esa-sceva/satcom-chunk-collection huggingface dataset:
# MIMO Signal Detection Based on IM-LSTMNet Model
## 3 Experiments and Results of Signal Detection
### The Result of the Signal Detection
#### 3.2.2 The Effect of the Subcarriers' Number  
In the MIMO-OFDM system, the subcarriers' number will affect the system performance. In the experimental simulation in this section, the FrFT order is set to 1.6 and the CP to 16, and the influence of 64, 32, and 16 subcarriers on the signal detection performance of the FrFT-based MIMO-OFDM system is discussed. Figures 14 and 15, respectively, describe the performance comparison between our proposed IM-LSTMNet method and the traditional ZF algorithm and MMSE algorithm and the performance comparison between the IM-LSTMNet method and the simple LSTM and CNN-LSTM methods when the number of subcarriers is 64, 32, and 16.  
As shown in Figure 14, when the number of subcarriers K is 64, the improved algorithm proposed by us has a lower bit error rate than the traditional algorithm, and the signal detection performance of the FrFT-based MIMO-OFDM system is better. When the subcarriers' number K is 16, the improved algorithm and the traditional algorithm have higher BER and worse signal detection performance.  
Figure 13: The BER of IM-LSTMNet and other neural network algorithms at different orders.  
As shown in Figure 15, when the subcarriers' number K is 64, the improved algorithm proposed by us and the simpler CNN-LSTM and LSTM neural network algorithms have lower BER, and the FrFT-based MIMO-OFDM system has better signal detection performance. When the subcarriers' number K is 16, the improved algorithm, CNN-LSTM, and LSTM neural network algorithms have higher BER, and the system signal detection performance is worse.  
Compared with the results obtained in Figures 14 and 15, when the subcarrier number is 64, 32, and 16, the IM-LSTMNet's BER is lower than that of the traditional methods and the other two neural network algorithms, and the signal detection performance is better than that of the traditional methods and the other two neural network algorithms.  
In conclusion, the larger the subcarrier number K, the smaller the BER of MIMO-OFDM signal detection based on FrFT and the better the system performance. The smaller  
Figure 14: The BER of IM-LSTMNet and the traditional algorithms under different subcarrier numbers.  
Figure 15: The BER of IM-LSTMNet and other neural network algorithms under different subcarrier numbers.  
the subcarrier number K, the greater the BER of MIMO-OFDM signal detection based on FrFT and the worse the system performance.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 3 Navigation Signal Design Considerations  
One of the main challenges in RNSS signal design is that the payload power source and frequency resource are limited. Additionally, a new RNSS signal should guarantee compatibility with the existing RNSS and other systems. In other words, when designing a new RNSS signal, we have to consider ranging performance, power/frequency constraints, and compatibility with existing systems.  
However, it is not easy to evaluate these considerations through the practical RNSS signal transmission/reception chain at the initial signal design phase. Therefore, we conducted a performance evaluation and analysis on the signal candidates based on various FoMs, defined as a function of the signal design parameters, which enabled quantitative performance evaluation and comparison between the candidates [24]. This study focused  
Figure 1: Frequency allocation of the 2.4–2.6 GHz band in South Korea.  
on several signal design considerations, such as RF compatibility, spectral efficiency, ranging performance, and receiver complexity.
# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 4 Simulation Analysis
### Comparison of Optimization Effects in a Single Iteration  
To identify the most suitable heuristic algorithm for solving this problem, this paper sets the particle population size at 50 and the number of iterations at 200, as noted in Algorithm 1. These parameters were chosen based on preliminary experiments that indicated that they provide an effective balance between convergence speed and computational efficiency.  
Figure 4: Illustrative diagram of user generation.  
The proposed Discrete PSO is then compared with classic Simulated Annealing (SA) and the Genetic Algorithm (GA) in terms of average user satisfaction for 1000 users after a single optimization iteration, as illustrated in Figure 5. In this figure, error bars with confidence intervals are used to depict the standard deviation of the results, thus offering a clearer understanding of the variability in user satisfaction across different algorithms.  
The analysis reveals that while the computation time for all three algorithms is similar, the average capacity satisfaction differs significantly. The Discrete PSO performs the best, with an average satisfaction close to 1.0 and relatively low standard deviation, indicating consistent performance. The SA algorithm follows, achieving an average satisfaction of approximately 0.85, though with a slightly larger variation in results. The GA exhibits the lowest average satisfaction at around 0.65, with more substantial variability. Therefore, the Discrete PSO not only provides the highest average user satisfaction but also demonstrates more stable performance, making it the optimal choice for optimizing satellite capacity allocation in this context.  
