esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Multi-Frame Optical Detection Algorithm for GEO Space Objects
###### Abstract
processing algorithm named CFS for further candidate filtration and supplement. The main contributions of this paper are concluded as follows. 1. To the best of our knowledge, this is the first effort toward introducing the deep model PP-YOLOv2 into the field of space object detection. We evaluate several representative models and conduct enough comparable experiments. This is expected to facilitate the development and benchmarking of object detection algorithms in satellite images. 2. To make the most of motion characteristics that space objects take on linear trajectories, an effective candidate filtration and supplement (CFS) method based on the straight-line searching strategy is designed for further increasing accuracy. Experimental results demonstrate that the proposed algorithm reaches an F1-score of 93.47% with competitive performance. 3. In order to verify the efficiency of proposed algorithm, we transplant the proposed pipeline to the embedded system platform of NVIDIA Jetson Nano. Considering the metric of frame per second (FPS), the proposed method achieves an inference speed at 1.42 FPS, with a ResNet50 [11] backbone other than typical lightweight backbone networks, which will pave the way for further optimization and improvement to meet the demand of future real-time image processing. The remainder of this paper is organized as follows: Section 2 briefly highlights related work and materials. Section 3 describes the multi-frame object detection pipeline in detail, including the 2D single-frame detector and the proposed CFS technique. Section 4 provides overall performance and comparison results of the proposed method with analysis and discussion. Finally, conclusions with a summary of the key findings of our work are drawn in Section 5.
# Jamming Analysis between Non-Cooperative Mega-Constellations Based on Satellite Network Capacity
## 2 System Model
### Propagation Model  
The free space propagation model is utilized to calculate the channel gain between satellites. Assume that the distance between the jamming satellite and jammed satellite is \(d\), and the free space attenuation of the signal is  
\[L_{d}=\left(\frac{\lambda}{4\pi d}\right)^{2} \tag{3}\]  
According to ITU-R S.1528 [15], when the satellite antenna's off-axis angle is \(\varphi\), the receiving antenna peak gain \(G_{r}(\varphi)\) is  
\[G_{r}(\varphi)=\begin{cases}G_{r}^{\max},&\text{if }\varphi\leq\varphi_{b}\\ G_{r}^{\max}-3\left(\frac{\varphi}{\varphi_{b}}\right)^{2},&\text{if }\varphi_{b}< \varphi\leq Y\\ G_{r}^{\max}+L_{s}-25\log(\frac{\varphi}{\gamma}),&\text{if }Y<\varphi\leq X\\ L_{F},&\text{otherwise}.\end{cases} \tag{4}\]  
where \(G_{r}^{\max}\) is the maximum receiving antenna peak gain. \(X=Y(10)^{0.0\text{(G}_{r}^{\max}+L_{s}-L_{F})}\), \(L_{F}=0\). For LEO satellites, \(L_{s}=-6.75\), \(Y=1.5\theta_{b}\). According to the reciprocity theorem, the transmitting antenna peak gain \(G_{t}(\varphi)\) equals \(G_{r}(\varphi)\).
# Formulation Strategies to Improve Oral Bioavailability of Ellagic Acid
## 4 Formulation Strategies for Improving EA Oral Bioavailability  
The Biopharmaceutics Classification System (BCS) is a scientific framework for classifying drug substances based on their aqueous solubility and intestinal permeability. It is a drug-development tool that divides drugs into high/low solubility and permeability classes. Based on its low aqueous solubility and low permeability (apparent permeability coefficient = 0.13 \(\times\) 10\({}^{-6}\) cm/s [47]), EA is classified as class IV drug according to the BCS, i.e., endowed with low solubility and low permeability, features that limit a lot its clinical application. Micro and nanotechnology approaches widely demonstrated great potential in modifying pharmacokinetic, bioavailability and stability of several drugs, including phytochemicals used in cancer chemoprevention or in dietary supplements, contributing to preserve the properties of the active ingredients or to mask bad tastes and odors [48; 49; 50]. From a formulation point of view, a good EA dosage form would grant easier handling, transport and storage, increase its light resistance, hamper undesirable reactions such as oxidation and hydrolysis, and enhance solubility.  
