esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Simulation Validation of Proposed Method
Figure 14: The ANC simulation results of trapezoidal enclosed space, (**a**) SPL without ANC for Obstacle-free trapezoidal enclosed space, (**b**) SPL with ANC for Obstacle-free trapezoidal enclosed space, (**c**) SPL without ANC for Trapezoidal Enclosed Space with Obstacle (**d**) SPL with ANC for Trapezoidal Enclosed Space with Obstacle. In future research, we will focus on algorithm research of ANC for a more irregular space and a more complex environment.
# Offloading of Atomic Tasks in Satellite Networks:
## 4 Method  
To achieve the overall goal of time consumed by the execution of all tasks is minimized, we further consider the actual task planning process. When faced with multimedia and new space tasks, satellites will receive many computational tasks, which are broken down into more atomic tasks. The existing satellite computing power cannot cover all the atomic tasks. Therefore, it is necessary to offload some computational tasks to high-orbiting satellites with solid computational capabilities to reduce the single-satellite computational load, improve the efficiency of mission execution, and speed up the response time to computational tasks.
# Meta-Learning Approach for Adaptive Anomaly Detection from Multi-Scenario Video Surveillance
## 2 Related Works
### General Deep Learning Methods  
Over the years, many supervised deep learning methods, including convolutional neural networks (CNNs), recurrent networks, and transformers, have shown strong performance in computer vision tasks. However, their effectiveness depends on access to large, labeled datasets, which are often costly and time-consuming to create, especially in multi-scenario surveillance systems with limited anomalous data.  
[PERSON] et al. 2025 [4] introduced the work that exemplifies how augmentation strategies can enhance performance when labeled data is scarce. While such techniques help, they cannot fully eliminate the dependency on annotated datasets, underscoring the need for more adaptive learning frameworks.  
[PERSON] et al. 2025 [5] investigated the effectiveness of multiple image augmentation techniques--such as brightness, contrast, and rotation--on non-PPE detection in construction site images using the YOLOv8 model. Their findings showed that well-chosen augmentations can significantly enhance detection accuracy, especially for classes with limited data. Their study focused on static image classification.  
[PERSON] et al. 2016 [6] focused on meta-learning techniques that address this by enabling models to learn from prior tasks and adapt quickly to new ones. Memory-Augmented Neural Networks (MANNs), especially Neural Turing Machines, provided external memory mechanisms that support rapid learning from a few examples. They also demonstrated that MANNs outperform LSTMs in both classification and regression tasks.  
[PERSON], [PERSON] et al. 2017 [2] concluded that MAML is a powerful and versatile meta-learning approach that allows models to adapt quickly to new tasks with minimal data. Its model-agnostic nature makes it applicable across various domains and tasks, addressing key limitations of traditional machine learning methods in dynamic environments.  
[PERSON] et al. 2017 [7] studied and introduced an LSTM-based meta-learner that learns an optimization strategy tailored for few-shot scenarios by efficiently updating a learner model's parameters.  
[PERSON] et al. 2017 [8] introduced Meta Networks (MetaNet), a novel meta-learning framework designed to enhance rapid generalization and continual learning in neural networks, particularly in scenarios with limited labeled data.  
[PERSON] et al. 2023 [9] focused on the head pose estimation techniques, in which they compared MAML and FO-MAML methods for efficiency. FO-MAML uses first-order approximation, avoiding Hessian computation. Experiments were conducted in one-shot, five-shot, and ten-shot settings. FO-MAML showed faster optimization than traditional MAML.
