esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Emerging Blockchain Technology Platforms for Securing the Internet of Things
## 3 Blockchain Platforms
### Hyperledger  
Hyperledger occupies a significant and influential position in blockchain technology and provides a broad spectrum of tools and frameworks tailored to cater to the distinct requirements of corporations and institutions. Some of its frameworks include Hyperledger Fabrics, Sawtooth, Indy, and Besu [50]. Like ethereum, Hyperledger's components include encryption (Digital signature and Hashing), chaincode, and Zero Knowledge of Proof (ZKP). Hyperledger is one of the most diversified blockchain frameworks. It redefined blockchain ecosystems by introducing lots of flexibility, such as choosing a custom consensus mechanism, two ledgers for blockchain queries, cryptography to secure transactions, decentralised identities, support for SDKs and APIs, and network monitoring [51]. Its notable influence has been seen in many studies lately for IoT security with blockchain.  
[PERSON] and [PERSON] [52] identified insecurity with IoT access control and proposed a solution using Hyperledger Fabric. This solution employs attribute-based access control, where several attributes were determined based on the sensitivity of the data. The authors report the performance of the proposed system by testing it with the iFogSim simulator. Another work [53] dealt with protecting IoT system devices against attacks associated with firmware updates of embedded IoT devices. Their system uses blockchain as a trusted network to ensure the authenticity and integrity of firmware updates. CATP-Fabric was proposed as a new blockchain platform to solve issues around conflicting transaction problems in Hyperledger Fabric for delay-sensitive IoT applications. This work divides transactions into diverse groups for parallel processing, filters stale transactions, and prioritises read-only transactions to reduce overhead. The results illustrated show thatCATP-Fabric achieves a high throughput of successful transactions while maintaining a lower aborting transaction rate compared to benchmark blockchain systems.  
To counter the latency issue that is common with IoT blockchain systems, [PERSON] et al. [54] proposed a latency distribution model for Hyperledger Fabric (HLF) using probability distribution fitting and showed that the latency can be modelled with the gamma distribution. The paper further presents three significant parameters of high-level features and examines their effects on latency. It offers an approach to reduce the average latency of HLF. The model and analysis put forward in this study can be employed to predict the latency of HLF before the actual implementation of blockchain Internet of Things (BC-IoT) networks, thereby addressing the outstanding issue of latency estimation in [50]. [PERSON] et al. [50] leveraged smart contracts in HLF to propose a model addressing the scalability, processing power, and storage limitation of IoT edge devices in the blockchain network. HLF is a permission blockchain that has been successfully proposed as a solution to several IoT system issues. Its flexibility has been leveraged in several works.
# Experimental Evaluation of Collision Avoidance Techniques for Collaborative Robots
## 4 Results
### Test Case 2  
In the second test case, a stationary obstacle is placed in the robot's path. As in the previous example, the planned motion is a straight line between two points at a speed of 0.12 m/s. A spherical object has been placed at the location where the virtual obstacle is located to facilitate the interpretation of the motion (Figure 7). When the end effector's region of influence reaches the obstacle, the algorithm imposes the repulsive velocity term. Once the robot overcomes the obstacle, the positioning error is gradually recovered, thanks to the proportional corrective term.  
Figures 8-10 make a comparison between the joint positions and velocities in the simulation and in the real system. Figure 8 refers to the case where the robot avoids the obstacle with mode I, i.e., the end-effector can freely vary both the position and the orientation. For this reason, a change in the velocity of joints 4 and 5 can be observed at \(t=2.2\) s. Figure 9 refers to mode II, i.e., with the possibility of changing the position and only the rotation about the z-axis. It can be seen that there is a small variation in the velocities of joints 4 and 5 so that the obstacle can be avoided without changing the orientation of the end-effector. Figure 10 refers to mode III, where the orientation is kept constant. The diagrams are very similar to the previous case, except for a different tendency of the speed of joint 6.  