Analyzing these results reveals that while ACO and the GA are established optimization techniques, they have notable limitations in dynamic satellite communication systems. Traditional Genetic Algorithms require substantial computational resources and lengthy convergence times due to their reliance on crossover and mutation, making them less adaptable to fluctuating user demands. On the other hand, Ant Colony Optimization, while effective for specific routing tasks, struggles with dynamic resource allocation in multi-satellite environments. Its performance can suffer from slow responses to sudden shifts in user demands and satellite visibility due to its pheromone updating mechanisms.  
Conversely, the Discrete PSO framework is explicitly designed to address these dynamic conditions with greater efficacy, thus enabling more rapid adjustments in user satisfaction and resource allocation. This adaptability positions Discrete PSO as a more efficient and reliable choice for real-time optimization of satellite capacity.  
Thus, the Discrete PSO not only achieves the highest average user satisfaction but also exhibits superior stability in performance, thereby establishing itself as the optimal methodology for optimizing satellite capacity allocation within this specific context.
## 3 KA-DDPG-Based Direct Torque Distribution Method
### KA-DDPG-Based Driving Torque Distribution
#### 3.2.2 Action Space  
For the drive configuration of the skid-steering vehicle described in this study, the vehicle forms a differential drivetrain, through four independently driven wheels. The wheels work in torque drive mode, and their control output is the driving torque of the wheel. Thus, the action space can be expressed as follows:  
\[\text{action}=\Big{\{}T_{fl},\;T_{rt},\;T_{fr},\;T_{rr}\Big{\}}. \tag{12}\]  
During the learning process, the agent learns from previous experience, by continuously interacting with its environment, and generates reasonable actions through the powerful fitting ability of the neural network. At the beginning of learning, the actions are randomly generated by the agent, which means that actions with low rewards are likely to  
Figure 4: Framework of KA-DDPG.  
be generated. Although the control performance of the low-fidelity controller cannot meet the requirements, it still greatly outperforms the control of randomly selected actions. Thus, the low-fidelity controller is used to assist the agent to learn quickly, by generating criteria actions; that is, it acts like a teacher when the agent has just started learning, guiding the direction of learning. The core idea of the criteria action method is to assist the agent in searching for actions at the beginning of learning, reducing randomly selected actions. The framework of the criteria action method is shown in Figure 5.  
The execution action \(a_{e}\) is generated by adjusting the agent action \(a_{a}\), with the help of the criteria action \(a_{c}\). The criteria action needs to be eliminated after assisting the agent for a period of time, such that the agent can learn to select reasonable actions independently. Thus, the execution action \(a_{e}\) is defined as follows:  
\[a_{e}=\left(1-\gamma^{i}\right)\cdot a_{a}+\gamma^{i}\cdot a_{c}, \tag{13}\]  
where \(\gamma\) is the discount factor, and \(i\) is the training episode. The execution action defined in this way is the same as training the agent with a low-fidelity controller at the beginning, then slowly reducing the assistance over time, until the assistance method has no effect on the agent's action selection, such that the agent selects the action independently.  
The low-fidelity controller helps to reduce randomly selected actions for the agent and provides a direction of learning for action selection. The low-fidelity controller defined in this study is expressed by Equation (14), which may be replaced by a suitable dynamics-based torque distribution method.  
\[\begin{split} T_{ff}&=T_{lr}=K_{p}\left(m\cdot \frac{\overline{v_{delta}}}{dt}-J\cdot\frac{\omega_{delta}}{dt}\right),\\ T_{rf}&=T_{rr}=K_{p}\left(m\cdot\frac{\overline{v _{delta}}}{dt}+J\cdot\frac{\omega_{delta}}{dt}\right),\end{split} \tag{14}\]  
where \(K_{p}\) is the proportional coefficient, and \(dt\) is the step size. The controller imparts the same driving torque to the wheels on the same side, which does not meet the requirements of actual control, but it is far superior to randomly selected actions.
