esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Hybrid Authentication Mechanisms  
Hybrid authentication mechanisms are garnering increasing interest due to their ability to combine the strengths of multiple technologies to address modern security challenges. By integrating centralized systems with decentralized approaches such as blockchain, these mechanisms achieve a balance between scalability, efficiency, and robust security. Recent advancements in this field highlight the use of hybrid architectures and innovative techniques such as machine learning and lightweight cryptography to enhance authentication in various environments, including IoT systems and distributed networks.  
The article in [15] explores a novel authentication method leveraging the Ethereum blockchain to replace traditional one-time passwords (OTPs) in two-factor authentication (2 FA). Using smart contracts, it demonstrates how blockchain-based 2 FA strengthens security against common vulnerabilities such as phishing and brute-force attacks. This method's applicability is illustrated through scenarios including registration, login, and password recovery, showcasing its potential to counter modern cyber threats.  
The research in [16] presents a hybrid architecture integrating centralized edge servers with blockchain technology to achieve efficient and scalable IoT system authentication. The architecture employs lightweight cryptographic methods to reduce computational load and energy consumption, making it suitable for resource-constrained IoT devices. Performance evaluations demonstrate its superiority in scalability, response time, and energy efficiency compared to existing solutions.  
In [17], the authors examine the integration of hybrid consensus algorithms with machine learning techniques to enhance blockchain security. By combining methods such as Delegated Proof-of-Stake (DPoS), Practical Byzantine Fault Tolerance (PBFT), and Proof-of-Work (PoW), they address challenges like scalability, energy efficiency, and resistance to 51% attacks. Their study highlights how machine learning improves anomaly detection, threat prevention, and adaptive decision-making, ensuring a robust and scalable blockchain ecosystem.
# Implementation of Efficient 5G AKA Protocol for Light-Weight Environment
## 1 Introduction
### Background
#### 1.1.1 Vulnerability of the 5G AKA protocol
When an authentication request is sent via broadcast, other UEs within the base station that do not correspond to the MAC provide MAC failure messages, but they send synchronization failure messages if the target exists in that base station range. In other words, an attacker can locate the target UE within the range of the attacker's base station if the authentication fails, because a synchronization failure message is received.
## 2 Photogrammetric Processing Chain  
### Principle and Outline of the Photogrammetric Workflow  
Nowadays, the photogrammetry workflows often combine principles of conventional photogrammetry and two computer vision approaches: \"Structure from Motion\" (SfM) and \"Multi-View Stereo\" (MVS). Unlike traditional photogrammetry, SfM photogrammetry allows for determining the internal camera geometry without prior calibration. Camera external parameters can also be determined without the need for a pre-defined set of GCPs [7].  
A detailed explanation of the SfM photogrammetry workflow is given in reference [10]. The main steps are depicted in Figure 2. Homologous points are identified in overlapping photos and matched. Generally, this step is based on the use of a Scale Invariant Feature Transform (SIFT) registration algorithm [28]. This algorithm identifies the keypoints, creates an invariant descriptor and matches them even under a variety of perturbing conditions such as scale changes, rotation, changes in illumination, and changes in viewpoints or image noise. Taking into account the tie points and the GCPs, (i) the external parameters of the camera, (ii) the intrinsic camera calibration, also called the \"camera model\" (defined by the principal point, the principal distance and the distortion parameters introduced by the lens) and (iii) the 3D positions of tie points in the study area are estimated. The estimation is optimized by minimization of a cost function. A dense point cloud is then computed using algorithms inspired from Computer Vision tools [29], which filter out noisy data and allow for generating very high-resolution datasets [7; 9].  
The GCPs are used for georeferencing and for the optimization of camera orientation (Figure 2), providing additional information of the geometry of the scene, to be used to refine the bundle adjustment. Therefore, the spatial distribution of GCPs can be critical for the quality of the results [3; 9; 10].  
In this study, each dataset was processed using two software tools in parallel: Agisoft PhotoScan(r) Pro v1.2.3 (a widely used integrated processing chain commercialized by AgiSoft(r) and MicMac(r) (an open-source photogrammetric toolset developed by IGN(r) the French National Institute of Geographic and Forestry Information). Both PhotoScan(r) and MicMac(r) workflows allow control measurements to be included in the bundle adjustment refinement of the estimated camera parameters. For a more coherent comparison, the camera model parameters are not fixed for both forms of software.  
Figure 1: Examples of linear coastal landforms on French coasts: (**a**) Sillon de Talbert thin trail of pebbles, (**b**) Mimbeau bank in Cap Ferret, (**c**) Suscinio beach, (**d**) Ermitage back-reef beach (GoogleEarth(r) images).