All plots show that the simulated and real data follow the same trend. The main difference is due to a slight latency in the real system when the obstacle effect is activated, which leads to an increase in joint velocity so that the obstacle can be avoided in a shorter time. Nevertheless, the recovery in the final phase of the trajectory is fast enough to reach the endpoint in the foreseen time.  
Figure 7: Test case 2: avoidance of a fixed obstacle while the robot moves along a linear path, Mode I. Simulated (**top**) and real (**bottom**) motion of the robot.  
Figure 11-13 show the error between simulated and real data when mode I, II, and III are selected, respectively. A peak of the error is visible when the obstacle is perceived, but it does not exceed 0.14 rad in position. The average of the error between the joints is 0.02 rad for position and 0.1 rad/s for velocity.  
Figure 8: Test case 2, Mode I. Joint rotations(**left**) and speeds (**right**).  
Figure 10: Test case 2, Mode III. Joint rotations (**left**) and speeds (**right**).  
Figure 9: Test case 2, Mode II. Joint rotations (**left**) and speeds (**right**).
### Metal Oxide Nanofluids in FSCs  
The TC and heat transfer of a fluid improve with the use of metal oxide nanoparticles, as shown by [8]. The nanofluids used were aluminum oxide (Al\({}_{2}\)O\({}_{3}\)), magnesium oxide (MgO), cerium oxide (CeO\({}_{2}\)), titanium dioxide (TiO\({}_{2}\)), zinc oxide (ZnO) and iron oxide (Fe\({}_{2}\)O\({}_{3}\)). In [9], the thermophysical characteristics of nanofluids, such as viscosity, TC and heat transfer with nanoparticles of (SiO\({}_{2}\)), (Al\({}_{2}\)O\({}_{3}\)), (ZrO\({}_{2}\)), (TiO\({}_{2}\)), and (CuO) have been experimentally evaluated. The researchers have shown that viscosity increases with decreasing the nanoparticle size, while thermal conductivity increases as the particle size increases. [PERSON] et al. [10] carried out an experimental investigation in which they studied the effects of the thermophysical properties of the Al\({}_{2}\)O\({}_{3}\)/water nanofluid on the efficiency of an FPSC. The authors showed that the efficiency of the solar collector increased by 28.3%. [PERSON] et al. [11] carried out an experimental study of energy and exergy, using MgO-H\({}_{2}\)O nanofluid in concentrations of 0.25 to 1.50 vol%. The authors found that at higher concentrations of nanoparticles, when agglomeration occurs, the Brownian movement between the nanoparticles and the molecules of the base fluid decreases, which causes a reduction in the convective heat transfer between the base fluid and the nanoparticles. [PERSON] et al. [12] estimated the thermal efficiency of an FSC using CuOH\({}_{2}\)O. Efficiency was improved by 5% with a volume percentage of 2 vol% and a nanoparticle size of 25 nm. The performance is proportional to the volume fraction of the nanoparticles in the base fluid, as described by [13]. [PERSON] et al. [5] carried out studies with metallic and metal oxide nanoparticles to operate FSCs.  
[PERSON] et al. [6] demonstrated that the use of hybrid nanofluids can enhance TC compared to single nanofluids by preparing a hybrid Al\({}_{2}\)O\({}_{3}\)-Cu nanofluid using the hydrogen reduction technique in a 90:10 ratio of Al\({}_{2}\)O\({}_{3}\)-to-CuO nano powder. Measurements on TC and viscosity showed that the increase in viscosity is more pronounced than the conductivity with respect to concentration. The highest conductivity gain was recorded at 12.11% for a 2% volume concentration. [PERSON] and [PERSON] [4] prepared hybrid nanofluid by mixing surface-modified (crystalline) copper-titanium nanocomposites, with a particle range of 0.1% to 2% (vol.%) and mean nanoparticle size of 55 nm. The results confirm the improvement in surface conductance (hs) from 0.52 to 1%. [PERSON] et al. [14] found the increasing effect of nanoparticle concentration on the TC of the Ag-MgO-water nanofluid with the same concentration ratio.
