esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Detection with Transfer Learning from Simulated Data of Laser Active Imaging
## 6 Conclusions  
In this paper, we propose a high-precision, real-time UAV detection method to be used in the field of laser active imaging by combining a deep CNN-based object detection algorithm with transfer learning, which provides the basis for the following tracking and monitoring. For specific tasks like small-scaled UAV detection of laser active imaging, in which few samples can be used for training, the performance of the model degrades sharply. To solve this problem, we firstly embedded prior knowledge learned from a large available dataset into the model. Then, we provided sufficient data for the specific task by simulating the imaging process of laser active illumination. The experiment carried out on our real laser active imaging system demonstrates the transferability of simulated data and the effectiveness of our solution.  
However, limited by the laser power, we can only collect targets within 500 m. In future work, bigger and more realistic datasets will be gathered, including longer distances, more complex scenes such as occlusions by trees, and confusing objects such as birds. Further work that can be carried out is to add the depth information of the target plane in the simulation process, which will contribute to producing more realistic images. This measure has great potential to improve the performance of small-scale UAV detection in the laser active imaging domain.  
Conceptualization, S.Z. and G.Y.; methodology, S.Z.; software, S.Z.; validation, S.Z. and T.S.; formal analysis, S.Z.; investigation, S.Z.; resources, T.S.; data curation, S.Z.; writing--original draft preparation, S.Z.; writing--review and editing, G.Y. and K.D.; visualization, S.Z.; supervision, J.G.; project administration, K.D.; funding acquisition, J.G. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Detection results of our approach on real laser active imaging data. The top row presents the detection results of UAVs hovering in different backgrounds, from left to right are buildings (**a**), trees (**b**), and the sky (**c**). The bottom row (including **d–f**) shows the detection results when the UAV is at different flight attitudes with buildings in the background.  
**Funding:** This work was supported by the National Natural Science Foundation of China (#61675200, #61904178).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Work
### Benchmarking  
There is little work that is focused on the benchmarking of measurement errors. The authors in [1] use the International Organization for Standardization Guide to the Expression of Uncertainty in Measurement as a reference to their proposed measurement standard for automotive vehicle localization and its measurement uncertainty evaluation. The objective of their work is to provide a benchmarking tool for evaluating the accuracy and availability of localization systems in the market to provide assurance of safety of these localization schemes.  
Even with the proliferation of cooperative positioning proposals, they still fall behind the accuracy requirements of safety-oriented vehicular applications. Vehicular safety applications, such as collision avoidance and lane-level navigation, need sub-meter positioning accuracy [21]. In addition, cooperative positioning depends on message exchange, which can only work well in dense traffic scenarios [22]. None of the current research work investigates the time correlation between successive measurement errors. Motivated by this observation, in the following section we proceed to investigate the existence of such time correlation, and we propose a prediction model that incorporates this correlation in order to enhance the prediction accuracy of location information.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### Mobility Management Concept  
In the ideal case, a mobile UE's connection to the serving wireless network should remain stable even as the UE moves within cells, and this is the definition of mobility in wireless networks. When comparing wireless and wired networks, this is often cited as an advantage of the original. The UE's mobility allows it to move in a variety of ways. As long as there is coverage, the UE can switch its connection as it moves from the first cell (known as a serving BS) to a new cell (known as a target BS). The original serving BS can reroute the connection to the new target BS. All of these enhancements make wireless services more accessible to more users in more situations. The received signal strength (RSS) fluctuates continuously as the UE moves. A HO procedure is initiated when the RSS at a given location falls below a certain threshold defined by the RSS Indicator (RSSI). First, the serving BS sends a request to the target BSs, requesting that the UE's connection be rerouted to the target BS with the strongest signal. As a result, in the best-case scenario, the UE's connection to the serving networks will be stable throughout the user's journey [45].