# Trajectory Tracking Control Strategy of 20-Ton Heavy-Duty AGV Considering Load Transfer
## 5 Trajectory Tracking Co-Simulation Analysis Based on Simulink and CarSim
### Simulation Results Extraction and Analysis
#### 5.2.1 Trajectory Tracking Simulation Under High Payement Adhesion Coefficient

Table 3: Simulation results under high pavement adhesion coefficient. As can be seen from Figure 14, during AGV startup and hill climbing, compared with the TED controller, the TPD controller reduces the front wheel adhesion rate while increasing the rear wheel adhesion rate. At the same time, the left-front and right-front wheels exhibit identical adhesion rates, as do the left-rear and right-rear wheels. This phenomenon arises because acceleration and slope during startup and climbing induce a rearward load transfer. The TPD controller automatically adjusts driving torque based on wheel-borne loads, resulting in smaller front-wheel torque and larger rear-wheel torque. Consequently, front-wheel adhesion decreases, rear-wheel adhesion increases, and maximum tire adhesion is reduced. As shown in Table 3, the maximum wheel adhesion rate decreases by 11.5%, demonstrating effective torque distribution.
# Vibration Damage Analysis of Bottom Hole Assembly Under Axial Impact Based on Dynamic Analysis
## 2 Impact Simulation Model  
### Basic Assumptions  
The dynamic analysis of the downhole drill string system during drilling is highly complex, as it requires the coupling of the rock formation, drill bit, drill string, and borehole wall, which is a challenging task. Therefore, the downhole environment is appropriately simplified in this study and a substructuring method is employed to establish the dynamic model of the drill string system [16; 17; 18]. Since the focus of this paper is on the analysis of the longitudinal vibrations and dynamic stresses in the drill string induced by impact drilling operations, only the axial impact-induced longitudinal vibrations between the drill string and the rock formation are considered. The lateral, torsional, and coupled vibrations of the drill string system are not accounted for in this study. For the sake of convenience in the analysis, the following reasonable physical assumptions are made:  
1. The impact force is converted into an equivalent periodic excitation acting on the drill bit;
2. The effect of drilling fluid on the drill string is neglected;
3. Rock damage and fragmentation are taken into account. If rock failure occurs, an element is removed from the mesh;
4. Initially, the borehole axis coincides with the drill string axis, and there exists an annular clearance between the borehole wall and the drill string. The borehole cross-section is assumed to be circular;
5. The borehole wall is set as an undeformable rigid body, while the BHA is modeled as an elastic beam element.
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## 3 Results and Discussion
### Simulated and Measured S-Parameter Results for the Printed Textile-Based Antenna  
Full-wave simulations of the antenna were performed using an ANSYS HFSS (Figure 6). A prototype of the complete antenna design was been constructed and tested to verify its impedance matching and mutual coupling performance. SMA connectors were used to feed both antennas. Two-port reflection coefficient measurements of the prototype antenna were performed using a VNA. The simulated and measured S-parameter results of the proposed two-port antenna with optimized dimensions (Figure 1) are provided in Figure 6. The antenna exhibits a -10 dB bandwidth around 79 MHz from 2.26-2.339 GHz, with mutual coupling of less than -30 dB over the entire band. The experimental and simulated results show good agreement for this antenna.  
Figure 5: Buckling test of the nonwoven substrate (eight layers of nonwoven fabric), knit substrate (eight layers of knit fabric), and combination substrate (two layers of nonwoven fabric and six layers of knit fabric spray coated with BT dielectric ink). (**a**) Photography of experimental setup. (**b**) Buckling force displacement curve with fixed–fixed ends.
# A Compact Quad-Element UWB-MIMO Antenna System with Parasitic Decoupling Mechanism
## 2 Antenna Configuration
### Equivalent Circuit Modeling  
Circuit modeling of a UWB antenna is generally carried out in a time domain simulation because of extremely wide bandwidth. The modeling is carried out by matching the input impedance or admittance of the antenna with the lumped equivalent model. One way of modeling a UWB antenna is by considering it a combination of various overlapping adjacent resonators. If a parallel RLC resonator is considered as a unit element, a number of them is considered to be attached in series as shown in Figure 2. Based on the required resonances, the number of these RLC resonant elements can be added or subtracted.  
