esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controllability Criteria
### Maneuverability Requirement  
Recalling the previous discussion, setting the UAV at an emergent hovering mode and landing may not handle the causality in some situations. Nowadays, efficient, safe landing searching algorithms autonomously plan routes that need complex and precise maneuvers. To fully implement these algorithms, the system should have the ability to produce all the moments required to meet the designed mission profile and disturbance rejection. A given UAV system is said to be capable of performing the maneuver when the requirement lies below the maximum capability of the system.  
The designed mission trajectories can be converted into a sequence of control commands analytically or obtained from simulation and represented as the time history of moments (THM). Based on the nature of the operation, some maneuvers may not have the same relative control authority requisite in different moment directions. This work utilizes a statistical tool, standard deviation geometry (SDG), to define the weakest and strongestdirection and geometrically characterize the required moment. If equal control is required in all directions, such as in one of the situations considered in the previous section, the geometry term indicates spheroid. In contrast, if weighted control authority is desired, standard deviation ellipsoid (SDE) would be indicated.  
Suppose \(X\in\mathbb{R}^{3}\) is the trivariate Gaussian time history of moment data. By taking each point of the moment time series as an observation, the mean of the desired moment data can be calculated as:  
\[\overline{X}=\frac{1}{n-1}\sum_{i=1}^{n}X_{i} \tag{12}\]  
The covariance matrix of trivariate data \(X\) is expressed as:  
\[\text{c}=\frac{1}{n-1}\sum_{i=1}^{n}\bigl{(}X_{i}-\overline{X}\bigr{)}\bigl{(}X _{i}-\overline{X}\bigr{)}^{T} \tag{13}\]  
where \(\overline{X}\) is the mean value, and \(c\in\mathbb{R}^{3\times 3}\) is the symmetric and positive semi-definite matrix.  
A corresponding ellipsoid can be constructed with the inverse square root of eigenvalues, \(\lambda_{1}>\lambda_{2}>\lambda_{3}\), to be its principal semi-axes oriented by the corresponding eigenvectors.  
We can parameterize the ellipsoid as the image of the unit ball under an affine transformation as:  
\[\mathbf{g_{e}}=\left\{\mathcal{O}+\text{W}u\mid u_{2}\leq 1\right\} \tag{14}\]  
where \(\text{W}=c^{\
icefrac{{1}}{{2}}}\) is the symmetric and positive semi-definite matrix.  
In addition, SDG can be extended to assess the probability of randomly scattered moment points falling inside the scaled ellipsoid and its corresponding magnification factor. In this work, an efficient computation algorithm for the confidence level analysis of SDG is used from the work of [9]. As shown in Figure 2, the 3D data example shows the underlying idea of how SDG and confidence level analysis can be applied to later formulations of optimization problems.
# Fault-Tolerant Model Predictive Control Applied to a Sewer Network
## 5 Results and Discussion
### Training and Validation of the Neural Network  
Figure 4 represents the inlet flows to the sewer network collected in each of the catchment areas considered, due to precipitation and wastewater, over a period of 10 days (scenario 1). It shows the training data profile of the neural network extracted from the benchmark:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{Q}(k)\) & \(\mathbf{R}\) \\ \hline \(f_{i}=1,i=1,2,3\) & \(a_{i}=10,i=2,3\) & \(r_{i}=10^{-8}\), \(i=1,\ldots,5\) \\ \(f_{4}=10\) & \(a_{4}=100\) & \\ \(f_{5}=50\) & \(a_{5}=200\) & \\ \(q_{9}=10^{-6}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: MPC cost function weights.  