## 1 Introduction
Research has been implemented to adaptively balance the interplay among loss terms. [PERSON] et al. [37] utilized the Dynamic Pull Method (DPM) to dynamically manipulate the weights of each loss term during the training process of PINN. [PERSON] et al. [38] built Gaussian probabilistic models for loss terms and adaptively updated the learning weights based on maximum likelihood estimation. These methods are dedicated to balancing the interplay between loss terms via the magnitudes of the loss terms, but pay little attention to the backpropagation gradients of each loss term with respect to the parameters of PINN. However, the gradients are essential for updating the parameters through the gradient descent method. [PERSON] [39] highlighted that the gradients of each loss term may become extremely unbalanced during the training process. In that scenario, the neural network may struggle to fit each loss term evenly, resulting in incorrect convergence. To maintain the balance in the gradients of loss terms of the PI_DeepONet during the training process, we propose an algorithm that can update the weights adaptively, utilizing the back-propagated gradient statistics. In this paper, the PI_DeepONet is constructed to serve as the solution operator of GDEs in beam and plate structures, which can efficiently solve the GDEs to reconstruct the displacement from strain under various loading conditions. This paper is structured as follows. In Section 2, the GDEs of the beam and plate under various restrictions are constructed. In Section 3, we provide the framework and implement the PI_DeepONet for the displacement reconstruction of beam and plate structures. The algorithm used to update weights adaptively according to the gradient statistics of each individual loss is also proposed in Section 3. In Section 4, the effectiveness of PI_DeepONet in the application of displacement reconstruction is validated via FEM. In Section 5, we present the main conclusions of our research.
## 2 Formulation of the Direct and Inverse Problems
### The Direct Problem  
We will study the model considered in [17; 24; 25]. It is a simplified MHD Hartman flow with incompressible and Newtonian (constant viscosity) characteristics in 1-D space and the case in which the fluid flows between two parallel solid plates and the velocity is perpendicular to the magnetic vector. Also, for the unit vectors of the velocity, the magnetic fields' external electromotive forces are generated right-handed. This mathematical model of describing the 1-D MHD flow system is derived from the viscous incompressible MHD equations [17; 21; 24] and is formulated in the following form:  
\[\frac{\partial u}{\partial t}-\
u\frac{\partial^{2}u}{\partial x ^{2}} = \beta(t)\frac{\partial B}{\partial x}-f(t), \tag{1}\] \[\frac{\partial B}{\partial t}-\
u_{m}\frac{\partial^{2}B}{ \partial x^{2}} = \beta(t)\frac{\partial u}{\partial x}. \tag{2}\]  
Here, the spatial variable \(x\) and the time variable \(t\) belong to the set \((x,t)\in Q_{T}=\Omega\times(0,T),\ \ \Omega=(0,X)\), where \(X\) and \(T\) are dimensionless quantities, \(u(x,t)\) is the flow velocity, and \(B(x,t)\) is the magnetic field. Further, \(f(t)\) is the pressure difference per unit of the channel length; the function \(\beta(t)\) is a given function of the induction of the external magnetic field and can be considered as the control input for the MHD flow; \(\
u=1/Re\) and \(\
u_{m}=1/Re_{m}\), where \(Re\) is the flow Reynolds number and \(Re_{m}\) is the magnetic Reynolds number.  
Dirichlet boundary conditions for the system (1) and (2) are posed:  
\[u(0,t)=u_{l}(t),\ \ u(X,t)=u_{r}(t),\ \ \ B(0,t)=B_{l}(t),\ \ B(X,t)=B_{r}(t) \tag{3}\]and the initial conditions are  
\[u(x,0)=u_{0}(x),\ \ B(x,0)=B_{0}(x). \tag{4}\]  
In the form (1)-(4), we solve the _direct (forward) problem_ in which \(f(t)\), the coefficients \(\
u,\
u_{m},\beta(t)\), the boundary, and the initial conditions are given.
# Variability of Diffuse Daylight Due to the Diversity of Cloud Arrays
## 3 Results
### Comparison of Two Specific Skies  
During the simulations, two sun and cloud configurations were found which produced the same direct horizontal illuminance and nearly the same diffuse horizontal illuminance. However, the corresponding sky luminance patterns differed due to the different cloud types and their distribution. These luminance patterns are shown in Figure 4.  