## 4 Discussion
### Helical Flow  
Previous studies have demonstrated that the complex morphology and flow condition in the aorta lead to right-handed helical flow in AAo as well as left-handed helical flow in DAo among healthy subjects [14; 16; 32; 88; 89]. [PERSON] et al. further investigated the development of helical flow in the aorta during the systolic phase by using in vivo four-dimensional phase contrast magnetic resonance imaging (4D PC-MRI), and concluded that the helical flow pattern in the aorta could optimize the blood flow in the aorta by avoiding excessive energy dissipation and reducing flow instabilities [14; 15]. In addition, the helical flow also affects the aortic mass transportation. Several studies on the low-density lipoprotein (LDL) transportation in the aorta reported that the helical flow environment would reduce the luminal surface LDL concentration in the aortic arch [90; 91]. Meanwhile, enhanced oxygen transportation as well as suppressed activation of blood components have been associated with the helical flow in arteries [18; 92; 93]. Such phenomena indicate that the physiological helical flow plays an important role in protecting the aorta form atherogenesis and thrombosis [16]. However, abnormally high grades of helical flow could elevate the potential risk of aortic wall remodeling and lead to aortic dilation [94]. Clinical evidence showed that the aortic insufficient and eccentric jet caused by a non-physiological aortic valve structure are the major causes of high helix grade [19; 88; 89]. Theresult from this study showed that in all of the three cases, right-handed helical flows appeared in the ascending and aortic arch at the very early stage of the acceleration phase and dominated the entire aorta at the systolic peak. These flow patterns agree well with the previous clinical observations of aorta flow in healthy subjects. Even though the helix grades of all cases are within the normal range at the acceleration peak (1.1 \(\pm\) 0.1 to 2.03 \(\pm\) 0.67) [32; 88], the SPAC tubular valve presents a 16.7% higher helix than that of the rest of the cases due to its smaller EOA. In addition to the higher helix grade, the smaller EOA of the SPAC molded valve also caused higher V\({}_{\text{mean}}\) and V\({}_{\text{max}}\) in the ascending aorta at the systolic peak. Also noted is that highly disturbed inter-sinus flow existed in the both SPAC cases, which is not observed in the conventional valve. This phenomenon is mainly due to the non-isolated sinuses of the SPAC approaches, and the distal flow in the aorta was not affected by this localized disturbance.
# Experimental Performance Analysis of Wi-SUN Channel Modelling Applied to Smart Grid Applications
## 2 AMI Architecture and Last-Mile Communication
### Wi-SUN (IEEE 802.15.4g)  
IEEE 802.15.4g is the amendment of the IEEE 802.15.4-2012 (Zigbee) standard's PHY layer, which is very sensitive to external interferences and multi-path fading [16]. The same range of frequencies (i.e., 2.4 GHz) in nearby wireless networks, such as IEEE 802.11 (WIFI) and IEEE 802.15.1 (Bluetooth), also generate interference to Zigbee. The multipath fading occurs when the signals are reflected off some obstacle in the environment resulting in multiple signals from different paths and time delays. Multi-path signals are combined at the receiver's front end resulting in a reduction of the quality of communication due to the inter-symbol interference (ISI). Due to the interference and fading, the Zigbee communication range is limited to 100-200 m in outdoor environments; therefore, relay nodes are required for range expansion, which is a tradeoff between the extended network coverage and cost. In addition, IEEE 802.15.4g has adopted an alternate PHY layer to overcome the Zigbee's limitation, by which the sub-GHz frequency bands and additional modulation schemes are introduced.
# Image Restoration Using a Fixed-Point Method for a TVL2 Regularization Problem
## 5 Split Bregman Methods for TVL2 Problems (3) and (4)  
In this section, we just provide the alternating split Bregman methods for solving the TVL2 problems (3) and (4) in order to evaluate the performance of Algorithms 2 and 3. For more details about the alternating split Bregman method as well as its convergence analysis, we refer to [5; 8].  
The following Algorithms 4 and 5 are the alternating split Bregman methods corresponding to the TVL2 problems (3) and (4), respectively.  
As was done in line 7 of Algorithm 3, the linear systems in line 4 of Algorithms 4 and 5 are solved using the CGLS instead of using the CG.  
```
1:Given : observed image \(f\), positive parameters \(\alpha,\ \beta,\ \lambda\)
2:Initialization : \(a^{0}=0,b^{0}=0\) and \(u^{0}=f\)
3:for\(k=0\) to maxit do
4: Solve \((A^{T}A+\lambda B^{T}B+\alpha I)u^{k+1}=A^{T}f+\lambda B^{T}(a^{k}-b^{k})\) for \(u^{k+1}\)
5:\(a^{k+1}=\text{prox}_{\frac{\alpha}{k}}(Bu^{k+1}+b^{k})\)
6:\(b^{k+1}=b^{k}+\frac{\alpha}{k}Bu^{k+1}-a^{k+1}\)
7:if\(\frac{\left\|u^{k+1}-u^{k}\right\|_{2}}{\left\|u^{k+1}\right\|_{2}}<tol\)then
8: Stop
9:endif
10:endfor
```  
**Algorithm 4** Split Bregman method for TVL2 problem (3)
## 2 Methodology  
### Blade-Element Momentum Theory  
The principle of the blade element momentum theory [13] is based on the conservation of the momentum of the air flowing through the rotor disk between the local force generated by the empirical lift coefficient and the empirical drag coefficient at the airfoil. The basic idea of blade element momentum theory is to simplify the wind turbine blade into several blade element units of length \(\mathrm{d}r\) in the span direction, and assuming that there is no interaction between the micro-segments of the blade element unit, the force on each blade can be approximated by the superposition of the spanwise integrals on each blade element, at the position \(r\) between the rotor and the hub center, the thrust and torque of the blade element unit on the rotor are:  
\[\mathrm{d}T=N\mathrm{d}F_{X}=0.5\rho V_{0}^{2}NCC_{X}\mathrm{d}r \tag{1}\]  
\[\mathrm{d}M=Nr\mathrm{d}F_{Y}=0.5\rho V_{0}^{2}NrCC_{Y}\mathrm{d}r \tag{2}\]  
\[C_{X}=C_{L}\cos\phi+C_{D}\sin\phi \tag{3}\]  
\[C_{Y}=C_{L}\sin\phi-C_{D}\cos\phi \tag{4}\]where \(v_{0}\) is the relative velocity on the blade, \(C\) is the chord length of blade element, \(C_{L}\) is the lift coefficient, \(C_{D}\) is the resistance coefficient; d\({}_{r}\) represents the length of blade element; \(N\) is the number of blades.