Focusing on preparative methods, features and biological results of the developed EA-loaded formulations, an exhaustive description of the employed strategies is thereafter presented. The various approaches were divided according to the kind of the dosage form and reported in Tables 2 and 3 depending on the micro or nanosize of the final product. Moreover, in the final paragraph, examples of fixed combinations containing EA were also described and discussed.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
Apart from that, according to a recent survey from the _FERC_ (_Federal Energy Regulatory Commission_), the uses of _Smart Metering_ in the U.S. are more numerous: enhanced customer service; outage detection; theft detection and other line losses; outage restoration; remote connection/disconnection; power quality; asset management; outage mapping; load forecasting; remote change metering parameters; remotely upgraded firmware; price-responsive demand response; interface pre-pay; pricing/event notification; and _HAN_ applications [17]. Enhanced customer service is the most commonly used service and there is an increased use of newer types of advanced metering functionality, especially the use of advanced metering to perform remote outage management and to remotely upgrade firmware on the advanced meters. The report also highlights the significant increase of _Smart Metering_ penetration from 2006 in the United States. The following subsections describe the aforementioned features by countries in the most remarkable projects and deployments worldwide.
# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 6 Conclusions  
In urban food growing areas, the setup where a single thermal camera is used could potentially be more effective than deploying multiple sensors, especially where the growing space is limited. However, this approach may have limitations in capturing the diverse range of data typically gathered by a variety of physical sensors. While deploying a single image sensor instead of many physical sensors has the potential to reduce the maintenance cost and overall expense of plant monitoring, it is important to note that the effectiveness of a single thermal camera in accurately and comprehensively monitoring plant health has not yet been fully established given that it could be a single point of trust. The quality of data obtained from one sensor type versus multiple sensors needs further exploration to ensure that the data can be trusted, and yield maximization is not compromised.  
Given the potential to forecast other measurements for sensors that are measuring plant vital signs, continuing this research with similar experiments and with different sensors is potentially valuable. However, the scope and accuracy of these forecasts, particularly in different environmental conditions and plant types, remain to be thoroughly tested and verified.  
Future research could also benefit from a more targeted approach in the application of thermal imaging. Focusing on specific areas of thermal images and using segmentation methods to concentrate on a particular part of the image or a specific plant could potentially result in more precise sensor measurement forecasts.  
Furthermore, exploring advanced technologies like Vision Transformers (ViT) in the context of plant monitoring could potentially offer new insights into sensor measurement forecasting. Given a diverse and rich dataset, ViT has shown promising results in various fields of image analysis, but adapting it to the specific needs and challenges of environment monitoring in urban agriculture would require more research and development.  
Conceptualization, A.K. and I.P.; methodology, A.K. and I.P.; software, A.K.; validation, A.K.; investigation, A.K. and I.P.; resources, A.K.; writing--original draft preparation, A.K.; writing--review and editing, A.K.; visualization, A.K.; supervision, I.P., A.R., and A.P.; project administration, A.R. and A.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author. Data are unavailable due to privacy.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript: CNN: Convolutional neural network; MAPE: Mean Absolute Percentage Error; MAE: Mean Absolute Error; MSE: Mean Squared Error; RMSE: Root Mean Squared Error; R2: Coefficient of Determination; LSTM: Long Short-Term Memory; IoT: Internet of Things.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Feature Points  
We use OpenCV implementation of SIFT, Oriented FAST and Rotated BRIEF (ORB) [26], Speeded Up Robust Features (SURF) [31], and Binary Robust Invariant Scalable Keypoints (BRISK) [27] feature points. Whereas CS-LBP, LBPG, LC-SIFT, DE-SIFT, NG-SIFT, MN-SIFT and other descriptors are computed with publicly available source codes.