Figure 4: Catchment flow rates (scenario 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Units** & **Values** \\ \hline \(A_{1},\ldots,A_{5}\) tank areas & m\({}^{2}\) & 1188, 252, 348, 852, 2988 \\ \(c_{01},\ldots,c_{05}\) discharge coefficients & m\({}^{5/2}\)/d & 1.89, 0.40, 0.55, 1.36, 6.12 (\(\times 10^{4}\)) \\ \(h_{\text{max1}},\ldots,h_{\text{max5}}\) tank heights & m & 5 (for all) \\ \(h_{\text{min1}},\ldots,h_{\text{min5}}\) minimum levels & m & 0 (for all) \\ \(q_{\text{max1}},\ldots,q_{\text{max9}}\) maximum flow rates at the pipes outlet & m\({}^{3}\)/d & 23.64, 6 (\(\times 10^{4}\)) \\ \(T\) sampling time & d & 0.0021 \\ \(\tau_{1},\ldots,\tau_{9}\) link elements time constants & d & 0.0313, 0.0104, 0.0104, 0.0208, 0.0208, \\ \(u_{\text{max1}},\ldots,u_{\text{max5}}\) maximum flow rates at the reservoirs outlet & m\({}^{3}\)/d & 5.98, 1.27, 1.75, 4.29, 19.34 (\(\times 10^{4}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: System parameters.  
Figure 8: Tank levels generated by the neural network (scenario 2).  
Figure 7: Tank levels (scenario 2).  
It is found that the results provided by the network in both cases largely match those generated by the system, so the network can be used to generate normal operating data based on the disturbances affecting the system.
## 5 Experimental Results and Analysis
### Comparison with Other Heuristics Algorithms' Performance  
#### 5.2.1 Parameter Setting for the Heuristics Algorithm  
A series of parameters for the heuristics algorithms were determined experimentally through several tests. The population size and iterations had been tested in the previous literature [18]. In this paper, the population size was set as 50. The maximum evolution and mutation generation was set as 100 generations. Based on the computation results of several tests, the crossover probability of the population was set as 0.8. The mutation probability of the population was set as 0.2. The local search mutation probability was 0.1. The general GA and DE algorithms were designed based on the above algorithm without a local search strategy and double schedule strategy. The Memetic algorithm is designed by improved genetic algorithm with a local search strategy.
# Simulation of Electromagnetic Generator as Biomechanical Energy Harvester
## 2 Electromagnetic Generator Modelling
### Simulation  
ComsoL Multiphysics has been used to conduct the simulation work for the electromagnetic generator. This simulation is done by using the 3D rotating machinery features from COMSOL Multiphysics. There are some key simulation parameters used to carry out these simulations as shown in Table 4.  
In COMSOL Multiphysics modelling, Equations (2)-(4) are the general equations that COMSOL solves during the simulation process:  
Electromagnetism:  
\[\
abla\times\mathrm{E}=-\delta\mathrm{B}/\delta\mathrm{t} \tag{2}\]  
[PERSON]'s law:  
\[\
abla\times\mathrm{H}=\mathrm{J}+(\delta\mathrm{D}/\Delta\mathrm{t}) \tag{3}\]  
[PERSON]'s equation for time domain:  
\[\
abla\times(\mu^{-1}\
abla\times\mathrm{A})=-\sigma(\delta\mathrm{A}/\delta \mathrm{t}) \tag{4}\]  
where E is the electric field, D is the electric displacement, J is the current, H is the magnetic field intensity, B is the magnetic flux density, \(\mu\) is the material's permeability, \(\sigma\) is the material's conductivity, and A is the magnetic vector potential. In the process, to solve the Equations (2)-(4), critical information such as boundary conditions of source and destination, material properties, and material's nonlinearities are taken into consideration.  
Throughout the simulations, the RPM has been varied by three different speeds at 360, 450, and 800. The load value has been varied by two conditions at 10 ohms and 100 ohms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RPM** & **Load** & **Sector** & **Coil** & **Stator and Rotor** & **Neodymium** \\
**N-Turn** & **Material** & **Magnet** & **Magnet** \\ \hline
360, 450 and 800 & 10 Ohm and 100 Ohm & 8 & 600 & 35 PN230 & N52 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Key simulation parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Description** & **Value** & **Unit** \\ \hline S & Number of slots & 8 & \\ P & Number of poles & 8 & \\ R\({}_{1}\) & Inner radius of the rotor & 9 & mm \\ R\({}_{2}\) & Permanent magnet surface & 11.5 & mm \\ \(\mathcal{O}\) & Overall diameter & 20 & mm \\ \(\alpha\) & Permanent magnet sector angle & 32 & \({}^{\circ}\) \\ dm & Air gap thickness & 1 & mm \\ \(\beta/\delta\) & Slot opening to slot pitch ratio & 1 & \\ L & Thickness & 10 & mm \\ d & Coil wire diameter & AWG 24 & \\ dm & Air gap thickness & 1 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of electromagnetic generator.  