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 3 Advanced LSFP
### Lsfp
#### 3.1.1 Downlink LSFP
It means that the improved LSFP precoding scheme can more effectively improve the anti-contamination and anti-interference performance over conventional LSFP as the number of antennas at a base station antennas stays unchanged.
## 4 Dynamic Simulation and Experimental Verification
### Effect of the Relative Position of the Faulty Teeth on Dynamic Characteristics of Gears  
Based on the simulated and experimental parameters in Section 4.1, in this paper, we assume that the number of healthy teeth between two spalling faulty teeth is \(H\). Three relative positions (\(H\) = 0, \(H\) = 1, and \(H\) = 2) of the two faulty teeth are considered. Figure 13a, Figure 14a, and Figure 15a show the failure states of tooth surface spalling in three relative positions. Furthermore, when the two spalling fault teeth are meshed separately, the mesh stiffness decreases, and the interval time of the reductions is related to the number of healthy teeth between the two spalling fault teeth, as shown in Figures 14b and 15b.  
Figure 14: Status of tooth surface and mesh stiffness curves (two teeth with spalling fault, which are separated by two healthy teeth): (**a**) status of tooth surface and (**b**) mesh stiffness curves.  
Figure 13: Status of tooth surface and mesh stiffness curves (two teeth with spalling fault, which are separated by one healthy tooth): (**a**) status of tooth surface and (**b**) mesh stiffness curves.  
The simulated and experimental signals both in time and frequency domain at the three relative positions of the double faulty teeth are presented in Figures 12, 15 and 16. As shown in Figures 12a, 15a and 16a, impulse vibration excited twice in one rotational period of the pinion. In addition, the distance between two impulse vibrations are 0.0025 s, 0.005 s, and 0.0075 s, which can be expressed by \((H+1)t_{m}\). However, the impulse vibration is not clear in Figures 12c, 15c and 16c. One possible explanation is that these features are submerged by the background noise in the experimental signal.  
Figure 16: The simulated and experimental signal in the meshing line direction (two teeth with spalling fault, which are separated by two healthy teeth): (**a**) simulated signal, (**b**) simulated frequency signal, (**c**) experimental signal, and (**d**) experimental frequency signal.  
Figure 15: The simulated and experimental signal in the meshing line direction (two teeth with spalling fault, which are separated by one healthy tooth): (**a**) simulated signal, (**b**) simulated frequency signal, (**c**) experimental signal, and (**d**) experimental frequency signal.  
Moreover, as presented in Figures 12d, 15d and 16d, the maximum amplitudes of the sidebands at the three relative positions are 0.0019 m/s\({}^{2}\), 0.0015 m/s\({}^{2}\), and 0.0011 m/s\({}^{2}\), respectively. Among them, the amplitude of the sidebands at the adjacent positions is the highest, which is close to the simulated results. It can be concluded that, when the two teeth with spalling fault are adjacent, the features of the spalling fault are the most clear. In addition, compared with Figures 15b and 15d, as well as Figures 16b and 16d, the strong background noise in the experimental signal also affects the identification of fault features, and then affects the accuracy of the verification results of the dynamic model and experimental signal.
# Experimental Study on Flexural Properties of Polyurethane-Cement Composites under Temperature Load
## 3 Synthesis Mechanism of Polyurethane Cement Composites
### Chemical Structure and Reaction Mechanism of Polyurethane
#### 3.1.3 Polyurethane Microphase Separation Structure  
Polyurethane consists of hard and soft segments as a block copolymer, as shown in Figure 7. The soft segment is mainly composed of polyester polyol or polyether polyol, which provides the flexibility and elasticity of the cross-linked polyurethane network. The hard segment is composed of isocyanate and chain extender, which mainly affects the mechanical properties of polyurethane, including compressive strength, flexural strength, and elastic modulus. There are significant hydrogen-bonding interactions between the urea and carbamate groups in the hard chain segments, leading to the formation of aggregates in the hard chain segments, which are dispersed in the soft chain segment region.