# Enhanced Modeling for Analysis of Fine Particulate Interactions with Coated Surfaces
## 1 Introduction
EDEM, notably the first modeling and simulation software developed using the DEM [16], has proven successful in various fields. Examples of its application include characterizing material movement in loader shovels [17], analyzing particle trajectories in mixers [18], and understanding the crushing process of gold-bearing ore in a jaw crusher [19]. The built-in JKR model [20] within EDEM is a cohesive contact model, taking into account the influence of van der Waals forces within the contact zone. This allows users to simulate highly adhesive systems. However, this model struggles to accurately capture the dynamics of fine particles interacting with the coating surface. One reason for this is the disparity in scale--several orders of magnitude--between the surface resolution of the coating and the size of the particles, which complicates geometry design. Additionally, EDEMTM 2022.1 software identifies contact based solely on coordinate distance [21], disregarding the surface properties of the coating. Therefore, accurately characterizing the force interactions between fine particles and coatings is crucial for analyzing the dust removal capabilities of these coatings and conducting dynamic simulations of fine particles. Drawing from the Hertz contact theory that offers a comprehensive analysis and derivation of elastic contact, this study aims to conduct an analysis of interactions between fine particles and coating surfaces. The central purpose is to develop a computational model that can accurately portray the contact between particles and coatings. Through an in-depth analysis of the coating's structural intricacies, we construct a simplified equation for estimating the pressure distribution at the interface between the particle and the coating surface. Consequently, this allows us to deduce the normal force acting upon the particle at the coating surface. The model introduced in this study, synergizing with the dynamic simulation capacities of EDEM software, serves as an efficient simulation tool for prospective dust removal tests for coatings. Moreover, it lays a foundation for simulating and evaluating surface dust removal capabilities of future Mars rovers. This paper is organized as follows. In Section 2, the theoretical model is provided. Numerical simulations and discussions are carried out in Section 3. Section 4 draws the conclusions.
## 7 Diffusion Probabilistic Models
### Conditional Score-Based Diffusion  
Conditional Score-Based Diffusion (CSDI) [5] models introduce a conditional into the reverse process of the DDPM as follows:  
\[p_{\theta}(\mathbf{x}_{0:T}^{ta}|\mathbf{x}_{0}^{co}) :=p(\mathbf{x}_{T}^{ta})\prod_{t=1}^{T}p_{\theta}(\mathbf{x}_{t-1} ^{ta}|\mathbf{x}_{t}^{ta},\mathbf{x}_{0}^{co}),\quad\mathbf{x}_{T}^{ta}\sim \mathcal{N}(\mathbf{0},\mathbf{I}), \tag{16}\] \[p_{\theta}(\mathbf{x}_{t-1}^{ta}|\mathbf{x}_{t}^{ta},\mathbf{x}_ {0}^{co}) :=\mathcal{N}(\mathbf{x}_{t-1}^{ta};\mathbf{\mu}_{\theta}(\mathbf{x }_{t}^{ta},t|\mathbf{x}_{0}^{co}),\sigma_{\theta}(\mathbf{x}_{t}^{ta},t| \mathbf{x}_{0}^{co})\mathbf{I}) \tag{17}\]  
where imputation targets \(\mathbf{x}_{0}^{ta}\in\mathcal{X}^{ta}\) are unobserved data, and \(\mathbf{x}_{0}^{co}\in\mathcal{X}^{co}\) are the observed data on which estimates of the unobserved data are made conditional. Introducing a conditional into the expression for \(\mathbf{e}_{\theta}\) in the DDPM parameterization in Equation (14), we arrive at the parameterization for CSDI:  
\[\mu_{\theta}(\mathbf{x}_{t}^{ta},t|\mathbf{x}_{0}^{co})=\frac{1}{ \alpha_{t}}\left(\mathbf{x}_{t}^{ta}-\frac{\beta_{t}}{\sqrt{1-\alpha_{t}}}e_{ \theta}(\mathbf{x}_{t}^{ta},t|\mathbf{x}_{0}^{co})\right) \tag{18}\] \[\sigma_{\theta}(\mathbf{x}_{t}^{ta},t|\mathbf{x}_{0}^{co})=\tilde {\beta}_{t}^{1/2},\quad\text{where }\tilde{\beta}_{t}=\left\{\begin{array}{ll}\frac{1- \alpha_{t-1}}{1-\alpha_{t}}\beta_{t}&t>1\\ \beta_{1}&t=1\end{array}\right. \tag{19}\]  
Which implies the corresponding CSDI loss function:  
\[\min_{\theta}L(\theta):=\min_{\theta}\mathbb{E}_{\mathbf{x}_{0}\sim q( \mathbf{x}_{0}),\mathbf{e}\sim\mathcal{N}(\mathbf{0},\mathbf{I}),t}\|\mathbf{e}-\mathbf{e }_{\theta}(\mathbf{x}_{t}^{ta},t|\mathbf{x}_{0}^{co})\|_{2}^{2} \tag{20}\]  
CSDI implements a self-supervised learning method, inspired by training in masked language models, which holds back observed data, in an amount determined by the \"missing ratio\" hyperparameter, as simulated unobserved targets with ground truth. The CSDI architecture uses an attention mechanism with multi-head and fully connected layers to identify temporal and feature dependencies. The feature dependencies are used to generate \"side information\" that is passed to the gated activation unit in each residual diffusion layer. The diffusion layers are each composed as a U-Net with skip connections all passing information to the final convolutional output layers.