###### Abstract  
This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining [PERSON]'s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations.  
Keywords:NB-IoT; 5G; autonomous agricultural machinery; cloud platform; smart farming; sensor data transmission +
Footnote †: journal: Article  
_Article_
# Analysis of BDS/GPS Signals' Characteristics and Navigation Accuracy for a Geostationary Satellite
## 4 Navigation Performance  
In this section, the accuracy of single point position solutions and real-time orbit determination solutions are analyzed over two days from 9:00 on 17 May to 9:00 on 19 May 2020. No truth orbits are available as references in evaluating navigation accuracy. However, we use the precision orbit determination solutions generated on ground, which are estimated about submeter level through assessment of overlap comparisons proposed in [22].
# Cyber Ranges and TestBeds for Education, Training, and Research
## 3 Background
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Operator** & **Objective** & **Sector** & **Environment** & **Infrastructure Platform(s)** & **Dataset** \\ \hline NATO Cyber Range [31] & T, E & M & EM & VW & N/A \\ \hline Masaryk University (KYPO) [32; 33] & R, T, E, ED & A & EM & O & Y \\ \hline Florida Cyber Range [34] & ED, R, T, O & M & N/A & N/A & N/A \\ \hline Sandia National Laboratories (Cyber & T & G & N/A & MN & N/A \\ Scorpion) [25] & T & G & N/A & MN & N/A \\ \hline Virginia Tech [35; 36] & R, T, E & A & S & AW & N/A \\ \hline De Monfortt University [10] & R, T, E & A & HCP & Q & OD \\ \hline Royal Military Academy [37; 38] & R, T & A, M & S & VB, C & N \\ \hline AIT Austrian Institute of Technology [39; 40; 41] & R, T, E & A,G, M PE & HCP & O, TR & N \\ \hline Naval Postgraduate School [42; 43] & T, E, ED & A, G, M & S & D & IN \\ \hline Norwegian University of Science and Technology (NCR) [44] & R, T, E, Ts & A, G, M, PE & EM, S, HCP & O, VB, VM, D & OD \\ \hline Universita degli Studi di Milano [21] & T & R & EM, S & O & No \\ \hline JAMK University of Applied Sciences (JYSSETEC) [45; 46] & R, T, E & A, G, M, PE & EM, S, HCP & N/A & IH \\ \hline Swedish Defence Research Agency (CRATE) [47; 48] & R, T, E & G, M & HCP & VB & ON \\ \hline Michigan Cyber Range [49] & T & A & N/A & N/A & Yes \\ \hline Silensec [50] & T & I & N/A & N/A & ON \\ \hline CYBERUM (fujitsu) [51] & T & I & N/A & N/A & ON \\ \hline DECIDE (NUARI) [52] & R, T, E & A & N/A & N/A & ON \\ \hline Georgia Cyber Range [53] & ED, T, R, DM, DV & A & N/A & N/A & ON \\ \hline IBM X-Force Command C-TOC [54] & T, E & I & N/A & N/A & ON \\ \hline Cybezer [55] & T, E & I & N/A & N/A & ON \\ \hline Airbus Cyber Range [56] & R, T, E & 1 & S & N/A & ON \\ \hline Raytheon Cyber Range [57] & T,E & M, A, I & N/A & N/A & ON \\ \hline hns-platform [58] & T, E & I & HCP, S & N/A & ON \\ \hline Cyberhit Cyber Range [59] & T, E & I & S & N/A & ON, OP \\ \hline Cyber Warfare Range [60] & T, E & I & S & N/A & OS, OP \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of cyber ranges and testBeds.