## 2 Materials and Methods
### Skin Permeation Tests  
The permeation experiments were performed in Franz diffusion cells (Phoenix DB-6, ABL&E-JASCO, Wien, Austria) with diffusion area of 1 cm\({}^{2}\) each. The volume of the receptor chamber was 10 cm\({}^{3}\), and it was filled with PBS solution (pH 7.4). Each diffusion unit was kept at the constant temperature of 37.0 \(\pm\) 0.5 \({}^{\circ}\)C [20]. The receptor chamber fluid was stirred with a magnetic stirring bar at the same speed (350 rpm) for all cells. The skin was mounted on the donor chamber. Skin integrity tests were performed as described in Section 2.2. Only undamaged skin samples with an even thickness were chosen for tests. The permeation tests were carried out for 24 h. The tested topical compositions of 1 g, were uniformly applied to the diffusion area of the skin in each donor chamber. At scheduled time intervals (0.5 h, 1 h, 2 h, 3 h, 4 h, 5 h, 8 h, and 24 h), portions of the receptor fluid (0.5 mL) were withdrawn for determination of naproxen concentration by HPLC method. The cumulative mass of the permeated naproxen (in \(\mu\)g NAP-cm\({}^{-2}\)) was calculated based on this concentration. The receptor fluid was replenished with a fresh PBS solution (0.5 mL) after each sampling. The measurements in the Franz diffusion chamber were performed in triplicate for each topical composition.  
The profiles of in vitro percutaneous absorption for infinite dose of drug are most often characterized by [PERSON]'s laws of diffusion [21; 22]:  
\[\mathrm{Jss}=\mathrm{dQ/Sdt} \tag{1}\]  
where Jss is the steady-state flux in \(\mu\)g NAP cm\({}^{-2}\) h\({}^{-1}\); dQ is the change in the quantity of permeated NAP (in \(\mu\)g NAP) through the skin to the receptor chamber in time interval dt; S is the active diffusion area in cm\({}^{2}\).  
The steady state flux of naproxen through the human skin into receptor fluid was determined as the slope of the linear portion of the plot of cumulative mass of NAP (Q) in the receptor fluid versus diffusion time (t).
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 2 Background and Related Works
### Related Works
As an extension component of the SDN controller, DoSDedender [16] defended against DoS attacks by maintaining the mapping relationship between switches and associated hosts. This solution uses a threshold-based approach to detect attacks that falsify source ports, easily leading to false positives. This paper focuses on detecting and mitigating the packet injection exploiting attack. We will introduce it in detail in later sections.
# A New Kinematic Model of Portable Articulated Coordinate Measuring Machine
## 2 Model
### Kinematic Model Based on Generalized Geometric Error Theory  
There are two steps for establishing the kinematic model of PACMM. Firstly, each joint variable of PACMM is nominally equal to zero at the initial state. In other words, this state is also called the zero pose. Secondly, the kinematic model of PACMM would be established by the generalized geometric error matrix at the initial state. In Figure 3, the homogeneous matrix \(A_{i}\) is obtained by the coordinate frame \(O_{i-1}^{\mathbb{R}}X_{i-1}^{\mathbb{R}}Y_{i-1}^{\mathbb{R}}Z_{i-1}^{\mathbb{ R}}\) transformed to \(O_{i}^{\mathbb{R}}X_{i}^{\mathbb{R}}Y_{i}^{\mathbb{R}}Z_{i}^{\mathbb{R}}\), and the matrix \(E_{i}\) represents the coordinate frame \(O_{i}^{\mathbb{R}}X_{i}^{\mathbb{R}}Y_{i}^{\mathbb{R}}Z_{i}^{\mathbb{R}}\) slightly deviating from the ideal frame \(O_{i}^{\mathbb{R}}X_{i}^{\mathbb{R}}Y_{i}^{\mathbb{R}}Z_{i}^{\mathbb{R}}\). When \(i=0,7\) the corresponding frames are the base frame and the center of the rigid probe of PACMM, respectively. It is worth noting that the coordinate frame is not directly established by the D-H model method because there is not the rotation joint at the coordinate frame \(O_{6}^{\mathbb{R}}X_{6}^{\mathbb{R}}Y_{6}^{\mathbb{R}}Z_  
initial zero position. Then, the actual joint variable is indicated by \(\Theta_{i}=\theta_{i0}+\theta_{i}\). The transformation matrix \(A_{i}\) is rewritten as the Equation (5):  
\[A_{i}=\left[\begin{array}{cccc}cos\Theta_{i}&-sin\Theta_{i}cos\alpha_{i}&sin \Theta_{i}sin\alpha_{i}&l_{i}cos\Theta_{i}\\ sin\Theta_{i}&cos\Theta_{i}cos\alpha_{i}&-cos\Theta_{i}sin\alpha_{i}&l_{i} sin\Theta_{i}\\ 0&sin\alpha_{i}&cos\alpha_{i}&d_{i}\\ 0&0&0&1\end{array}\right] \tag{5}\]  
