esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 6 Theoretical Analysis
### Environmental Adaptability Analysis  
Assume that at the beginning of the IoV system operation, there are 10 client nodes with an average credit score of 100. The ratio of consensus group nodes to candidate nodes \(P_{0}\) is set to \(2/5\), meaning that four nodes belong to the consensus group and six nodes belong to the candidate group. When the system encounters various challenges, such as signal interference caused by road congestion, adverse weather conditions, or equipment maintenance, as well as poor network conditions, some nodes may experience performance degradation, leading to a reduction in their credit scores and a decrease in the overall average score.  
In this scenario, assuming the current average credit score drops to 99.586, it can be calculated using Equation (3) that the new ratio \(P\) is approximately \(3/10\). This adjustment implies that the number of consensus group nodes decreases to 3, while the number of candidate group nodes increases to 7. Given that the consensus group nodes carry a higher weight in Equation (2), it suggests that some consensus group nodes are more likely to underperform or fail under challenging conditions. Thus, dynamically reducing the size of the consensus group helps retain as many functioning nodes as possible, allowing the system to operate more reliably and maintain better adaptability to environmental changes.
# Global Buckling Resistance of Cold-Formed Steel Beams with Omega-Shaped Sections
## 3 Experimental Campaign
### Geometric and Material Properties  
The profiles under investigation belong to a family of commercial profiles with an omega section, comprising six different heights, each with three or four distinct thicknesses. The selection aimed to include the smallest profile (depth of 70 mm), the largest one (depth of 300 mm), and an intermediate one (depth of 170 mm). In terms of thickness, a consistent logic was applied: the smallest thickness (1.5 mm) for the 70-mm profile, the largest thickness (2.5 mm) for the 300-mm profile, and an intermediate thickness (2.0 mm) for the 170-mm profile.  
Table 1 provides the geometric characteristics of the selected profiles, while Table 2 presents the material properties evaluated based on tensile coupon tests.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Profile** \\ **Section** \\ \end{tabular} } & **Weight** & **Height, \(h\)** & **Width, \(b\)** & **Length, \(L\)** & \multicolumn{2}{c}{**Thickness**} \\ \cline{3-6}  & **(kg/m)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline \(70\times 1.5\) & 2.90 & 70 & 138 & 4.20 & 1.5 & 1.46 \\ \(170\times 2.0\) & 7.75 & 170 & 234 & 4.20 & 2.0 & 1.96 \\ \(300\times 2.5\) & 14.47 & 300 & 269 & 8.30 & 2.5 & 2.46 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric properties of the selected profiles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Profile** \\ **Section** \\ \end{tabular} } & **Yielding Stress, \(f_{y}\)** & **Ultimate Stress, \(f_{u}\)** & **Maximum Strain, \(\varepsilon\)** \\  & **(MPa)** & **(MPa)** & **(\%)** \\ \hline \(70\times 1.5\) & 340 & 455 & 27.5 \\ \(170\times 2.0\) & 330 & 453 & 31.6 \\ \(300\times 2.5\) & 374 & 476 & 29.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties.  
The modulus of elasticity considered was 210 GPa, and the material density was 7850 kg/m\({}^{3}\).
## 2 Background
### Hop Count  
According to [22, 23], the hop count refers to the number of intermediate routers the data will pass through, which clears the misconception that a direct connection between a point-point link in a transmission path is a 1-hop connection. A direct connection does not go through an intermediate router, and thus, it is not a 1-hop connection. However, when an intermediate router exists between them, this can be regarded as 1-hop connection. Figure 1 shows a simple 1-hop connection.
## 4 Results and Discussion
### Crisis Management Vulnerability  
Upon examination, the obtained maps suggest that the majority of buildings within the study area pose a low to medium risk of ignition when considering all the factors combined, as mentioned in the preceding sentences. This finding indicates that firefighting operations can be conducted, provided that strategic plans are in place for emergency services, thereby minimizing potential impacts on the building stock and, most importantly, the population. It is important to highlight that no building presents a very high fire inception index; however, it is essential to implement improvement practices and measures, considering that a significant number of buildings possess a medium Fire Inception Index.  