The clouds have a radius of 0.5 km and the base 0.5 km above the ground in the first case (case 1). In the second case (case 2), it was 0.25 km and 1 km. The cloud fraction was 0.3 and the zenith angle of the sun was 40\({}^{\circ}\) in both cases, though the random distribution of clouds in the sky vault was diverse. The sun is not covered by clouds, so the direct horizontal illuminance was equal to 80.917 klx. The calculated diffuse horizontal illuminances were 28.059 klx for case 1 and 28.19 klx for case 2.  
Nearly identical illuminances indicate nearly identical irradiance. According to the standard empirical models [15; 16; 17], the sky luminance distribution in these two cases should be very similar, not regarding the specific cloud arrays. One such sky luminance map calculated by the Perez all-weather model [16] is shown in Figure 5.  
Figure 4: Sky luminance patterns for two simulated cloud arrays differing in their distribution and the cloud parameters: (**a**) case 1—the cloud radius 0.5 km, the base altitude 0.5 km; (**b**) case 2—the cloud radius 0.25 km, the base altitude 1 km. The cloud fraction equal to 0.3 and the solar zenith angle of 40\({}^{\circ}\) were assumed in both cases.  
Figure 5: Sky luminance pattern according to the Perez all-weather model computed for case 1.  
The almost identical luminance patterns predicted by the empirical model for these two cases result in very close illuminances on the same vertical walls. To demonstrate this fact, we estimated diffuse illuminances on the four vertical surfaces under consideration using the Perez all-weather model [16] and compared them to the values obtained by the UniSky simulator. The results are shown in Figure 6. Apart from the fact that the Perez model underestimates the illuminances when compared to the UniSky model, it is clear that it is incapable of capturing the differences between the two cases.
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 4 Evaluation  
### Introduction of the Datasets  
The Australian Imaging, Biomarker & Lifestyle (AIBL, aibl.csiro.au) [34] and the Alzheimer's Disease Neuroimaging Initiative (ADNI, adni.loni.usc.edu) [35] were used in this study. The National Institutes of Health and the National Institute on Aging provided funds to establish the ADNI, the premier data center for AD research. Both datasets gather sMRI, fMRI (functional MRI), and positron emission computed tomography (PET) from AD and NC participants.  
The data used in the study were collected from an MRI scanner built by the MRI manufacturer SIEMENS. The slice thickness is 1.2 mm; the field strength is 3.0 Tesla. All the sMRI images were downloaded from ADNI-GO, ADNI1, and AIBL. Standard sMRI image preprocessing was conducted using SPM12 (fil.ion.ucl.ac.uk) on Matlab (R2022a), including format conversion, AC-PC correction, non-parametric non-uniform intensity normalization (N3), and alignment to the MNI standard template. The reconstructed images are 181 \(\times\) 217 \(\times\) 217, the voxel size is 1 \(\times\) 1 \(\times\) 1 mm\({}^{3}\), and normalized intensity values are in the range of [0,1]. Table 1 displays the demographic information of the datasets.  
The ADNI and AIBL datasets are openly accessible, but access to the information still needs official authorization. Additionally, no data may be shared without consent, and only approved researchers may utilize it for studies. The summary of the demographics of the dataset is illustrated in Table 1.
