the fiber laser for different states of PD: unphotodarkened fiber (red squares and linear fit to the data as a black solid line), 16 min (orange squares), 64 min (green triangles), and 155 min of PD (blue circles). Adapted with permission from [2] © The Optical Society.
# HYDRUS-1D Simulation of Soil Water Dynamics for Sweet Corn under Tropical Rainfed Condition
## 3 Results and Discussions
### Surface Runoff  
Figure 8 shows the comparison of simulated and observed values of surface runoff for two consecutive seasons. The results for cumulative simulated and observed values were found in good correspondence. However, the results differ for some of the individual rainfall events. The first few low-intensity rainfall events do not produce runoff in actual field conditions while HYDRUS-1D simulation shows runoff for those events for both seasons. The reason for deviation seems to be fine soil tilth at the time of crop plantations that enhance the water holding capacity of the soil. The maximum rainfall value during the first season was recorded as 11.3 cm on the 33 rd day of the plantation, which produced peak simulated and observed runoff of 9.61 and 9.12 cm, respectively. Whereas the maximum rainfall value during the second season was recorded as 6.47 cm on the 29 th day of the plantation, that produced peak simulated and observed runoff of 4.92 and 6.05 cm, respectively. The difference in the ratio of runoff to total rainfall varies from event to event due to soil antecedent moisture level, rainfall amount, and intensity. Surface runoff accounted for 41% and 28.6% of total water input for first and second season respectively. Effective runoff simulation also shows HYDRUS-1D ability to handle the upper boundary condition.  
Figure 7: Simulated and observed soil water contents below the root zone (60–140 cm) during the (**a**) first season and (**b**) second season.
## 3 Results and Discussion
### Principal Component Analysis  
Principal component analysis (PCA) is an effective technique used in pattern recognition to identify complex datasets with interrelated variables. It is a statistical approach that derives eigenvalues and eigenvectors from the correlation matrix of the dependent variables. The principal component (PC) is formed by creating a linear combination of the original variables, achieved by multiplying the variables by their corresponding eigenvector [62].  
PCA was conducted using the data provided in Table 3, with the mean values of each variable from each distillery used for the analysis. The Scree plot and Biplot derived from the PCA are illustrated in Figures 2 and 3, respectively. The Scree plot revealed that the eigenvalues for only PC1 and PC2 were greater than 1. Consequently, only two principal components were extracted in this study. PC1 accounted for 56.28% of the variance, while PC2 accounted for 24.37% of the variance (Figure 3). Together, PC1 and PC2 explained approximately 80.65% of the variance in the dataset, which is generally considered satisfactory. The eigenvectors extracted from PCA are provided in Table 4.  
It was observed that protein, hemicellulose, and lignin were the primary contributors to the variance in PC1, with positive loading values. The largest contributor to PC1 was lignin, followed by hemicellulose and protein. In contrast, the largest contributor to the variance in PC2 was cellulose, followed by lignin, both with positive loading values. However, extractive content was negatively associated with both PC1 and PC2, as indicated by negative loading values.  
Figure 2: Scree plot.  
The positive loading values of cellulose, hemicellulose, lignin, and protein in either PC1 or PC2 demonstrate that the PCA model effectively extracted meaningful insights from the data, highlighting the similarities or differences among samples from various distilleries. From Figure 3, it was observed that no distinct clusters or groupings were formed among the distilleries. This finding indicates that the DSG samples obtained from the nine distilleries were different, with no significant similarities among them.
## 3 Results and Discussion
### Characterization of Zr-EPRC  
#### 3.2.1 Scanning Electron Microscopy (SEM)  
Through an SEM observation (Figure 9a-d), we found that the surface of the Zr-EPRC material is rough and uneven, providing a large surface area. The internal structure appeared fragmented and loose, with irregularly stacked coral-shaped protrusions, increasing the contact area between the material and the adsorbate. Additionally, the material exhibited abundant pore structures, offering ample space for adsorption and enhancing its adsorption capacity, thereby creating favorable conditions for efficient phosphorus removal.  
Figure 8: Phosphorus adsorption via fly ash under different loading times.  
Figure 7: Phosphorus adsorption in steel slag under different loading times.
