esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Multiple-Organ Segmentation Method Using Multiple U-Nets in PET/CT Images
## 1 Introduction  
Cancer is a major cause of death in many countries and an important obstacle in extending life expectancy. According to the World Health Organization (WHO), cancer is the first or second leading cause of death in people under the age of 70 years in 112 of 183 countries [1].  
In today's medical care, various diagnostic imaging examinations, such as computed tomography (CT) and magnetic resonance imaging (MRI), are used for the early detection and treatment of cancer. Notably, PET/CT examinations that scan low-dose computed tomography (LDCT) and positron emission tomography (PET) play important roles in cancer diagnosis. Specifically, LDCT images provide morphological information, such as the shape and location of organs, whereas PET images provide functional information, such as glucose metabolism in organs. \({}^{18}\)F-Fluorodeoxyglucose (FDG), mainly used in PET/CT examinations, is a radioactive tracer that acts as a glucose analog to localize tissues with altered glucose metabolism. Importantly, the amount of FDG accumulated in each organ indicates the function of glucose metabolism, and the standard uptake value(SUV) is widely used as a semiquantitative index of glucose metabolism in clinical settings. Furthermore, fusion images created from LDCT and PET images can be used to identify the exact anatomical location of abnormal FDG uptake observed on PET images.  
Based on the morphological and functional information obtained from LDCT, PET, and fusion images, PET/CT examinations can contribute to the diagnosis of cancer and other diseases. However, PET/CT examinations require the use of a large number of medical images in a single examination, which places a heavy burden on radiologists during diagnosis. This has led to the development of computer-aided diagnosis (CAD) and other image diagnosis-assisted technologies that use artificial intelligence (AI) to reduce the burden on radiologists.  
In recent years, with the development of computer technology, CAD methods for various modalities have been actively studied [2; 3; 4; 5]; however, few of them have been developed for PET/CT examinations because PET/CT examinations target the whole body and the different patterns of FDG uptake in each organ require different algorithms for detecting and analyzing lesions in different organs. To develop such an algorithm, it is necessary to recognize each organ region. Therefore, we developed an automated organ segmentation technique for PET/CT images as a fundamental technology for developing CAD for each organ.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 4 Results and Analysis of Experiment
### Results and Evaluation
Based on the measurement of the rate of data transfer, the proposed method in the second experiment has the same behavior as the previous parameters for the majority of flows, where the performance becomes better than it is in the first experiment, although it may be slightly less than the performance of the controller sometimes. As a result, the proposed approach is more efficient than the Ryu controller and proves its ability to find the best route according to the flow type.
# Fibers Effects on Contract Turbulence Using a Coupling Euler Model
## 5 Conclusions  
In order to understand the effect of fiber concentration on the average flow and fiber orientation distribution in the turbulent contraction, a coupling Euler model was used for numerical simulation. In view of the relative independence of displacement and angle space calculation, a semi-discrete approximation form is adopted to simplify the Fokker-Plank type function of fiber orientation angle. At the same time, the explicit fourth-order Runge-Kutta method is introduced to greatly reduce the iterative calculation cost. The velocity profile and fiber orientation distribution of suspension flow can be obtained under different fiber concentrations based on the Reynolds stress model solver of Fluent software.  
Due to the local tensile effect in the turbulent contraction whose contract ratio \(C_{x}>4\), the higher fibers concentration is, the easier it is to move in clusters and earlier align with the flow direction when the fiber concentration exceeds the semi-dilute phase. Fibers in near semi-dilute suspension have more orientations than those in the dilute suspensions because there is hydraulic disturbance and lubrication within the range of fiber length for semi-dilute suspension.  
From the mechanism of two-way coupling RSM, the fiber's concentration mainly affects the additional stress of suspension through the additional viscosity term, and then affects the orientation diffusion of the fibers through the flow field, and finally determines the orientation distribution of fibers. Hence the addition of fibers changes the viscosity of the flow field and affects the momentum and mass transport mechanism of the suspension flow. The results of a two-way coupling simulation are basically consistent with the theory and functions of the model here, which can provide a process strategy for fiber orientation optimization and rheological control in the industrial applications of suspension such as paper making and textile.  