The number of sectors, number of coil turns, coil wire thickness, stator and rotor material plus material of the magnet are the same.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 3 Results and Discussion
### The Relationships Between Optical Constants and Mueller Matrix Elements
#### 3.1.2 The Relationships Between the Extinction Coefficient and Mueller Matrix Elements  
Figure 5 illustrates the variation in Mueller matrix elements with the extinction coefficient, \(k\), increasing from 0 to 10, while the refractive index remains unchanged at \(n=3.42\). The parameters \(\sigma\), \(\tau\), \(\lambda\), and \(\theta\) are all the same as those in Section 3.1.1. It can be seen that the values of \(m_{12}\) and \(m_{21}\) are the same, and they both increase with increasing \(k\). \(m_{33}\) and \(m_{44}\) are the same too. However, \(m_{34}\) and \(m_{43}\) are negatives of each other. It is worth noting that the values of \(m_{22}\) remain unchanged at 1.  
Figure 6 depicts the comparisons of the \(m_{12}\), \(m_{33}\), and \(m_{34}\) elements of rough surfaces and those of smooth plates, whose optical constants are the same as those of the rough surfaces. Figure 6a shows the curve correlating with \(m_{12}\) of rough surfaces is perfectly coincident with that of smooth plates. That means that \(m_{12}\) is not sensitive to rough surfaces, and from Equation (27), we know that the amplitude ratios of polarized light induced by rough surfaces remain unchanged as \(k\) changes within the studied range. Figure 6b shows that \(m_{33}\) of the Mueller matrix for both randomly rough surfaces and smooth surfaces gradually decreases with increasing extinction coefficient, \(k\). Notably, the decreasing rates of \(m_{33}\) for these two surface types are nearly identical, resulting in a nearly constant difference between their \(m_{33}\) values as \(k\) increases from 4 to 10. Figure 6c shows that the difference between \(m_{34}\) of the Mueller matrix for randomly rough surfaces and smooth surfaces remains nearly constant as \(k\) increases from 4 to 10.  
Figure 4: Comparison of the independent Mueller matrix element (**a**) \(m_{12}\); (**b**) \(m_{33}\); (**c**) \(m_{34}\) of rough surfaces and smooth plates as a function of the refractive index.  
Figure 5: The Mueller matrix of randomly rough surfaces as a function of the extinction coefficient. The refractive index remains unchanged at \(n=3.42\), and the morphological parameters are as follows: \(\sigma=0.05\) μm and \(\tau=0.1\) μm. The incident wavelength and angle are fixed at \(\lambda=6\) μm and \(\theta=65^{\circ}\), respectively.  
Figure 6: Comparison of the independent Mueller matrix element (**a**) \(m_{12}\); (**b**) \(m_{33}\); (**c**) \(m_{34}\) of rough surfaces and smooth plates as a function of the extinction coefficient.
## 2 Materials and Methods  
### Algorithm  
The existing automatic irrigation system used the algorithm shown in Figure 1 and required the user to input the soil moisture values used to trigger irrigation \"on\" and \"off\" [18]. It can be difficult for a user to determine the appropriate field capacity for the crop type, growth stage, soil texture, and soil bulk density. For this reason, the user may set the irrigation threshold criteria for the soil based on their own experience. The existing automatic irrigation system reduced the amount of labor required for irrigation and consistently managed the soil moisture. However, it would not be reliable if the irrigation threshold criteria were determined by the experience of the user; it needed data in order to improve the crop productivity and conserve water. Therefore, an algorithm, as shown in Figure 2, was developed that automatically provided the field capacity suitable for the cultivation conditions. The developed algorithm automatically determined the minimum and maximum values of suitable soil moisture, according to selected data such as the crop name, planting date, soil texture, and bulk density based on the crop type and the soil moisture for the growth stages stored in the database. The system could then determine whether to start irrigation by comparing the soil moisture value measured in real time with the starting value. At the start of irrigation, intermittent irrigation was performed by operating the pump for a set number of times and periods. The system then determined whether to stop irrigation by comparing the soil moisture value measured in real time with the stop value.  