The values of these lumped components are optimized through a schematic solver where the values of each element is tuned to match the envelop of the impedance curve and resonance peaks. The proposed UWB antenna element comprises of three resonant peaks within the UWB band, one is around 3 GHz, second is around 7 GHz and third is around 11.5 GHz. Therefore, three parallel resonant circuits are included in the equivalent circuit model. The equivalent circuit lumped components optimized at R\({}_{1}\) = 80 \(\Omega\), R\({}_{2}\) = 95 \(\Omega\), R\({}_{3}\) = 110 \(\Omega\), L\({}_{1}\) = 1.4 nH, L\({}_{2}\) = 0.4 nH, L\({}_{3}\) = 0.21 nH, C\({}_{1}\) = 1.92 pF, C\({}_{2}\) = 1.25 pF and C\({}_{3}\) = 0.82 pF.
# Realistic Texture Mapping of 3D Medical Models Using RGBD Camera for Mixed Reality Applications
###### Abstract  
Augmented and mixed reality in the medical field is becoming increasingly important. The creation and visualization of digital models similar to reality could be a great help to increase the user experience during augmented or mixed reality activities like surgical planning and educational, training and testing phases of medical students. This study introduces a technique for enhancing a 3D digital model reconstructed from cone-beam computed tomography images with its real coloured texture using an Intel D435 RGBD camera. This method is based on iteratively projecting the two models onto a 2D plane, identifying their contours and then minimizing the distance between them. Finally, the coloured digital models were displayed in mixed reality through a Microsoft HoloLens 2 and an application to interact with them using hand gestures was developed. The registration error between the two 3D models evaluated using 30,000 random points indicates values of: 1.1 \(\pm\) 1.3 mm on the \(x\)-axis, 0.7 \(\pm\) 0.8 mm on the \(y\)-axis, and 0.9 \(\pm\) 1.2 mm on the \(z\)-axis. This result was achieved in three iterations, starting from an average registration error on the three axes of 1.4 mm to reach 0.9 mm. The heatmap created to visualize the spatial distribution of the error shows how it is uniformly distributed over the surface of the pointcloud obtained with the RGBD camera, except for some areas of the nose and ears where the registration error tends to increase. The obtained results indicate that the proposed methodology seems effective. In addition, since the used RGBD camera is inexpensive, future approaches based on the simultaneous use of multiple cameras could further improve the results. Finally, the augmented reality visualization of the obtained result is innovative and could provide support in all those cases where the visualization of three-dimensional medical models is necessary.  
augmented reality; mixed reality; computed tomography; cone-beam computed tomography; 3D camera; texture mapping Article
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges  
Despite the unparalleled advantages of satellite images to explore the SUHI, researchers confront a series of difficulties in using them due to both data and methodological limitations. The main research challenges are summarized below.
###### Abstract
This model combines 3D convolution for extracting spatial and spectral features and 2D convolution for extracting spatial features only. Such a combination effectively improves the classification accuracy of HRSIs. Combining residual connections and the attention mechanism establishes a multiscale residual attention module to refine feature mapping. 4. The 3D-2D CNN structure uses multiscale convolution composed of depthwise separable convolution (DSC), which can effectively reduce the amount of parameter calculation and prevent overfitting.
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 3 Results
### Soil Properties of Landslide and Non-Landslide Areas  
Soil layers in landslide areas are known to have finer soil particles and smaller liquid limits than in non-landslide areas [62; 63]. Soil particle size distribution is also an important factor in landslides. In flow-type landslides, soil particle size analysis is performed. If there are 20-80% particles of 2 mm or more in diameter, they are classified as a debris flow. On the other hand, it is considered soil flow if it contains 80% or more of particles less than 2 mm in diameter [11]. Figure 5 shows the particle size test results for soil samples in the different geological features of the areas of study, categorized into the landslide and non-landslide areas in the soil particle size distribution curve. In the genesis region, the soil particle size distribution curve is generally inclined  
Figure 4: Analysis of landslide occurrence patterns of different geological features in the study area. moderately, and coarse and fine-grained soil is moderately mixed. The uniformity coefficient ranges from 5 to 40; while the curvature coefficient, from 1 to 3, satisfying the qualifications for well-graded soil gradations. In addition, the soil particle size in landslide areas was found to be distributed more evenly, while particle size in non-landslide areas has similar distribution specifications. In the granite region, the soil particle size distribution curve showed a moderate slope, and the soils were more thoroughly mixed. Moreover, the uniformity coefficient ranges from 2 to 33; while the curvature coefficient, from 1 to 3, showing that the soil is indeed well-graded. In addition, the granite area showed the same particle size distribution patterns in both landslide and non-landslide areas. Table 1 shows the constants for the characteristics of soil layers in the landslide areas of the gneiss and granite regions. The liquid limit showed a similar tendency, whereas the plastic limit of the soil surface was found to be higher in the gneiss region. Moreover, the plastic limit is a property that is directly related to the content of clay particles in the particle size distribution of the soil layer, which is consistent with the result that the gneiss region shows a higher content of fine-grained soil than the granite region. Soils in the granite region had the highest permeability coefficient between the two regions. Meanwhile, this phenomenon may have been affected by other soil characteristics, such as particle size distribution, roughness, and structure, and geological properties, such as weathering or sedimentation environment.