Furthermore, it is worth noting that the Civil Protection headquarters is located at a considerable distance from the city center, approximately 4 km (Figure 6e). Despite this distance, other emergency services are conveniently situated nearby or within the city center, offering various possible routes in case of a fire incident. These conditions signify that the historic city center has favorable circumstances for effective crisis management, facilitating the mitigation of significant fire-related impacts.  
Figure 6: Cont.
## 3 Results and Discussion
### Heat Flux-Wall Superheat Relationship
#### 3.1.2 Comparative Analysis of Boiling Curve with the Literature Data  
Figure 11 presents a comparative analysis of the current study with selected literature data to provide context for the observed heat transfer performance. While the figure indicates that some studies report higher CHF values than those achieved in the current work, a fundamental distinction lies in the unique boiling behavior observed for our lattice structures. Specifically, the Large Lattice and Small Lattice did not reach CHF within the limits of this experiment. Instead, these lattice surface structures demonstrated a stable boiling behavior in which the wall superheat gradually increased while maintaining robust heat transfer capability, even at the highest heat fluxes achieved.  
When the heat flux range is segmented for comparison, it is evident that at lower heat flux (at or below 750 kW/m\({}^{2}\)), all porous surface structures, including the Staggered, exhibit similarly high heat transfer performance, comparable to much of the literature data.  
Stable operation at high heat flux without reaching or delaying the boiling hysteresis is desirable in practical applications. Therefore, the lattice structures demonstrated robustness and stability, as evidenced by their ability to sustain high heat fluxes without reaching thermal instability. Furthermore, an enhancement of up to 230% in CHF (relative to the plain surface) is comparatively strong and competitive within the field of boiling heat transfer enhancement.  
Figure 11: Comparative analysis of the relationship between heat flux (\(q\))—wall superheat (\(\Delta T\)): This study vs. [PERSON] et al. [29], [PERSON] et al. [52], [PERSON] et al. [53], [PERSON] et al. [54], and [PERSON] et al. [55].
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 4 Precise Statement and Solution of the Discrete-Time Control Problem
### Static State Feedback for the Extended Discrete-Time Plant
In order to show that this is the case for our system, we use the expressions (77), (64), (55), and (18); namely,  
\[\lambda_{1}(S^{d})=\frac{\mu+j\omega_{r}}{\mu-j\omega_{r}}\,,\quad\lambda_{2}(S ^{d})=\frac{\mu-j\omega_{r}}{\mu+j\omega_{r}}\,, \tag{106}\]  
and  
\[z_{l}(\Sigma_{\rm G}^{d})=\frac{\mu+s_{l}(\Sigma_{\rm G})}{\mu-s_{l}(\Sigma_{ \rm G})}\,,\quad l=1,2\,, \tag{107}\]  
where  
\[s_{1}(\Sigma_{\rm G})=-\frac{b}{2p}-\sqrt{\Big{(}\frac{b}{2p}\Big{)}^{2}- \frac{k}{p}}\,,\quad s_{2}(\Sigma_{\rm G})=-\frac{b}{2p}+\sqrt{\Big{(}\frac{b }{2p}\Big{)}^{2}-\frac{k}{p}}\,. \tag{108}\]  
Simple considerations show that, if \(b>0\), then \((\lambda_{l}(S^{d}))_{l=1}^{l=2}\cap(z_{l}(\Sigma_{\rm G}^{d}))_{l=1}^{l=2}=\emptyset\); meanwhile, for \(b=0\), the latter condition still holds if and only if \(k\
eq\omega_{r}^{2}p\). \(\Box\)  
Theorem 1 is essential, as it provides a basis for the development of the dynamic error feedback controller in the form of (80).