Conceptualization, W.Y.; methodology, W.Y.; software, W.Y.; validation, W.Y. and P.H.; resources, W.Y. and P.H.; writing, review and editing, W.Y.; visualization, P.H.; project administration, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 11302110), the Public Project of Science and Technology Department of Zhejiang Province (Grant No. 2015C31152) and Wang Kuancheng Education Foundation Project.  
Not applicable.  
The authors declare no conflict of interest.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
###### Abstract  
This paper presents a novel method of calculating desynchronization between transmitters working in a single frequency digital video broadcasting-terrestrial (DVB-T) network. The described method can be a useful tool for enhancing passive radar operations and improving passive coherent location (PCL) sensors to correct their measurements of target localization. The paper presents the problem of localizing DVB-T transmitters utilized by passive radars, and proposes a novel method based on Time Difference of Arrival (TDoA) techniques to solve the problem. The proposed technique has been validated using real signals collected by a PCL sensor receiver. The details of the experiment and extensive result analysis are also contained in this article.  
passive radar; TDoA; SFN; DVB-T +
Footnote †: journal: Sensors
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 5 Intrusion Detection in Fog, Edge, and Cloud Computing
### Fog Computing
#### 5.1.2 Advantages of Fog Computing  
Deploying Fog Computing as a measure for strategic system innovation can be seen to have an array of advantages for a business. Some of the most significant advantages can be observed as:  
* Data storage in fog computing will be able to minimize any delays in the transmission of data;  
Figure 3: Fog Computing Architecture.  
* Allowing to efficiently process and analyze the data for many applications (Industrial IoT Smart Cities);
* Providing interactions that are essential between the end-devices and cloud computing servers;
* Deploying a distributed network on a global scale that aids in reducing network downtime;
* Supporting service in real time and providing a reduced state of network latency.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 4 MIMT-CNN Modeling
### Network Structure
#### 4.1.1 Feature Extraction Layer  
The three input channels input red-green-blue images with the size of 100 \(\times\) 100 \(\times\) 3, respectively. These images are divided by 255, so that each pixel value is normalized to [0, 1]. Then, the signal in each channel enters the image feature extraction layer to extract features. Since the amount of input data is very large, it is not suitable to load all the data at once for gradient calculation and weight updating. Therefore, the input data are divided into small batches, which makes the network obtain a greater generalization ability.  
Each channel contains two original feature extraction modules, which both consist of two convolution layers with the size of 3 \(\times\) 3, two ReLU layers, and a maximum pooling layer with the size of 2 \(\times\) 2. Through the layer-by-layer convolution, the important features are continuously strengthened, while the unimportant features are gradually weakened.  
Figure 6: Network structure of MIMT-CNN.  
Besides, the nonlinear activation function ReLU is used for the multi-input and multi-task output, with the advantages of simple form, short calculation time, as well as the prevention of gradient disappearance and overfitting [25].  
The Maximum pooling [26] layer is periodically placed between the convolutional layers of the feature extraction layer, which extracts the maximum feature in the local receptive field of the feature map, thereby reducing the computational complexity, preventing network over-fitting, and improving the generalization ability of the model. Due to the existence of the pooling layer, the feature dimension of each channel is reduced from 100 \(\times\) 100 \(\times\) 3 to 22 \(\times\) 22 \(\times\) 3 after the feature extraction layer.
# State-of-the-Art DC-DC Converters for Satellite Applications: A Comprehensive Review
## 3 Review of DC-DC Converters for Satellites
### Isolated Topologies
#### 3.2.3 Current-Fed Weinberg Converter  
The benefits of using the current-fed Weinberg topology for satellite applications were investigated in [96; 97]. Compared to the full-bridge converter, this converter is shoot-through tolerant, and the input voltage spans a broad range without compromising efficiency. However, its disadvantage lies in the high stresses on the semiconductor devices. The circuit topology is shown in Figure 13b.