## Appendix A  
Substituting Equation (3) into Equation (6), one can obtain an explicit form for heat generation from ultrasound:  
\[ \begin{split} q_{1}&=4A_{11}^{2}a_{1}\frac{\sin^{2 }(k_{2}L_{2})\big{\{}R_{2}^{4}\cos^{2}[k_{1}(x-L_{1})]+R_{1}^{2}R_{3}^{2}\sin^ {2}[k_{1}(x-L_{1})]\big{\}}}{QR_{1}}\\ &-2A_{11}^{2}a_{1}\frac{R_{2}(R_{2}^{2}-R_{3}^{2})\sin[2k_{1}(x- L_{1})]\sin(2k_{2}L_{2})}{Q}\\ &+4A_{11}^{2}a_{1}\frac{R_{2}^{2}cos^{2}(k_{2}L_{2})\big{\{}R_{3 }^{2}\cos^{2}[k_{1}(x-L_{1})]+R_{1}^{2}\sin^{2}[k_{1}(x-L_{1})]\big{\}}}{QR_{ 1}}\\ & q_{2}=4A_{11}^{2}a_{2}\frac{R_{2}\big{[}R_{3}^{2}\cos^{2}[k_{2 }(L_{1}+L_{2}-x)]+R_{2}^{2}\sin^{2}[k_{2}(L_{1}+L_{2}-x)]\big{]}}{Q},\\ & q_{3}=4A_{11}^{2}a_{3}\frac{R_{2}^{2}R_{3}}{Q},\end{split} \] (20a) with \[Q=R_{2}^{2}(R_{1}+R_{3})^{2}\cos^{2}(k_{2}L_{2})+\left(R_{2}^{2}+R_{1}R_{3} \right)^{2}\sin^{2}(k_{2}L_{2}), \tag{20b}\]  
Equation (5) and Equations (7)-(10) present a 1-D transient heat conduction problem with nonhomogeneous boundary conditions. The nonhomogeneous boundary condition in Equation (8a) can be overcome by shifting the temperature scale. By setting \(\Theta(x,\ t)=T(x,t)-T_{end}\), a new formulation with homogeneous boundary conditions can be found as follows. \[\frac{\partial\Theta_{j}(x,\ t)}{\partial t}=\alpha_{j}\frac{\partial^{2} \Theta_{j}(x,\ t)}{\partial x^{2}}+\frac{\alpha_{j}}{K_{j}}q_{j}(x,\ t),\ j=1,\ 2,\ 3,\] (21a) \[\Theta_{1}(x=0,\ t=t_{end})=0,\] (21b) \[\frac{\partial\Theta_{j}(x,\ t)}{\partial x}=0,\ \text{at}\ x=L_{1}+L_{2}+L_{Substituting Equation (A4) into Equation (A3a), one can obtain  
\[\alpha_{j}\frac{1}{\psi_{j}}\frac{d^{2}\psi_{j}}{dx^{2}}=\frac{1}{\Gamma}\frac{d \Gamma}{dt}=-\beta^{2},\ j=1,\ 2,\ 3,\] (A5)  
The separation given by Equation (A5) results in two ordinary differential equations for the determination of the time-dependent function \(\Gamma(t)\) and the space-dependent function \(\psi_{j}(x)\) in each layer. For parallel layers, the general solution of the space-dependent function \(\psi_{j}(x)\) in each layer can be obtained by solving the corresponding eigenvalue problem,  
\[\psi_{1}(x)=A_{n}\sin\left(\frac{\beta_{n}}{\sqrt{\alpha_{1}}}x\right),\ 0\leq x<x_{1},\] (A6a) \[\psi_{2}(x)=B_{n}\sin\left[\frac{\beta_{n}}{\sqrt{\alpha_{2}}}(L_{1}-x)\right] +C_{n}\cos\left[\frac{\beta_{n}}{\sqrt{\alpha_{2}}}(L_{1}-x)\right],\ x_{1} \leq x<x_{2},\] (A6b) \[\psi_{3}(x)=D_{n}\cos\left\{\frac{\beta_{n}}{\sqrt{\alpha_{3}}}[(L_{1}+L_{2}+L_ {3})-x]\right\},\ x_{2}\leq x<x_{3},\] (A6c)  
where \(A_{n}\), \(B_{n}\), \(C_{n}\), and \(D_{n}\) are unknown coefficients which can be determined from the boundary conditions in Equations (A3b)-(A3e).
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 2 Pull-Out Test
### Material Properties  
As depicted in Figure 3, steel fibers, accounting for approximately 6.78% of the mass fraction, are integrated into the ultra-high-performance concrete (UHPC) to enhance its ultimate tensile resistance. These steel fibers are in the form of straight lengths with a length of 14 mm, a diameter of 0.2 mm, and a tensile strength of 2200 MPa. The concrete's cubic compressive strength, denoted by \(f_{cu}\), is measured at 118.75 MPa. Correspondingly, the uniaxial compressive strength, represented by \(f_{c}\), stands at 95.00 MPa, which is 80% of \(f_{cu}\). The tensile strength of the concrete, determined using a specimen shaped like a dog bone and labeled as \(f_{t}\), is recorded at 10.38 MPa. For the reinforcing bars, the yield strength (\(f_{ry}\)) and ultimate tensile strength (\(f_{ru}\)) are 351.30 MPa and 691.50 MPa, respectively. In the case of the perfobond rib and its flange, the yield strength (\(f_{sy}\)) and tensile strength (\(f_{su}\)) are 384.67 MPa and 531.33 MPa, respectively.