## 4 Results and Discussion
### Basic Property Test Results
Figure 8: Softening point test results of modified and unmodified asphalt at different freeze-thaw (F-T) cycles. Figure 7: Penetration test results of modified and unmodified asphalt at different F-T cycles. (**a**) 25 \({}^{\circ}\)C penetration; (**b**) penetration index. Figure 9: Ductility test results of modified and unmodified asphalt at different F-T cycles. As can be observed, 135 \({}^{\circ}\)C rotational viscosity and aging index for modified and unmodified asphalt under the F-T aging process are given in Figure 10. It can be seen that 135 \({}^{\circ}\)C rotational viscosity and aging index for modified and unmodified asphalt increases with the increase of F-T cycles. After 30 F-T cycles, the aging index of BA, CBMA and NHSSMA is 0.0049, 0.00321 and 0.0012, respectively. NHSSMA has the lowest aging index after 30 F-T cycles, indicating that the addition of NHSS can effectively resist F-T aging progress. Based on the basic properties tests such as penetration, softening point, 10 \({}^{\circ}\)C ductility and 135 \({}^{\circ}\)C rotational viscosity, it can be concluded that the incorporation of NHSS results in a relatively more stable binder compared to BA and CBMA, which may be beneficial for F-T aging resistance.
###### Abstract
This allows for the learning of more complex nonlinear functions and representations of parameter change relationships, without over-emphasizing either bias or variance in the model, particularly when the number of input samples is limited. Hence, a DCNN offers excellent potential for extracting important features from low-dimensional core drilling data. For example, [PERSON] et al. (2013) applied a DCNN to predict the strength parameters of rock cores based on data generated during the core drilling process. The approach demonstrated excellent performance in estimating the unconfined compressive strength of different types of rocks. Moreover, the addition of convolution kernels in the algorithm facilitated the rapid and reliable prediction of the strength parameters of on-site rocks. However, the problem of extracting sample features and fitting sample functions remains to be solved. The present study addresses the above-discussed issues by proposing a novel preprocessing method that transforms complex and abstract drilling data into intuitive image data, thereby converting the intricate lithofacies recognition problem into a classic image recognition problem under supervised learning. First, time-sequence core drilling data comprising torque, WOB, and rotational speed features are normalized and transformed into square matrices, and the data within the square matrices are then converted into the pixel values of what serves as a three-channel lithofacies image. Second, the lithofacies image data are flattened into individual column vectors for input into a DANN, which increases the input vector dimensions from the small number of dimensions typically employed to tens of thousands of dimensions. Our proposed approach was applied for detecting two lithofacies in a binary classification process, and the results demonstrated that the proposed approach offered more than a 92% accuracy rate. This implies that the preprocessed drilling data can be more effectively used for feature extraction. Accordingly, the proposed method provides a new approach to identifying lithofacies from core drilling data.
# An Enhanced Aircraft Carrier Runway Detection Method Based on Image Dehazing
## 4 Experiment Results and Discussions
### Runway Localization Analysis
#### 4.2.1 Dehazed Function  
To demonstrate the necessity of image dehazing, we selected two hazed images as the input images and observed the runway detection performance before and after dehazing. The results are shown in Figure 14. From Figure 14, we can observe that in a hazed image \(I_{1}\) compared to the dehazed images \(R_{1}\), the UFLD algorithm detects fewer points in the hazed images, and the extension directions of these points deviate significantly from the direction of the ground truth (GT) runway lines. These two defects could lead to large errors in the localization runway line equations, which are undoubtedly fatal for the landing process of the CUAV. This is mainly attributed to the fact that in hazed images, the average pixel value of the image rises while the variance is reduced due to the degradation of the image. As a result, some of the edge information in the image is lost, and the detection algorithm cannot effectively detect the edges of the runway lines, leading to a decrease in detection performance. Dehazing operations mitigate this phenomenon, thereby enhancing the performance of runway line detection algorithms.
## 1 Introduction
### Axial Stiffness Analysis of the Laminated Membrane Coupling and the Lamination Group  
In this section, the pre-tightening force of connecting bolts between the driving and driven flanges is set to 15,000 N. After fixing the driven flange, an axial displacement of 1 mm is applied on the active flange. Then the load sub-step method, which is divided into 5 sub-steps, is applied to analyze the axial stiffness of the laminated membrane coupling and the lamination group.  