# Apollonian Packing of Circles within Ellipses
## 2 Materials and Methods
### Tangent Circles in the Elliptic Domain
#### 2.3.4 Determination of the Tangent Circle Center and Radius  
The search for circle \(\mathcal{C}_{t}\), shown in Figure 5a, which is externally tangent to circles \(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\) and internally tangent to elliptic boundary \(\mathcal{E}\), may be carried out by means of a bisection algorithm, restricting the indeterminacy position interval for circle center \(\mathcal{C}_{t}\). The first guess for the indeterminacy interval for point \(\mathcal{C}_{t}\) is hyperbolic arc \(T_{1,2}K\), with \(T_{1,2}\) being the tangency point between circles \(\mathcal{C}_{1}\) and \(\mathcal{C}_{2}\), and \(K\) being the intersection point between ellipse \(\mathcal{E}\) and hyperbola \(\mathcal{A}_{1,2}\) found in the previous subsection. The first approximation of point \(\mathcal{C}_{t}\) is represented by point \(M\), corresponding to the mean value between values \(t=0\) (point \(T_{1,2}\)) and \(t=t_{K}\) (with \(P(t_{K})\) being the best approximation of point \(K\)). Then, condition \(\overline{MC_{2}}-r_{2}=\overline{MC_{1}}-r_{1}\gtrless d(M,\mathcal{E})\) is tested by checking the equidistance of \(M\) from boundaries \(\mathcal{C}_{1}\), \(\mathcal{C}_{2}\) and \(\mathcal{E}\), as stated in Equation (9). By means of such condition, the indeterminacy interval for point \(\mathcal{C}_{t}\) may be restricted to the first half or the second half of the interval, and a better approximation of \(\mathcal{C}_{t}\) may be found. The algorithm is reiterated until the desired accuracy for center \(\mathcal{C}_{t}\) is obtained, i.e., when the difference between the distance of point M from \(\mathcal{C}_{1}\) (or \(\mathcal{C}_{2}\)) and ellipse \(\mathcal{E}\) is less than predefined accuracy \(\epsilon_{\textit{tng}}\): \((\overline{MC_{2}}-r_{2})-d(M,\mathcal{E})=(\overline{MC_{1}}-r_{1})-d(M, \mathcal{E})<\epsilon_{\textit{tng}}\), and circle \(\mathcal{C}_{t}\), shown in Figure 5b, may be drawn.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 2 Part A: Bibliometric Analysis
### Descriptive Bibliometric Analysis  
The overview of the descriptive bibliometric statistics for the dataset (Figure 1) is presented in Table 1. The search strategy yielded 133 relevant publications on hyperspectral image processing for anomaly detection in remote sensing applications that were published in 41 sources (Table 1). The number of citations per document, on average, was 72.65, or 8.14 citations per document per year, respectively. In the selected publications, a total of 4276 documents were cited. Most publications that fulfilled the search strategy were journal articles (118), with a smaller fraction of conference papers (15). There were only five documents created by a single author, while the average number of co-authors per document was 3.71. In the end, the collaboration index [24] was calculated as the ratio of the total number of authors of multi-authored documents and the total number of multi-authored documents, and scored the value of 2.3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Main Information** & **Result** \\ \hline Time span & 2000–2020 \\ Sources & 41 \\ Total number of documents & 133 \\ Average years from publication & 7.41 \\ Average citations per documents & 72.65 \\ Average citations per year per doc & 8.14 \\ References & 4276 \\ \hline
**Document types** \\ \hline Article & 118 \\ Conference paper & 15 \\ \hline
**Authors and collaboration** \\ \hline Authors & 299 \\ Authors of single-authored documents & 5 \\ Authors of multi-authored documents & 294 \\ Co-Authors per Documents & 3.71 \\ Collaboration Index & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary bibliometric statistics of the relevant publications on hyperspectral image processing for anomaly detection in remote sensing applications acquired by the presented search strategy (Figure 1).  
Figure 1: PRISMA flow chart of the procedure for selection of relevant publications.  
The core sources of relevant documents, by Bradford's law [25], were IEEE Transactions On Geoscience And Remote Sensing and IEEE Geoscience And Remote Sensing Letters, which accounted for a total of 54 publications (Table 2).  
The top journal (Table 2) was appreciated among the first publications on this topic, and the appearance of the consequent sources followed their popularity (Figure 2a). The first document was published in the IEEE Geoscience And Remote Sensing Letters (rank 2) in 2005, in the IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing in 2012, and in the Remote Sensing in 2014. Researchers in this scientific field were most productive in 2018 (Figure 2b). However, the articles published in 2002 and 2005 attracted the most attention from the scientific community (Figure 2b).