## 4 Numerical Simulation Analysis
### Stress Evolution Analysis  
The condition when the normal load is applied without shearing is referred to as the initial state. The states of the crack at the rock bridge after germination, extension, and penetration are named the germination stage, extension stage, and penetration stage, respectively. These four states are important nodes in the process of non-persistent joint shear failure and can provide critical information for analyzing the stress evolution process. Figure 12 illustrates the progression of stress evolution. The stress distribution varies at different stages. Take 30-1.0-2 in Figure 12a as an example. In the initial stage, the stress value at the joint ends is the highest. In the germination stage, cracks appear at the joint ends and the nearby rock bridge. The maximum stress value in the model increases, and the joints and cracks in the numerical model are areas of high stress concentration. In the extension stage, the maximum stress value in the model decreases, and the area near the joint ends and the cracks extension are areas of high stress concentration. In the penetration state, the maximum stress value in the model continues to decrease. The rough contact of the shear surfaces becomes an area of stress concentration.  
We conducted a further analysis of the influence of variables on the stress evolution process. Figure 12a shows that as the joint aperture increases from 0.5 to 2, the maximum stress values in the initial, germination, and extension stages all rise. In addition, as the joint aperture grows, the stress diffusion range gradually decreases in the germination and extension stages, and the higher stress gradually concentrates on the rock bridge. This law also exists with the increase of joint persistency (Figure 12b) and normal stress (Figure 12c).  
Figure 11: Comparison of the results of numerical simulations and direct shear tests for non-persistent joints. (**a**) 30-0.5-2; (**b**) 30-1.0-2; (**c**) 30-2.0-2; (**d**) 20-1.0-2; (**e**) 40-1.0-2; (**f**) 30-1.0-1; (**g**) 30-1.0-3.  
Nevertheless, the rock bridge remains intact primarily in the initial, germination, and extension stages. This shows that before the rock bridge develops a through crack, the stress concentration inside the specimen increases as the joint aperture, joint persistency, and normal stress increase. Additionally, the stress is more concentrated near the rock bridge, which makes it easier for cracks to spread along the rock rather than extend away from it. This explains the failure patterns of the specimens. As the three variables increase, the failure pattern changes from tensile failure to shear failure, and the width of the failure zone decreases.  
Figure 12: Cont.  
In the penetration stage, the rock bridge has been destroyed, and the stress concentration area is located at the contact point of the shear surface. The stress value is greatly affected by the roughness of the shear surface and the normal stress. It can be seen from Figure 12 that the maximum stress value diminishes with the increase of joint aperture and joint persistency and grows with the increase of normal stress.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 3 Methods
### A Method of Automatic Threshold Determination  
[PERSON]'s method is an efficient and widely used image binarization algorithm that utilizes the maximum variance between classes to distinguish objects and background. That  
Figure 2: Spectral curves of different land cover and water types. (**a**) The spectral curves of various land cover types and (**b**) the spectral curves of various water types.  
is, when the value of the target water body is quite different from that of the background, the water body and the nonwater area can be easily distinguished. The algorithm has been widely used for the automatic threshold extraction of water bodies [13].  
The principle of the algorithm is as follows: It is assumed that image \(I\) is classified according to the gray level \(\{1,2,3,\ldots,M\}\) and the image is divided into thresholds. Then, the probability for each gray level \(i\) is determined as follows:  
\[P_{i}=\frac{n_{i}}{N}\ \ (P_{i}\ \geq 0) \tag{4}\]  
where \(N\) denotes the total number of image pixels and \(n_{i}\) denotes the number of pixels corresponding to gray level \(i\). The probabilities of \(C_{0}\) and \(C_{1}\) pixels in the entire image are \(\omega_{0}\) and \(\omega_{1}\), respectively, and the average gray values are \(\mu_{0}\) and \(\mu_{1}\), respectively.  