The end face of the driven flange is set to \"fixed support\", and two load steps are set to apply the load, as shown in Figure 15. On the side of the six bolts, the preload of the first bolt is set to 15 kN. Then'set the lock' is applied in the second step. Table data is then used to load the end face of the active flange. The first step is set to 0, and the second step is set to 1 mm axial displacement. In the second step, five sub steps were set up, and the loading displacements were 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm and 1 mm, respectively.  
Figure 14: Influence of the flange elasticity on the angular displacement and torsional stiffness.  
Figure 15: Influence of the flange elasticity on the reaction force and axial stiffness.  
Through the simulation analysis, the corresponding reaction forces of the laminated membrane coupling and the lamination group for axial displacements of 0.2 mm, 0.4 mm, 0.6 mm, 0.8 mm and 1 mm are obtained. In this case, the axial stiffness \(K_{a}\) of the laminated membrane coupling and the lamination group can be expressed as:  
\[K_{a}=\frac{\mathrm{d}F}{\mathrm{d}L_{a}}\approx\frac{\Delta F}{\Delta L_{a}} \tag{3}\]  
Figure 15 shows the corresponding reaction force and axial stiffness of the laminated membrane coupling and the coupling lamination group are loaded under different axial displacements loads. It can be seen from the figure that for a small displacement range (i.e., axial displacement 0-1 mm), there is a nonlinear correlation between the axial force and displacement of the laminated membrane coupling, and the same is true for the coupling lamination group. In this range, the axial stiffness of the laminated membrane coupling is approximately \(1.4\times 10^{5}\) N/m. Meanwhile, it is found that the axial stiffness of the coupling lamination group is approximately \(2.40\times 10^{5}\) N/m. It should be indicated that this stiffness value is relatively small so that the laminated membrane coupling can be considered as a flexible component along the axial direction.
## 2 Predictive Equations and Machine Learning Models
### Machine Learning Methodologies
#### 2.3.3 SHapley Additive exPlanations (SHAP)  
SHapley Additive exPlanations (SHAP) summary plots are visual depictions of the influence each input variable has on the predicted quantity. In recent years, the SHAP approach has been frequently applied in order to explain ML models [54; 55; 56; 57; 58; 59]. In SHAP summary plots, each data sample is represented by a dot, the color of which is determined by the feature value. The horizontal position of each dot in the SHAP summary plots gives information about the impact of the input feature on the model output, such that featureswith an increasing impact on the model output are placed on the right-hand side of the summary plot, and those with a decreasing impact are placed on the left-hand side. The farther a dot is positioned to the right- or left-hand side of the zero point, the greater impact this feature has on the model output. The dots colored in the shades of red indicate the high value of an input feature whereas dots colored in the shades of blue indicate the low value of an input feature. The changes in the prediction when a feature is removed from the model provide the basis for the SHAP values \(\phi_{\rm i}\), which control a sample's location along a horizontal direction. The computation formulas of these values are expressed in Equations (5) and (6):  
\[\rm g(x^{\prime})=\phi_{0}+\sum_{i=1}^{M}\phi_{i}x^{\prime} \tag{5}\]  
\[\rm\phi_{i}=\sum_{S\subseteq F\{i\}}\frac{|S|!(|F|-|S|-1)!}{|F|!}\Big{[}f_{S \cup\{i\}}\Big{(}x_{S\cup\{i\}}\Big{)}-f_{S}(x_{S})\Big{]} \tag{6}\]  
In Equation (5), M is the total number of input features and g is an explanation function, which linearly combines the simplified input features \(x^{\prime}\), which only take values from the set {0, 1}. The Shapley values, \(\rm\phi_{i}\), are the coefficients of this linear combination. These simplified input features are related to the actual input values by a mapping function, h, with \(\rm x=h(x^{\prime})\). The computation of \(\rm\phi_{i}\) values is shown in Equation (6), where F denotes the set of all input features, S denotes a subset of F which does not include the input feature with index I, and f represents the predictive model. Further explanations of the SHAP methodology can be found in [60].