# Architectural Design for Digital Twin Networks
## 4 Functional DTN Implementation
### DTN Description
#### 4.1.3 Physical Network Connection Facet  
This facet comprises two processes. First, each node in the mesh is set up to generate notifications with a Time-To-Live (TTL) of 1, reaching only neighboring nodes, at specific intervals. Second, at a separate interval, these nodes send the collected data to the gateway connected to the DTN.
# Ship Scheduling Algorithm Based on Markov-Modulated Fluid Priority Queues
## 3 Problem Modeling and Algorithm Modeling
### Algorithm Modeling and Parameter Setting  
The SSA-BMMFPQ proposed in this study realizes a priority response and the orderly control of the overall load for high-priority ships by modeling ships of different priority as independent fluid inputs modulated by the state and assigning service rates. Ships arrive from different channels, and the console controls them to enter the anchorage according to their arrival conditions. The anchorage condition is regulated by SSA-BMMFPQ. The algorithm model integrates a state transition table, a priority controller, model parameters, and control logic. Ships of different priority levels all operate under this system, as shown in Figure 1.  
SSA-BMMFPQ firstly introduces different task flows into corresponding service queues through a priority mechanism; each queue consists of multiple service nodes and processes tasks according to the set service rate. Based on this, the queue length is calculated by the matrix method, and the service time distribution is approximated by the Erlang method, so that the exact queue performance index is obtained. The waiting time is analyzed based on the queue length information, and the Laplace transform (LST) of the waiting time is obtained through the reward accumulation method; finally, the actual waiting time is calculated and evaluated, as shown in Figure 2.  
Table 2 shows the model parameter design of SSA-BMMFPQ. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Symbol** & **Meaning** \\ \hline K & Set of priority classes for ships \\ \(\{I(t),t\geq 0\}\) & CTMC that describes the state of the anchorage \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter table of algorithm model. Figure 1: Schematic diagram of multi-priority ship anchorage dispatching system.
# Ontology-Based Feature Selection: A Survey
## 3 Feature Selection  
The first step towards successful classification is to define the features that will be input to the classifier. This process is called Feature Engineering (FE) and encompasses algorithms for generating features from raw data (feature generation), transforming existing features (feature transformation), selecting most important features (feature selection), understanding feature behavior (feature analysis), and determining feature importance (feature evaluation) [39].  
Feature selection is well studied under the framework of FE. An increasing number of dimensions in the feature space results in exponential expansion of the computational cost. This issue is directly related to the problem of the curse of dimensionality. Furthermore as the volume of feature space increases, it becomes sparsely populated and even close data points may be driven apart from irrelevant data, thus appearing as far away as unrelated data points. This will increase overfitting and reduce the accuracy of the classifier. Restricting the used features to only those that are strictly relevant to the target classes results in improved interpretability of the model  
The feature selection process attempts to remedy these issues by identifying features that can be excluded without adversely affecting the classification outcome. Feature selection is closely related to feature extraction. The main difference is that while feature selection maintains the physical meaning of the retained features, feature extraction attempts to reduce the number of dimensions by mapping the physical feature space on a new mathematical space.  
Feature selection can be supervised, unsupervised, or semi-supervised. Supervised methods consider the classification information and use measures to quantify the contribution of each feature to the total information, thus keeping only the most important ones. Unsupervised methods attempt to remove redundant features in two steps. First, features are clustered into groups, using some measure of similarity, and then the features with the strongest correlations to the other features in the same group are retained as the representatives of the group. Identification and removal of irrelevant features is more difficult and abstract and depends on some heuristic of relevance or interestingness. To devise such heuristics, researchers have employed several performance indices namely, category utility, entropy, scatter separability, and maximum likelihood [40]. Semi-supervised feature selection addresses the case when both a large set of unlabeled and a small set of labeled data are available. The idea is to use the supervised class-based clustering of features in the small dataset as constraint for the unsupervised locality-based clustering of the features in the large dataset.  
Depending on whether and how they use the classification system, feature selection algorithms are divided into three categories, namely filters, wrappers, and embedded models.