###### Abstract  
One of the methods used to produce buffer layers for thin film solar cells is the Spray ion layer gas reaction (SPRAY-ILGAR). This method has exhibited astonishing efficiencies in the fabrication of homogenous compact metal chalcogenide films. The same approach can be applied in the preparation of photocatalyst on a substrate, in order to acquire a homogeneous and durable layer. In this work, the Spray-ILGAR technique has been utilized in the preparation of titania photocatalysts on stainless steel, which was carried out at Helmholtz-Zentrum Berlin for Materials and Energy, in Berlin, Germany. The characterizations and photocatalytic testing of the synthesized materials were then done at the Institute of Catalysis, Hokkaido University. The scanning electron microscopy (SEM) analysis showed that the titania was dispersed uniformly on top of the stainless steel, with a very durable and strong attachment. It was also found that the concentration of the titania on stainless steel can be easily varied by changing the concentration of the titania-precursor solution. Higher concentrations will result in a more compact and dense layer, while lowering the concentration of the precursor solution produces a less dense layer of TiO\({}_{2}\). Apart from that, different temperatures did not change the distribution of the samples much. The photocatalytic activity of the synthesized materials was determined in terms of the photocatalytic decomposition of acetaldehyde under ultra violet (UV) light irradiation. The photocatalytic testing results proved that the samples can completely degrade acetaldehyde under UV irradiation. The heating temperature played a crucial role, as the sample prepared by with heating temperature of 550 \({}^{\circ}\)C, concentration of titania-precursor of 6.83 mM and a spraying time of 12 min showed the best results, requiring only 35 min to fully degrade 500 ppm of acetaldehyde.  
spray-ILGAR; titania; stainless steel; photocatalytic decomposition; acetaldehyde +
Footnote †: journal: Applied Physics Letters
# Design and Analysis of Dual-Band High-Gain THz Antenna Array for THz Space Applications
###### Abstract
The use of the antenna array can provide many benefits in different working conditions, such as the control of electromagnetic radiations and the provision of high directivity [24]. For wireless communication in the THz frequency band, many papers were reported in the literature, such as in [25], where the authors presented a casegrain-based horn antenna that provides a high gain of 25 dB at 0.3 GHz, but the size of the antenna was not small. Similarly, the authors in [26] reported a 15-element antenna array providing directivity and radiation efficiency of 11.71 dBi and 70.8%, respectively, while operating at 0.3 THz. Similarly, the authors in [27] presented a PBG-based microstrip patch array antenna operating at 0.6 THz and providing gain, directivity, and radiation efficiency of 16.88 dBi, 17.19 dBi, and 89.72%, respectively. However, the authors of [28] presented a PBG-based microstrip patch antenna array while operating at 0.65 THz and providing gain and radiation efficiency of 11.60 dB and 86.75%, respectively In addition to it, a high-gain Terahertz microstrip antenna array was proposed while operating at 0.1169 THz and providing gain, directivity, and radiation efficiency of 19.6 dB, 21.6 dBi and 67%, respectively [29]. Furthermore, the authors in [30] presented work on a graphene-based microstrip patch antenna array, working at 0.8883 THz and offering gain, directivity, and radiation efficiency of 14.6 dB, 15.5 dBi, and 83.67%, respectively. Similarly, the authors of [31] reported a Sub-THz 4 \(\times\) 1 array antenna, operating at 0.3 THz and providing gain and efficiency of 13.6 dB and 89%, respectively. According to the work presented in [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37], all the authors attempted to present a THz antenna, but they were not able to present a high-gain antenna with wide bandwidth in terms of any targeted application. In Table 1, the results are compared with the simulated results of the past reported work in the literature. In our current work, all the elements of the plannar antenna array are impedance-matched with waveguide feed network impedance. For gain enhancement, an investigation is carried out to study the effect of a multiple-order antenna array, e.g., 2 \(\times\) 1, 4 \(\times\) 1 and 64 \(\times\) 1 setups are introduced to accomplish an exceptionally mandated pattern of 24 dBi directivity, to empower the proposed antenna for wireless transmission for space applications. The remaining parts of this paper are organized in the following sections. Section 2 depicts the design of a dual band novel shape antenna. Section 3 presents the geometry of the dual-band THz antenna array. Section 4 presents the simulation results augmented with the discussion, and, finally, Section 5 concludes the presented work.