## 4 Results and Discussion  
### Seasonal NDVI Profiles under Different Fertilization Levels  
Figure 3 shows the impact of fertilizer application treatments on NDVI for the five crops using six VHR images well distributed throughout the crop growing cycles and acquired after sowing. Millet NDVI is fastest to rise reflecting the vigorous establishment of the species. By late August differences between crops tend to reduce reflecting general canopy closure. Sorghum is the latest crop to establish with a delayed NDVI response. Sorghum showed a stronger NDVI increase with fertilizer applications than the other crops, clearly visible in late August. Millet displays an overall weak or no NDVI response to fertilizer application, as expected. Maize NDVI response to fertilizer is surprisingly weak throughout, indicated that ground cover was not strongly affected by the additional fertilizer applications. The highest apparent response to fertilizer application in early October is misleading and reflects differences in maize senescence. A fertilization effect is also observed for both cotton and peanut with the largest mean differences between plots on 4 October 2014 for cotton and 28 August 2014 for peanut.  
However, standard deviations calculated for a given crop type and fertilization level reveal large differences between fields. The impact of fertilization on the NDVI, as observed for the five crops from about the end of August until the beginning of October, seems of comparable magnitude when compared to the background landscape-scale variability, as expressed by the large standard deviation. Detection of fertilization levels at landscape level will thus be impossible without taking into account other factors affecting NDVI.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 3 IoHT Security Landscape
### Possible Points for Data Leakage
#### 3.2.2 Transient Data  
Transient data refers to the data on the move, as in the IoHT implementations, the information is transferred from devices to servers using different protocols and networking devices. Here, we discuss such devices and protocols from where the data can be stolen during transmission.  
Communication protocols are used to transmit data from an IoT device to a device. IoT devices are primarily lightweight, low computational, and battery-oriented. Low-range protocols with weak security features transfer data from IoT devices to a gateway device.  
Intermediate devices collect data from end devices using a specific protocol to aggregate and transform this data into another format that can be understood by the next device. A mobile phone or a PDA connected to the wearer acts like an intermediate device. These intermediate devices use multiple protocols to communicate with IoT endpoint devices, data servers, and monitoring devices. These protocols have security issues; IoT gateways, smart hubs, mobile phones, and WiFi devices enable IoT devices to connect with the Internet. These devices are limited in storage and are not used to store data; therefore, they forward it as it is received by changing its format. These devices communicate with IoT devices with different designs like LoRaWAN, ZigBee, Bluetooth, etc., but forward this collected data by using TCP/IP or UDP to the Internet.  
Access Points/Smart Hubs/IoT Gateways enable communication between IoT and data servers. They are not storage devices; however, they forward received data to the intended destination. Due to the contact with different types of IoT devices, these devices operate multiple protocols. Security features like encryption, hashing, and password checking are used with these protocols. However, there are no established guidelines to  
Figure 6: Possible Sites for Data Leakage in an IoHT implementation.  
comply with any healthcare laws describing the security levels needed for IoT healthcare intermediate devices.
###### Abstract  
In light of the high bit error rate in satellite network links, the traditional transmission control protocol (TCP) fails to distinguish between congestion and wireless losses, and existing loss differentiation methods lack heterogeneous ensemble learning models, especially feature selection for loss differentiation, individual classifier selection methods, effective ensemble strategies, etc. A loss differentiation method based on heterogeneous ensemble learning (LDM-HEL) for low-Earth-orbit (LEO) satellite networks is proposed. This method utilizes the Relief and mutual information algorithms for selecting loss differentiation features and employs the least-squares support vector machine, decision tree, logistic regression, and K-nearest neighbor as individual learners. An ensemble strategy is designed using the stochastic gradient descent method to optimize the weights of individual learners. Simulation results demonstrate that the proposed LDM-HEL achieves higher accuracy rate, recall rate, and F1-score in the simulation scenario, and significantly improves throughput performance when applied to TCP. Compared with the integrated model LDM-satellite, the above indexes can be improved by 4.37%, 4.55%, 4.87%, and 9.28%, respectively.  