Therefore, the relationship matrix \(T\) from the base frame \(O_{0}^{\Re}X_{0}^{\Re}Y_{0}^{\Re}Z_{0}^{\Re}\) to the probe frame \(O_{7}^{\Re}X_{7}^{\Re}Y_{7}^{\Re}Z_{7}^{\Re}\) is represented by the Equation (6):  
\[T=\prod_{i}^{6}B_{i}Trans\left(0,0,d_{7}\right)=\prod_{i}^{6}A_{i}E_{i}Trans \left(0,0,d_{7}\right) \tag{6}\]  
where all the generalized geometric error parameters are indicated by using vector \(\epsilon=\left[\epsilon_{11},\,\epsilon_{12},\,\cdots,\epsilon_{ij},\,\cdots, \,\epsilon_{66}\right]\)\(\left(i,j=1,\,2,\,\cdots,\,6\right)\) in the actual kinematic model of PACMM.  
Figure 3: Configuration of portable articulated coordinate measuring machine (PACMM) based on the generalized geometric error model.
# Return final key  key = 'join(shared_key_alice.astype(str))  return key
## Appendix C Ultra-Optimisation  
Here is the Ultra-Optimised QRNG Key Generation Algorithm, which was identified as the best performing among the 10 methods analysed.  
Key Features of This Algorithm: Direct Decimal Generation: Instead of generating binary numbers and converting them, it directly generates decimal digits (0-9).  
Optimised Data Type: Uses np.uint8 for memory efficiency and high-speed performance. Ultra-Fast Execution: Produces a 2048-bit (256-digit decimal) cryptographic key in just \(\sim\)0.00011 s.  
Output Example: Generated Key:3487123733006247813337925766721879591743713536851167789863445148205067982411  
58126544192032357810662123430024202544155480269947069923885808514826426782850994  
00243690631763973637975195311563853771766521369076261055949748573600383013208752  
59292457995100998106  
Execution Time: 0.00011 s  
Security Considerations:  
This method is exceptionally fast, but for true quantum randomness, it should be implemented using hardware-based QRNG devices (e.g., quantum vacuum fluctuations).  
We provided the Python code for the ultra-optimised QRNG key generation algorithm.  
import numpy as np  
import time
## 3 Reliability Models of Electric Distribution System Components
### Interdependencies Modeling of Coupled Electric Power System and ICT Infrastructures  
With the rise of smart grid technologies, the interdependencies of communication technologies and electric power systems become an important aspect in the development of both networks [73]. Modeling such interdependencies will be even more complex in the planning and future operation of multi-energy systems (MES) integrating various energy converters and sources of different physical nature [74].  
Infrastructures interdependencies are based on physical and functional relationships among individual components both within and between systems. To characterize the effects of failure propagation from the single component or system to mutually dependent interconnected systems the structure modeling of complex infrastructures can be used [75]. The individual operating conditions of the component in the system can be analyzed and the fault propagation can be reduced by having fast recognition of threats, redundancy design and alternative modes of operation [76]. The concept of complex networks theory [77], which is based on the graph theory, can be used to describe and analyze critical infrastructures on a large scale with multifaceted topologies [78]. The interdependency modeling techniques of coupled infrastructures for integrating ICT within the electric power system (EPS) are offered in [33; 34].  
A graph can represent a network with its set of components and connections between them. Applying graph representation to the coupled EPS and ICT infrastructures, the vertices indicate system components such as buses, gateways and routers while edges correspond to the power lines, cables and communication links. In order to characterize the interdependencies between the infrastructures they can be classified as follows:  
* Connection of an electric node with another electric node, which characterize typical power flow in the electric power grid;
* Connection of a communication node with another communication node that corresponds to a data flow;
* Connection of an electric node to a communication node that represents an electricity supply for the ICT infrastructure;
* Connection of a communication node to an electric node which is responsible for sending and requesting information to and from the power system component.