# Detection and Classification of Human Activity for Emergency Response in Smart Factory Shop Floor
## 2 Related Works and Background Information  
### Safety of Persons Working in a Smart Factory Shop Floor  
The introduction of Industry 4.0 technology, such as IIoT, has undoubtedly resulted in a reduction in accidents and injuries in factories [16]. IIoT sensors have been extensively utilized for the acquisition of real time data regarding the status of machines and the factory environment, including temperature, vibrations, and pressure. Such data allow human supervisors to take preventive and corrective decisions. In the same vein, robots are being utilized to serve as assistants to human workers, especially, where risky tasks are to be undertaken. This will help to eliminate or reduce accidents, such as falling from a great height, inhaling toxic gas, or getting trapped in between machines [17]. Similarly, automated guided vehicles help in the navigation of factory floors, thus helping in the movement of weighty items from one part of the factory to another, without posing any risk to the humans working. Potential risks could include falling, tripping or being struck by heavy objects. In order to improve the training of workers on the appropriate occupational safety measures to take, augmented reality has been employed [18]. This is used to educate workers through the use of simulations which are able to accurately mimic real life situations. The use of biomedical sensors can also be used in keeping track of the worker's physical activities, especially critical and extreme environmental conditions, such as hot rooms and places with high toxicity. In such places, there is the need for emergency evacuation of workers, to reduce mortality and fatality rate. Thus, it is important to have sensors which can constantly track features, such as heart breathing rate and extreme vibration, and send the necessary data to a supervisory unit, if a predefined threshold is crossed [19]. The authors in Reference [19] proposed industry 4.0-based Bluetooth beacons attached to the worker's safety helmet to track workers entering danger zones and estimate the positions of workers in the event of an accident.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 20 Energy Optimization in Wireless Visual Sensor Networks While Maintaining Image Quality
### Formulation  
Using algebraic operations on the equations of FOV and DOF, the authors of [15] derive a convex optimization problem. A brief explanation of the formulation is as follows:  
* Decision variables: 1. \(\rho_{i}\in\{0,1\}\) selects decides on whether camera node \(i\) is selected to participate in the tracking process. The authors relax the binary constraint (\(0\leq\rho_{i}\leq 1\)) in the formulation and do not explicitly clarify how integrality is restored later. 2. The number of encoding bits \(N_{b_{i}}\) per pixel that camera \(i\) uses for image quantization. It is treated as a continuous variable in [15]. 3. the focal length \(f_{i}\) of node \(i\), which impacts the FOV and the DOF. It is a continuous variable.
* Objective function: Is the sum of the total energies, given in Equation (38), of each node \(\sum_{i}^{N}\rho_{i}E_{i}^{total}\).
* Constraint sets: 1. One camera node should be active in each scenario of target tracking, therefore, \(\sum_{i}^{N}\rho_{i}=1\). 2. Linear constraint sets in the \(f_{i}\geq\rho_{i}F_{min}\). 3. Bilinear constraint sets \(f_{i}\rho_{i}\leq F_{max}\). 4. The encoded image variance \(\sigma^{2}_{out}\) should be greater than a threshold \(\sigma\)th. This is represented by a nonlinear constraint in \(N_{b_{i}}\) and \(\rho_{i}\), given as: \[\rho_{i}e^{-0.06d_{i}^{target}}\left(0.03N_{b_{i}}^{0.4}+0.91\right)\geq\rho_ {i}\sigma^{2}_{th}\] (44)
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 2 Materials and Methods
### Machine Learning Algorithms
#### 2.3.2 Artificial Neural Network  
ANN is an information processing model that resembles the structure and function of neurons in the brain and consists of a large number of neurons built on top of each other [32]. As shown in Figure 3, ANN is composed of the input layer, hidden layer, and output layer. The role of the input layer is to accept the input data and pass the data into the hidden layer; the role of the hidden layer is to access the data of the input layer and then train to build the model. The role of the output layer is to access the data of the hidden layer, and then calculate the final output value [33]. The role of the output layer is to access the data of the hidden layer and then calculate the final output value.  
ANN has many advantages, such as dealing with complex nonlinear data, utilizing parallel distributed training, adaptively adjusting the weights and parameters of the network, and learning deeper features. However, ANN also has many problems. For instance, it is easy to fall into the local optimum. In addition, too many hyperparameters lead to difficulty in tuning, and it is quite sensitive to noise and outliers. Therefore, before choosing an ANN model, it is crucial to consider its advantages and disadvantages and weigh them according to the needs of the problem.  
BP (Back Propagation) neural network is one of the most used neural network models in engineering prediction [34]. The BP algorithm is used to backpropagate the error between the output value of the forward propagation and the actual value, and it continuously adjusts the weights and thresholds through multiple iterations and gradient descent methods to minimize the error.  
Figure 3: Schematic diagram of ANN.