## 3 Proposed Methodology
### Connected Dominating Set (CDS) and IDS Node Selection  
A group of nodes is said to be a CDS if they are selected in such a way that they belong to an IDS set covering the entire range of the network. A trusted node with sufficient energy is selected as the IDS node for query processing. All of the nodes under the IDS set are connected with each other. They are connected in such a way to ensure the full coverage of the network. We have extended the CDS approach by selecting the IDS query issuing node from the IDS set. Before selecting this node, the proposed DDBG technique checks two factors, i.e., the trust and energy of a node. For a node to become trusted, all of the nodes in the IDS set must observe their neighboring node in promiscuous mode. Every node in the IDS set monitors the behavior of the neighboring node regarding the packet dropped or put forward and then this information is stored in their own table of knowledge.  
Equation number (1) calculates the energy of the IDS node for query processing (status packet). The node must have energy and should be labeled as a trusted node. If both of these conditions apply to a node, then that node is selected for query issuing to send the query (status packets) periodically.  
Let N be any node:  
TE (N) = The total energy of the node when it is fully charged  
BE (N) = The beginning energy level of the node  
CE (N) = The current energy of the node  
\(\Gamma\)= The maximum % of the BE (N) for the IDS query node  
\(\Theta\)= The minimum % of the TE (N) must be conserved.  
Therefore, node N cannot be selected as a query issue node if:  
\[\left(1-\frac{CE}{BE}\right)\times 100>\Gamma\text{ OR }\left(\frac{CE}{TE}\right)\times 100<\Theta \tag{1}\]  
The values of \(\Gamma\) and \(\Theta\) are based on the average energy of the nodes in the network.  
Figure 2 shows the whole process of the IDS nodes. The purpose of the deployment of an IDS node is to provide a strong defense mechanism against the black hole and gray hole attacks in MANETs. IDS nodes continuously observe the behavior of nodes by sending status packets to check if a node is legitimate or is a malicious node. If any node is misbehaving (i.e., dropping full data packets it receives, dropping half of the data packets, or dropping selective data packets during data communication), the IDS node broadcasts a block message to notify the other nodes about the malicious node.  
There were two assumptions considered when designing the proposed DDBG technique, as follows:  
* All of the nodes are connected within the IDS set and at least one IDS node must be in the range of the other IDS nodes, i.e., The IDS node can share the information about the malicious node to all of the remaining nodes in the entire range.
* All of the IDS nodes must have enough energy to broadcast the status packet to detect malicious behavior.
## 2 Sex Differences in MS Epidemiology  
MS is considered the primary cause of non-traumatic neurological disability in young adults [11]. The mean age of onset has progressively increased in the past decades, going from 23.79 \(\pm\) 10.19 in the 70s to 31.11 \(\pm\) 9.82 in 2022 [12]. As aforementioned, MS is a multifactorial disease; genetic variants, lifestyle, and environmental factors (e.g., EBV infection, low vitamin D, smoking, and adolescent obesity) are associated with an increased risk of developing the disease [13]. Migration studies highlight the importance of non-genetic factors in modulating the disease risk. The risk of developing MS depends on the destination country and the age at which an individual migrates. Individuals migrating from low-risk countries to high-risk countries before adolescence acquire a high risk, which could suggest environmental factors in the etiology of MS. The existence of a sex bias in MS epidemiology, which increased over the last decade, is also considered further proof of an important environmental influence on disease risk [14; 15; 16]. A higher incidence of MS in females has been recognized for many years, and the prevalence in females has been increasing in the past decades, leading to a mean female-to-male ratio of 3-4.5:1 [2]. Differences between sexes are also observed in the relapse rate and disease course. Females show a higher relapse rate after the disease onset compared to males. In contrast, males are less susceptible but show a higher accumulation of disability [14]. An increased susceptibility in females compared to males has been described for many autoimmune diseases [15]. Environment-genotype-sex interactions influencing the penetrance of specific gene polymorphisms have been implicated in the increased immunologic susceptibility observed in females [16]. Differences in sex chromosomes, sex hormones, or in the complex interplay between sex and the environment can be advocated to explain the sex bias [14].