\[\omega_{0}=\sum_{i=1}^{T}P_{i} \tag{5}\]  
\[\omega_{1}=\sum_{i=T+1}^{M}P_{i} \tag{6}\]  
\[\mu_{0}=\sum_{i=1}^{T}\frac{iP_{i}}{\omega_{0}} \tag{7}\]  
\[\mu_{1}=\sum_{i=T+1}^{M}\frac{iP_{i}}{\omega_{1}} \tag{8}\]  
The total average gray level of the whole image is \(U\).  
\[U=\omega_{0}*\mu_{0}+\omega_{1}*\mu_{1} \tag{9}\]  
The interclass variance (Y) of \(C_{0}\) and \(C_{1}\) is as follows.  
\[\mathrm{Y}=\omega_{0}(\mu_{0}-U)^{2}+\omega_{1}(\mu_{1}-U)^{2} \tag{10}\]  
The maximum threshold of the interclass variance \(\mathrm{Y}\) is the best threshold \(\Re_{good}\) in the study area, as is described as follows.  
\[\Re_{good}=\mathrm{argmax}(\mathrm{Y}) \tag{11}\]  
Water body \(W\) was detected by the best threshold \(\Re_{good}\) and it was extracted according to the following rules.  
\[W=\left\{\begin{array}{ll}1&i\geq\Re_{good}\\ 0&i<\Re_{good}\end{array}\right. \tag{12}\]  
\(W=1\) denotes a water body and \(W=0\) denotes a nonwater body.
## 2 Analysis of DC Arc Fault Characteristics of PV System  
### DC Arc Fault Test Platform for PV System  
Since the DC arc faults in PV systems occur randomly on site, which are difficult to be captured or reproduced, a specific test platform is required to generate arc faultsunder different operating conditions. As per the UL1699B standard, a DC arc fault test platform is built, including PV arrays/simulators, arc generators, and PV inverters, as shown in Figure 1. A PV array with two strings in parallel is used for the tests, which will make the data collection more convincing. The maximum output power for each string is 10 kW. Meanwhile, a Chroma brand PV simulator with a rated power of 15 kW is also applied to make the tests more convenient and diversified, capable of simulating different I-V curves under various weather conditions such as irradiation, temperature, rain, and shaded by trees or clouds. At the same time, the potential influence of parasitic capacitance and parasitic inductance of DC transmission line on arc current detection should be considered, and impedance networks are added in the middle of photovoltaic strings. The resistance and capacitance parameters of the impedance network are shown in Table 1. An arc generator is integrated with the system to produce series arc faults, which are usually caused by loose connections of metal terminals.  
According to the requirement of UL1699B, the arc fault detection device (AFDD) for PV systems can be categorized into two types, including the embedded type and the external type. The embedded type AFDD can be integrated with a PV inverter, combiner box, or DC-DC converter, which means that AFDD is usually used for a specific PV inverter. On the contrary, the external type AFDD is more generalized in the application, and it has to be adapted to different brands of PV inverters. Therefore, we are working on the external type AFDD, and three different inverters are applied for this research, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Parameter Value** \\ \hline C1 & 20 \(\mu\)F \\ C2, C3 & 22 nF \\ L1, L2 & 25 \(\mu\)H \\ R1, R2 & 1 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impedance network parameter setting.  
Figure 1: DC arc test platform.  
In addition, the arc generator comprises a pair of stationary and moving electrodes, which can be driven by a stepper motor to move separately at a constant speed of 5 mm/s to initiate arcs. The electrodes are made of copper bars with a diameter of 6.35 mm (1/4 inch) according to UL 1699B. Figure 2 shows a close-up view of the arc generator. While Table 2 shows the maximum air gaps of the electrodes under different arc currents.