LEO satellite networks; loss differentiation; ensemble strategy; heterogeneous ensemble learning +
Footnote †: journal: _entropy_  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)]([https://creativecommons.org/licenses/py/](https://creativecommons.org/licenses/py/)) 4.0/).
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 3 Materials and Methods
### Data Processing
#### 3.2.4 Geolocation  
The ultimate goal of the UAS application is to count the total number of olive trees and obtain the geographic coordinates of each tree. Each image is tagged with the position and attitude of the UAS at the time it was taken. We use direct georeferencing [52] to compute the tree's coordinates without the use of control points or aerial triangulation. It takes into account the effects of terrain elevation and slope using an iterative algorithm. Initially, the elevation of the target is estimated as the terrain elevation in the vertical plane of the UAV, and a first estimation of the coordinates is computed. Based on the estimated coordinates, a new possible value of the terrain elevation is obtained and used for the next iteration. For this, the digital elevation model (DEM) of the area is needed. The DEM file can be obtained from the national cartographic services ([[http://centrodedescargas.cnig.es](http://centrodedescargas.cnig.es)]([http://centrodedescargas.cnig.es](http://centrodedescargas.cnig.es))). The file size is 1.4 MB, and its content is a matrix with the corresponding elevation of the terrain on each cell with a resolution of 5 m per cell. These data are uploaded to the payload computer in advance to enable the on-board processing.
## 5 Results and Discussion  
### Flow Field Characteristics in the Micronozzle  
Figure 4 show the isolines of the Mach number (_Ma_) and temperature (\(T\)) in the fully expanded flow fields of the micronozzle. As can be observed in Figure 4a, _Ma_ keeps growing. In the convergent section, _Ma_ is less than 1 (_Ma_\(\approx\) 0.4), and it is a subsonic flow.  
Figure 3: The test of grid independence in the flow field of the micronozzle.  
Figure 2: Distribution of the dimensionless temperature of gas along micronozzle centerline.  
As the cross-section of the nozzle decreases gradually, the gas velocity increases, and \(Ma\) is about 1 at the nozzle throat. After the gas flow expands along the micronozzle in the divergent section, the gas velocity continues to increase with \(Ma>1\). The results suggest that \(Ma\), as well as \(T\), has the opposite changes (Figures 4 and 5). \(Ma\) is at the highest value around the nozzle outlet, where \(T\) is the lowest due to the thermodynamic energy being converted into kinetic energy in the flow fields. In accordance with the gas equation of state and the energy equation, the ideal gas flow velocity is restricted to \(U_{max}=c_{0}\sqrt{2/(\gamma-1)}\), where \(c_{0}\) is the speed of sound under stagnation conditions, as mentioned by [PERSON] and [PERSON] [24]. Figure 4a also shows a thick boundary layer in the diverging part of the nozzle, which is a typical feature of the small Reynolds number nozzle.  
The centerline Mach number and gas temperature distributions are illustrated in Figure 5. As \(T\) decreases, the viscosity coefficient of gas decreases, which hinders the development of a boundary layer and promotes gas expansion. Furthermore, the effect of rarefied gas increases owing to the decrease in the gas density. Therefore, the wall viscous effects on the nozzle flow increase and the boundary layer gradually becomes thicker, decreasing the velocity gradient near the outlet. \(Ma\) is approximately 4.2 in the nozzle outlet in Figure 5, which is near the theoretical value of the isentropic condition. It can be  
Figure 4: Isolines of Mach number and gas temperature in a fully expanded micronozzle: (**a**) Mach number; (**b**) gas temperature (K).  
Figure 5: Centerline Mach number and temperature distribution in a micronozzle.  
concluded that the boundary layer affects the Mach number distribution along with the change in temperature and gas density.  
The characteristics of the flow fields in the micronozzle are discussed above. There are considerable changes in the parameters, such as gas velocity and temperature, thus affecting the velocity and trajectory of submicron particles.
# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 3 Results
### 'Non-Convulsive Seizure' Subgroup  
For non-convulsive seizures, logistic regression yielded the best sensitivity and specificity. The mean F1 score, considering all features, was 0.79. When analysing the most significant variables, including channel means and greatest local differences in various transforms, the averaged F1 score was 0.83 with a standard deviation of 0.03. Sensitivity reached 81.5%, and specificity was 100%, as shown in the confusion matrix (Figure 3I).  
False positives and negatives mainly were from patients with brain haemorrhage, CET, and CRA, with a distribution between GS (46.7%) and FS (53.3%).