## 6 Implementation  
The implementation of the decomposition in OpenQL is split into two parts: the calculation of all of the rotation angles, and the generation of the circuit. This is done so that the implementation is independent from OpenQL.  
An example of unitary decomposition in OpenQL can be found in Code Listing 1.  
For unitary decomposition in OpenQl, first a _Unitary_ object is defined, which is then decomposed to calculate all the angles for all the rotation gates. The _Unitary_ is then added to a _kernel_ as any other gate. The _kernel_ is added to a _program_, which is compiled with a _compiler_. The implementation is thus split between the _Unitary_ class and the call to _kernel_gate()_.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Number of Qubits** & **1** & **2** & **3** & **4** & **5** & \(n\) & **Section** \\ \hline Theoretical lower bounds [15] & 0 & 3 & 14 & 61 & 252 & \(\frac{1}{4}\cdot(4^{n}-3n-1)\) & Sections 5.2–5.4 \\ Givens rotations [16] & 0 & 4 & 64 & 536 & 4156 & \(\approx\)8.4 \(4^{n}\) & Section 5.5 \\ Recursive CSD [18] & 0 & 14 & 92 & 504 & 2544 & \(\frac{1}{2}\cdot n\cdot 4^{n}-\frac{1}{2}\cdot 2^{n}\) & Section 5.6 \\ Recursive CSD (optimized) [21] & 0 & 4 & 26 & 118 & 494 & \(\frac{1}{2}\cdot 4^{n}-\frac{1}{2}\cdot 2^{n}-2\) & Section 5.6 \\
**QSD**[22] & **0** & **6** & **36** & **168** & **720** & \(\frac{3}{4}\cdot 4^{n}-\frac{3}{2}\cdot 2^{n}\) & Section 5.7 \\ QSD (optimized) [22] & 0 & 3 & 20 & 100 & 444 & \(\frac{23}{48}\cdot 4^{n}-\frac{3}{2}\cdot 2^{n}+\frac{4}{3}\) & Section 5.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: CNOT counts for different implementations of unitary decomposition for one through five-qubit gates, as well as an \(n\)-qubit unitary gate.  
Figure 12: CNOT counts for different implementations of unitary decomposition for one through five-qubit gates. The chosen algorithm is shown in bold.  
```
imports fromopenqlimportopenql asql importnumpy asnp imports sys nqubits = int(sys.argv[1]) ql.set_option('output_dir',os.path.join(curdir,'output')) ql.set_option('log_level','LOG_ERROROR'); platf = ql.Platform(\"starmon\",os.path.join(curdir,'config.json')) program = ql.Program('example',platf,nqubits) kernel = ql.Kernel(\"newKernel\") compiler = ql.Compiler('compiler1') matrix = mp.load('data/out_' + str(nqubits) + \".npy\") ul = ql.Unitary(\"testname\",matrix) ul.decompose() kernel.gate(u1,range(0,nqubits)) program.add_kernel(kernel) compiler.compile(program)
```  
Listing 1: Using unitary decomposition in OpenQL.