## 5 Model Architectures and Training
### Model Architectures
#### 5.1.2 3D Convolutional Network  
In order to consider temporal context within the convolution operations, 3D spatiotemporal convolutions are evaluated. For that purpose, the ResNet3D [46] architecture is utilized. It is an adaptation of the renowned Residual Network (ResNet) architecture [47], designed to process three-dimensional data by replacing classic 2D convolutions with 3D convolutions to extract spatiotemporal features. By that, it extends the foundational principles of ResNet into the 3D domain, leveraging volumetric data across various applications, particularly in video analysis [48]. Like its 2D counterpart, ResNet3D is structured in subsequent layers, each with 1...N blocks of convolution, ReLu, and normalization layers. Each layer is connected to its subsequent neighbor by additional residual connections, either directly or by a residual unit with an additional convolution and normalization layer (see Figure 7).  
The residual connections are critical in mitigating the vanishing gradient problem, allowing for deeper networks by enabling a direct flow of gradients. Further, the inclusion of residual connections ensures that information is preserved across the network during inferencing, enhancing the model's ability to capture and utilize deep, complex patterns in 3D data. Similar to the Conv-LSTM, the classification and regression task is accomplished by a respective MLP, here with one linear layer.
# Metaverse Meets Smart Cities--Applications, Benefits, and Challenges
## 5 Results
### Real-World Metaverse Applications in the Smart City  
Table 2 lists exemplary smart city metaverse applications and records whether they are pure metaverse developments or only fulfill some components and attributes. The ap  
plications are explained in further detail in the following paragraphs, including references to the respective sources. Seoul--Republic of Korea: The South Korean city of Seoul is the first city in the world to venture into the metaverse. Under the name \"Metaverse Seoul\", which is part of the comprehensive plan called \"Seoul Vision 2030\", residents are offered various services in a specially developed metaverse in addition to virtual exploration of the city. The project is divided into three phases. In the first phase, which started in spring 2023, users can explore the city with the help of avatars and ask questions about their personal tax affairs or receive assistance with various civic issues. The other phases will be completed by 2026, during which further services will be introduced. This includes the vision that citizens can interact with the city's infrastructure by combining virtual and physical reality. Various target components are described in Seoul Vision 2030. The services are divided into seven areas: Economy, Education, Communication, Culture, Urban Development, Administration, and Taxation. The platform offers various functions that developers can use to develop applications for the abovementioned areas [32]. For this purpose, functions are provided that enable the management of citizen data, tax accounting, education, citizen complaint management, culture, and tourism. The Seoul metaverse has created new opportunities for residents and visitors to experience and interact with the city without being physically present [33]. In addition to digitalizing citizen services and tourism, the city wants to use modern technologies to solve challenges such as poor air quality and improve the healthcare system. Seoul was awarded a prize for its innovation efforts and was named Smart City 2022 [32]. Helsinki--Finland: Virtual Helsinki is a digital replica of Helsinki's city center, launched in 2018. Developed initially as a digital twin, the application allows users to experience the city's famous sights virtually without being physically present. Virtual Helsinki is available for various VR devices and as a 360 video for mobile devices, providing visitors with an
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
###### Abstract  
Much research effort has been conducted to introduce intelligence into communication networks in order to enhance network performance. Communication networks, both wired and wireless, are ever-expanding as more devices are increasingly connected to the Internet. This survey introduces machine learning and the motivations behind it for creating cognitive networks. We then discuss machine learning and statistical techniques to predict future traffic and classify each into short-term or long-term applications. Furthermore, techniques are sub-categorized into their usability in Local or Wide Area Networks. This paper aims to consolidate and present an overview of existing techniques to stimulate further applications in real-world networks.  
machine learning; optical networks; traffic prediction +
Footnote †: journal: _Journal of the American Statistical Association_  
The Internet follows an explosive growth trend, with exponential increases in connected users, devices, and data transmission volumes. By 2023, there will be an estimated 5.3 billion Internet users, with each user connecting, on average, 3.6 devices to the network. The global average bandwidth speed is also expected to increase from 61.2 Mbps in 2020 to 110.4 Mbps in 2023 [8]. At such a large scale and growth rate, it is becoming more and more difficult to control, manage, monitor, and predict traffic patterns in optical networks using traditional manual operations. As such, machine learning can be the principal solution to automate network decisions to improve performance, mainly through traffic prediction.