###### Abstract  
With the rapid progress of hardware and software, a wireless sensor network has been widely used in many applications in various fields. However, most discussions for the WSN node deployment mainly concentrated on the two-dimensional plane. In such a case, some large scale applications, such as information detection in deep space or deep sea, will require a good three dimensional (3D) sensor deployment scenario and also attract most scientists' interests. Excellent deployment algorithms enable sensors to be quickly deployed in designated areas with the help of unmanned aerial vehicles (UAVs). In this paper, for the first time, we present a three dimensional network deployment algorithm inspired by physical dusty plasma crystallization theory in large-scale WSN applications. Four kinds of performance evaluation methods in 3D space, such as the moving distance, the spatial distribution diversion, system coverage rate, and the system utilization are introduced and have been carefully tested.Furthermore, in order to improve the performance of the final deployment, we integrated the system coverage rate and the system utilization to analyze the parameter effects of the Debye length and the node sensing radius. This criterion attempts to find the optimal sensing radius with a fixed Debye length to maximize the sensing range of the sensor network while reducing the system redundancy. The results suggest that our 3D algorithm can quickly complete an overall 3D network deployment and then dynamically adjust parameters to achieve a better distribution. In practical applications, engineers may choose appropriate parameters based on the sensor's hardware capabilities to achieve a better 3D sensor network deployment. It may be significantly used in some large-scale 3D WSN applications in the near future.  
3D node deployment; wireless sensor networks; dusty plasma crystallization +
Footnote †: journal: Sensors
## 4 Results and Discussion
### 20 m Far-Field Distance  
Figure 14 demonstrates the error ratios of force coefficients and Table 16 illustrates the corresponding mean error ratios under different AOA ranges. The optimal simulations were performed by SA+200 formula, SST+NACA4, SST+NACA4, SST+NACA4, SST+400 formula and SA+400 formula, in order, and the related results are (0.00%, 0.00%), (0.78%, 1.38%), (2.80%, 1.22%), (4.98%, 1.11%), (5.75%, 1.54%) and (6.67%, 1.55%). At the range of (0\({}^{\circ}\), 2\({}^{\circ}\)), despite the \(C_{d}\) of SA+264 formula being the smallest, the \(C_{l}\) is relatively large. Considering the \(C_{d}\) and \(C_{l}\) comprehensively, the result of SST+NACA4 is optimum. At the range of (0\({}^{\circ}\), 8\({}^{\circ}\)), SA+132 formula obtains the minimum \(C_{d}\) while the \(C_{l}\) of SST+400 formula is the best. Due to the \(C_{l}\) being a magnitude greater than the \(C_{d}\), the optimal result is achieved by SST+400 formula. Similarly, at the range of (0\({}^{\circ}\), 10\({}^{\circ}\)), SA+400 formula performs best.
# Collaborative Unmanned Vehicles for Inspection, Maintenance, and Repairs of Offshore Wind Turbines
## 4 Redundancy for Collaborative UVs
### Redundancy Strategies  
In designing a redundant system, Failure Modes and Effects Analysis (FMEA) is initially carried out to identify the root cause of the potential failures [130]. Strategies of sensor fusion using two GNSS receivers with two IMUs have proven to be reliable in providing robust positioning for a UAV [138]. Other sensor fusion methods that involve a GNSS with visual-inertial odometry [135] and fusion with IMU and LiDAR [139] have also been reliable in providing stable localization and navigation of a UAV.  
Regarding collaborative UVs within the IMR OWT operations, since they are still in their infancy stage, the FMEA, to the best of the authors' knowledge, has not been carried out yet. However, through modelling and simulation studies of collaborative UVs for the IMR OWT operations, the FMEA as a potential resource for introducing redundancy can be carried out with proper validations.  
From a different point of view, strategic planning and deployment of multiple UVs may as well establish a redundant system within the collaborative system. As previously discussed in Section 3.2, a USV may provide additional GNSS guidance from a safe distance to increase the reliability of the UAV navigation. Hence, collisions can be prevented by addressing the issue of poor localization and navigation system. In the case of a malfunctioning UAV component, a possible redundancy strategy might be introduced by using a component replacement station which is placed on a nearby USV. However, a sophisticated mechanism would be required for this method. Furthermore, as discussed in Section 3.4, a collaborative communication system through a private ad hoc network may also reduce exposure to cyber-attack that usually occurs through penetration through an external gateway. Other redundant strategies through collaborative efforts can be further devised to address the failure mode associated with the implementation of collaborative UVs.