## 4 Discussion
### The Feasibility Analysis of the IFCS for Other Large Lakes  
The feasibility of a similar scheme was analyzed in Taihu (i.e., the third largest fresh water lake in China) by [PERSON] (2010). It was found that there was only 14.6% of the total storage for flood control under the natural condition for the 1997 type flood, while the effective flood control capacity would be increased to 90% of the total storage with the new scheme. The IFCS can also be applied into other flood-prone lake areas, such as Tonle Sap Lake (Cambodian) and Poyang Lake (China). The water depth in Tonle Sap fluctuates from 1.5 to 10 m with the corresponding surface area from 2600 to 15,000 km\({}^{2}\)[24]. We assumed the minimum lake area as the inner lake (i.e., 2600 km\({}^{2}\)). After the application of IFCS into Tonle Sap Lake, the water level difference is 8.5 m (from 1.5 m to 10.0 m) in the inner lake resulting in the total flood control capacity being increased by 22.1 km\({}^{3}\) (= 8.5 m \(\times\) 2600 km\({}^{2}\)), which is 27.6% of the total storage (80 km\({}^{3}\), Yu (2019) [25]). For the Poyang Lake, the surface area ranges from 714 to 3164 km\({}^{2}\) during 2001-2010 [26]. We assumed that the surface area of the inner lake is about 64% of the maximum lake area (i.e., about 2025 km\({}^{2}\)), according to the preliminary design of the IFCS in Dongting Lake. The safe water level is 19.50 m and the highest water level recorded in history is 22.50 m (2 Aug. 1998) in Poyang Lake [27]. Hence, the water level difference is 3.0 m, with the corresponding flood control capacity of 9.49 km\({}^{3}\) (= 3.0 m \(\times\) 3164 km\({}^{2}\)). The ecological water level is 12.07 m in Poyang Lake [28], and thus the difference between the maximum water level and the ecological water level is 10.52 m in the lake. Therefore, the flood control capacity could be increased to 21.30 km\({}^{3}\) (= 10.52 m \(\times\) 2025 km\({}^{2}\)) with the IFCS, which is 2.24 times larger than that under natural condition. Based on the above analysis, the IFCS has great potential to increase the flood control capacity of the lake, and thereby to mitigate the flood disaster in the flood-prone lake area.
## 4 GNC/FDIR System Design and Analysis
### Active Safety  
This section addresses the active safety aspects. As illustrated in Figure 15, active safety complements the passively safe phases by guaranteeing that corrective actions, such Collision Avoidance Maneuvers, are taken in the event of a fault that could otherwise lead to undesirable effects, such as impact. Whereas passive safety is guaranteed for Far Range  
Figure 14: Nominal ROE trajectories for Client Phasing—blue (**a**) and Far Rendezvous—red (**b**).  
by ROEs application with the collinearity of a relative eccentricity and inclination vector, as explained in Section 4.3, the passive safety cannot be guaranteed for the Close Rendezvous phase due to the small distances between the servicer and the client spacecraft. In this phase, the FDIR functions take over and ensure no collision can occur between the two spacecraft, thus providing overall safety.  
The active safety implements the concepts of Approach and Abort corridors, described in Section 3.4, and the associated actions of Abort and Cancel. As illustrated in Figure 16, the Cancel trajectory is designed such that the boost will reach a point where operations can be resumed (left), and the sequence of Abort boosts is designed to attain a trajectory that diverges from the target (right). The solutions are designed such that they can be triggered at any point inside or outside the corridor.
# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
###### Abstract  
Phishing attacks pose significant risks to security, drawing considerable attention from both security professionals and customers. Despite extensive research, the current phishing website detection mechanisms often fail to efficiently diagnose unknown attacks due to their poor performances in the feature selection stage. Many techniques suffer from overfitting when working with huge datasets. To address this issue, we propose a feature selection strategy based on a convolutional graph network, which utilizes a dataset containing both labels and features, along with hyperparameters for a Support Vector Machine (SVM) and a graph neural network (GNN). Our technique consists of three main stages: (1) preprocessing the data by dividing them into testing and training sets, (2) constructing a graph from pairwise feature distances using the Manhattan distance and adding self-loops to nodes, and (3) implementing a GraphSAGE model with node embeddings and training the GNN by updating the node embeddings through message passing from neighbors, calculating the hinge loss, applying the softmax function, and updating weights via backpropagation. Additionally, we compute the neighborhood random walk (NRW) distance using a random walk with restart to create an adjacency matrix that captures the node relationships. The node features are ranked based on gradient significance to select the top k features, and the SVM is trained using the selected features, with the hyperparameters tuned through cross-validation. We evaluated our model on a test set, calculating the performance metrics and validating the effectiveness of the PhishGNN dataset. Our model achieved a precision of 90.78%, an F1-score of 93.79%, a recall of 97%, and an accuracy of 93.53%, outperforming the existing techniques.  
deep learning; feature extraction; feature selection; website phishing detection 202517331  
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