Figure 1: The existing algorithm for irrigation.
## 4 Results and Discussions
The regression of communication signals employed an error assessment method that failed to satisfy operational requirements, resulting in an increased BER. In short, the MSE loss function improved how well the model worked and reduced the BER in 2D-CNN communication signal processing. We used the widely used optimizers AdamW, Adam, and SGD to evaluate their impact on the performance of 2D-CNNs. Figure 10 illustrates the BER comparisons across several optimizers. The AdamW optimizer trained the 2D-CNN, achieving a minimum BER of \(5.9\times 10^{-3}\), signifying optimal performance. AdamW mitigated model overfitting with a weight-decay method derived from Adam. It may adjust the learning rate for each parameter and dynamically modify the update step size based on the gradient and historical gradients to expedite convergence to the optimal solution while maintaining stability. Adam, without a weight-decay mechanism, exhibited overfitting with intricate data and had a high BER. SGD employed a fixed learning rate and updated step magnitude. Figure 8: Impact of various activation functions on the BER of 2D-CNN, with insets of recovered constellation diagrams for (**i**) Sigmoid, (**ii**) Tanh, (**iii**) Mish, and (**iv**) ReLU with an optical power of 8 dBm. Figure 9: The BER performance of the 2D-CNN utilizing various loss functions. Large datasets and complex models made it hard to reach the best solution and could become stuck in less optimal points, leading to a higher BER.
# Virtualization Airborne Trusted General Computing Technology
## 3 Key Technologies
### Virtualization Security Protection Architecture  
To address security threats such as malicious network attacks and unauthorized network access, a virtualization security multilevel protection architecture was designed based on the high-security hardware unit, as shown in Figure 3, which encompasses the virtualization layer security middleware unit of virtualization management layer, the virtualization security gateway of VM layer, and the VM security middleware unit within VMs.  
1. The virtualization layer security middleware unit consists of the VM traffic management module, the VM security loading module, and the interface protocol processing module. The VM traffic management module can acquire all traffic between VMs, between VMs and hosts, as well as from outside hosts. It diverts all the acquired traffic to the virtualization security gateway for traffic processing, and then forwards the gateway-processed traffic. The VM security loading module implements the secure and reliable loading of VMs through trusted measurement of the eigenvalues of the guest operating system kernel unit in the VMs. By communicating with the high-security hardware unit in the hardware facility layer, the interface protocol processing module of high-security unit offers trusted computing support for the VM security loading module.
2. The virtualization security gateway runs on the VM operation layer in the form of VMs, which implements security protection such as VM access control, intrusion prevention, and malicious code detection by processing the traffic acquired by the VM traffic management module.
3. The VM security middleware unit, which runs in the kernel layer of the VM guest operating system, provides the resident applications with an inter-VM security communication service through the system call interface. When multiple VMs are started, the VM management software unit starts loading in sequence, according to the ID number of the VM. During the loading process, first the VM security loading module is used to decrypt the image binary file of the VM, and then trusted measurement of the decrypted image file is implemented. When the trusted measurement of the VM passed, the VM image is loaded.
## 1 Introduction
As shown in Figure 4, they are classified into six types based on track design: single, dual, triangular, articulated, hybrid, flexible belt, and rigid metal tracks. Each type provides unique benefits in stability, maneuverability, and terrain adaptability, making them ideal for rugged environments. Commonly used in industry, search and rescue, and challenging terrains, they outperform wheeled robots on difficult surfaces. Designing decontamination robots involves choosing locomotion, navigation, and decontamination methods to operate efficiently in various environments. Challenges include customizing robots for specific tasks like cleaning surfaces or using different disinfection methods. To overcome these challenges, several researchers [12; 13; 14; 15; 16] explored the use of advanced artificial intelligence (AI) models, large language models (LLMs) like chat generative pre-trained transformer (ChatGPT 4o) and natural language processing (NLP), and other machine learning techniques, which leverage vast datasets of robot specifications. By processing user inputs like surface type, disinfection method, or robot size, these models analyze and select the best robot design for specific decontamination needs. Researchers are refining this technology to improve accuracy and adaptability, streamlining robot design and enabling easier adaptation for complex tasks in various settings. The remainder of this paper is structured as follows. Section 2 presents a comprehensive review of related studies, including robotic decontamination methods as well as various locomotion strategies. Section 3 introduces the proposed data-driven locomotion  
Figure 4: Classification of tracked robot configurations based on track design. Figure 3: Classification of active wheel-based robot configurations and their corresponding drive mechanisms. recommendation algorithm. Section 4 details the design of the decontamination robot. Section 5 demonstrates the prototype development of the designed decontamination robot by highlighting limitations and future scope in the proposed design. Section 6 concludes the study by summarizing the main contributions and findings.