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 5 Application and Practical Benefits  
Output of the model is presented in Figure 14. The process is repeated until a probability density function of the joint distribution is derived for each risk factor. These distributions can then be input into any existing MCS-based decision-support systems. Notably, for risk factors that have only schedule or risk impacts, marginal distributions will only be calculated for the cost-risk impact or schedule-risk impact (Figure 12a,b). MCS-based decision-support systems use the distributions to sample a cost-risk impact, schedule-risk impact, or joint cost-schedule risk impact value for each risk  
Figure 16: Cumulative density function of the joint distribution.  
Figure 15: Contour plot of the joint probability density function.  
factor based on its probability of occurrence. The results of several simulation iterations are then combined to provide a number of project insights, such as (1) the expected project completion date as cumulative distribution, (2) the expected project cost as a cumulative distribution, and (3) time and cost contingencies. Readers are referred to [12; 37; 47] for more information on the application of MCS-based decision-support systems for risk assessment.  
A primary benefit of the proposed method is its ability to incorporate detailed information into risk analysis inputs. Existing methods for deriving probability distributions from expert knowledge (e.g., elicitation of parameters for triangular, Pert, or uniform distributions) can only incorporate minimum, maximum, and most likely risk impact values. Experts are neither able to provide detailed information regarding the consideration of root causes nor perform risk impact decomposition, limiting the specificity and, in turn, accuracy, of model results. In contrast, the proposed method allows experts to consider the root causes of a risk factor while providing impact values within an impact range through value mapping. Indeed, results of the sensitivity analysis, which demonstrated that the model was particularly sensitive to changes in mapping values (Figure 11), indicate the importance of accurately capturing subjective information. Methods that allow for the decomposition of input information, such as mapping values to smaller ranges, can allow experts to more precisely express their subjective knowledge, thereby enhancing the comprehensiveness of model results.  
A second benefit of the fuzzy logic approach presented here is the transformation of qualitative statements into a quantitative-like format (i.e., probability distribution function), thereby supporting the input of subjective data into quantitative methods, such as MCS. In turn, the proposed approach allows the use of one reliability-based risk assessment method throughout the life-cycle of a project. This alleviates the need for separate qualitative and quantitative methods in different phases of the project, in turn enhancing the consistency of output results.
## 1 Introduction
However, the sophisticated synthesis of CNTs and the difficulty in dispersing the nanoparticles on their surfaces seriously hinders their practical applications. Thus, the improvement of simple and efficient approaches for the synthesis of highly active, supported Pd-based nanocatalysts still remains challenging [36]. In this regard, palladium-based carbon support nanocomposite materials have been extensively useful in cross-coupling reactions [37; 38]. Nevertheless, Pd/C or other heterogeneous, Pd-catalyzed C-C couplings are commonly established for the preparation of a variety of organic molecules. Moreover, carbon as a solid support certifies a greater surface area associated with the analogous silica and alumina-supported catalysts [38]. Palladium-based carbon catalysts are stated to be more stable in air and water, as well as acids and bases, and often do not require reactions to be performed under an inert atmosphere [38]. Indeed, Pd/C-catalyzed Suzuki reactions can be effectively accomplished under diverse reaction conditions, including in aqueous media [39; 40], in organic media [41; 42; 43] and under microwave circumstances [44; 45; 46]. Here, we report on palladium on activated carbon catalysts, which were prepared by varying the content of 'Pd' via an in situ reduction method using hydrogen as a reducing agent. The as-prepared various concentrations of the Pd/C catalysts were characterized by a number of methods, comprising XRD, SEM, EDX and BET. The as-synthesized Pd/C catalysts were applied to Suzuki-Miyaura and Mizoroki-Heck cross-coupling reactions. The synthesized 3% Pd/C catalyst showed exceptional catalytic efficiency toward the Suzuki-Miyaura and Mizoroki-Heck reactions under facile environments in aqueous conditions.