# Kerr-Lens Mode-Locking: Numerical Simulation of the Spatio-Temporal Dynamics on All Time Scales
## 1 Introduction  
Kerr-lens mode-locking (KLM) is a state-of-the-art technique for generating ultrafast pulses, central to the field of ultrafast lasers and is extensively used [1; 2; 3]. KLM relies on the highly non-linear interplay between the spatial and temporal properties of the field inside the laser cavity that is challenging to fully analyze [4; 5; 6] and, in many cases, requires approximations that exclude some of the intricacies of these oscillators. As a result, the design of KLM-based oscillators often relies strongly on intuition and trial-error experience. Most standard methods of analysis focus on the steady-state of the field [2; 7; 8; 9; 10; 11; 12; 13], but cannot describe the dynamical evolution towards this steady state. Some other methods make different approximations to separate the spatial and temporal parts of the dynamics [14], which is effective for understanding the basic operation of KLM lasers, but can miss important dynamics in the interplay between the spatial and temporal profiles of the field [15].  
We introduce a numerical simulation algorithm, along with an open-source MATLAB program that calculates from first principles the complete dynamics of the spatio-temporal field profile in a KLM oscillator for a wide range of operation regimes. Our method is able to reproduce the evolution of the field in both space and time--starting from an initial noise-seed up to steady-state pulses. The only assumption being made is that the spatial beam of the laser is a single mode, well approximated by a Gaussian profile. This is a practically universal regime of operation in KLM lasers that are strongly driven to a single spatial mode by the interaction between the self-focusing Kerr-lensing and the diffraction through the effective aperture in the cavity [2; 3].  
Our paper is structured as follows: Section 2 includes the major validations, where the simulation reconstructs fundamental properties of KLM oscillators--soliton formation from spontaneous noise and its dependence on the laser parameters, such as gain bandwidth, gain saturation, self-phase modulation (SPM), linear and nonlinear dispersion, all from first principles. This section also shows how different pulse properties depend delicately on the spatial cavity parameters and how we can use the full simulation of a KLM laser to assist cavity design. Section 3 provides a detailed description of the paradigm of our numerical simulation and the algorithm.
###### Abstract  
This paper reports the latest update to and a 24 h continuous operation test of the CEU's thermochemical iodine-sulfur cycle water-splitting system with a maximum H\({}_{2}\) hydrogen production capacity of 1500 L/h. To address challenges such as high energy consumption and severe corrosion in traditional processes, the system was updated and optimized by introducing a small-cycle design, simulated using Aspen Plus software, achieving a thermal efficiency of 53%. Specifically, the key equipment improvements included a three-stage H\({}_{2}\)SO\({}_{4}\) decomposition reactor and an HI decomposition reactor with heat recovery, resolving issues of severe corrosion when H\({}_{2}\)SO\({}_{4}\) boils and reducing heat loss. During 24 h continuous operation in January 2025, the system achieved a peak hydrogen production rate of 1536 L/h and a long-term stable rate of approximately 300 L/h, with hydrogen purity reaching up to 98.75%. This study validates the potential for the scaling up of iodine-sulfur cycle hydrogen production technology, providing engineering insights for efficient and clean hydrogen energy production.  
hydrogen production; iodine-sulfur cycle; pilot-scale facility; continuous operation; CFD simulation Article
## 3 System Architecture
### Detailed System Implementation
#### 3.4.4 Sparse Region Handling and Computational Analysis  
The system implements hierarchical density-based clustering to handle sparse peripheral regions where the listing density is 10\(\times\) lower than central areas:  
Three-Phase Sparse Region Strategy:  
Initial DBSCAN with adaptive eps (5 th percentile nearest neighbor distance).  
Sparse region detection (<5 listings/km\({}^{2}\)) using sliding window analysis.  
Hierarchical merging of adjacent sparse clusters within a 500 m radius.  
Following advanced mechanisms, as described in previous studies [12], we have implemented the following:  
* Spatial partitioning: a 4-partition grid for our city-scale dataset.
* Optimized processing: single-thread.
* R-tree indexing: O (log n) point-in-polygon checks **vs.** O (n) naive.
* Boundary caching: recomputation only when density changes > 15%.  
Performance metrics: 45 ms (22.5 us/point), 176 bytes/listing memory footprint, maintaining a 96% assignment rate in sparse zones versus 71% with fixed boundaries.