## 5 Discussion and Suggestions  
1. This paper presents a one-time and one-way horizontal push and release test on the building, which is not representative of the building's reciprocating horizontal displacement during an earthquake. As a result, the parameters such as dynamic response obtained during the test are utilized as a benchmark for evaluating the performance of isolated structures.
2. The isolation layer can be reset almost instantly after the load is released. It can be inferred that when an earthquake occurs, the isolation layer will exhibit free attenuation movement and will promptly return to its initial position. This conclusion dispels any questions about the isolation bearing's ability to be swiftly reset.
3. Edge and corner isolation bearings exhibit greater horizontal deformation than center bearings during the initial displacement. Therefore, while designing structural members, the displacement amplification factor of edge and corner isolation bearings should be considered, or the displacement of the least unfavorable isolation bearing should be used as the design value.
4. The building has been put into service, and the reserved reaction wall will be kept for later isolation structure tests, as well as the natural aging and degradation performance of the isolation bearing.
# Efficient Mobile Vehicle Data Sharing Scheme Based on Consortium Blockchain
## 4 Problem Formulation and Solving
### Stackelberg Game Scheme Modeling
We use \(n\) to denote the vehicles that represent the data transactions between the buyer \(b_{i}\) and the buyer within the management range of \(RSU_{j}\), and if the set of them constitutes \(\{s_{1},\ s_{2},\ \ldots,\ s_{n}\}\), then the utility function of \(b_{i}\) is:  
\[U_{b_{i}}(p_{i})=\eta_{i}\sum\limits_{k=1}^{n}d_{k}-\beta_{i}p_{i}\sum\limits _{k=1}^{n}d_{k} \tag{2}\]  
where the total revenue \(\eta_{i}\sum\limits_{k=1}^{n}d_{k}\) of \(b_{i}\) is the buyer's purchase of data from the vehicle \(n\) within the management range of \(RSU_{j}\), and the income that the buyer \(b_{i}\) can earn from the unit data of the vehicle purchased. The total cost \(\beta_{i}p_{i}\sum\limits_{k=1}^{n}d_{k}\) of the transaction between the buyer \(b_{i}\) and vehicle \(n\) shows that the bid for all vehicles is exactly the same. According to the different amounts of data used by each buyer, the cost and performance gains are fully considered before the transaction, and the parameters \(\beta_{i}\) are balanced. In summary, to determine the buyer's bid price \(p_{i}\) and the amount of data with the maximum utility function, we have:  
\[\max_{d_{k}}U_{s_{k}}(d_{k})=\max_{d_{k}}\left(p_{i}D_{k}d_{k}-\alpha_{k}{d_{k }}^{2}\right)\] (3a) Subject to: \[d_{k}\leq D_{k} \tag{3b}\]In addition, given the amount of data \(d_{k}\) that the seller \(s_{k}\) is willing to trade, the buyer \(b_{i}\) determines the bid for the unit data based on the maximization of its utility function (2), as:  
\[\max_{p_{i}}U_{b_{i}}(p_{i})=\max_{p_{i}}\left(\eta_{i}\sum_{k=1}^{n}d_{k}-\beta _{i}p_{i}\sum_{k=1}^{n}d_{k}\right)\] (4a) Subject to \[p_{i}\leq p_{avg} \tag{4b}\]  
Through the above analysis, it can be seen that the average price \(p_{avg}\) of transactions made by the buyer \(b_{i}\) and other sellers in the past, \(p_{i}\leq p_{avg}\) means that the price at which the agent and the vehicle are transacted does not exceed the average price.