esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Link Budget  
It is important to mention that the magnitude of the pseudorange error is closely related to the signal strength such as the carrier to noise spectral density ratio (\(C/N_{0}\)). It is known that the typical \(C/N_{0}\) values of the GPS signals on the ground are about 37 to 45 dB-Hz [12].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite 1** & **Satellite 2** & **2 drms (m)** & **Mean HDOP** & **2 drms/HDOP (m)** \\ \hline PRN 01 & PRN 21 & 82.4 & 82.2 & 1.00 \\ \hline PRN 12 & PRN 25 & 117.1 & 112.0 & 1.05 \\ \hline PRN 16 & PRN 26 & 39.9 & 43.6 & 0.91 \\ \hline PRN 19 & PRN 17 & 113.3 & 102.6 & 1.10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MDPO horizontal position errors and mean HDOP values.  
Figure 5: Parallel baseline motion of two GPS satellites in different orbital planes.  
With the assumption of an antenna noise temperature of 290 K and isotropic antennas for both the transmitting and receiving ones, the \(C/N_{0}\) value can be computed from the following formula [13]:  
\[\frac{C}{N_{0}}\left[\mathrm{dB-Hz}\right]=P_{T}\left[\mathrm{dBW}\right]-20 \log_{10}\left(\frac{4\pi df}{c}\right)\left[\mathrm{dB}\right]+204.0\left[ \mathrm{dBW-Hz}\right], \tag{22}\]  
where \(P_{T}\) is the transmitted power, \(d\) is the propagation distance, \(f\) is the frequency, and \(c\) is the speed of light.  
As shown in Table 3, we assumed a low lunar circular orbit for the MDPO satellites. In this case, the signal propagation distance varies from 300 km at the zenith to about 1000 km on the horizon. For in situ lunar-based navigation systems, the frequency band of 2483.5 to 2500 MHz is recommended by Space Frequency Coordinate Group (SFCG) [14]. A simple link budget analysis in Table 5 shows that the expected \(C/N_{0}\) on the Moon can be comparable to that of the GPS signals on the ground with 0.2 W transmitted power, which is acceptable even for a small satellite.
# Towards Hierarchical Workflows in SysML to Support Virtual Validation of Technical Systems
## 4 Method
### The Framework of Virtual Testing Workflows Based on the Functional Architecture
#### 4.2.1 Modular Design of Virtual Testing Workflows  
Virtual testing workflows employ a modular design, breaking down the workflows into smaller components called modules. Each simulation activity serves as a module for performing the related domain model simulations. Simulation activities are nested according to the domain model's classification. In this work, domain models are categorized based on system scope, model purpose, and model fidelity [6]. The system scope describes the extent of the physical system that the domain model deals with. The model purpose describes the objective of the simulation for the domain model. The model fidelity describes the degree of exactness of the domain model simulation results compared to reality. These classifications are interconnected; for instance, a simulation activity associated with a particular system scope includes simulation activities that execute models with varying purposes.
## 2 Related Work and Foundational Concepts
### Software and System Development Life Cycles
#### 2.1.4 Spiral Method  
The Spiral methodology offers a hybrid approach, merging the iterative nature of Agile with the structure of Waterfall and placing a strong emphasis on risk assessment at each phase [15]. Its iterative cycles of planning, risk analysis, and evaluation make it well-suited for large, complex projects with high levels of uncertainty [4]. For highly classified systems like the Internet of Battlefield Things (IoBT), the Spiral model's intrinsic focus on continuous risk management is invaluable for adapting to changing mission requirements and threat landscapes.
## 4 Simulation Verification
### Verification of the Number of Relay Nodes  
The average gain of a communication system is a direct indicator of the network communication throughput capability [34]. The system gain of a cooperative communication retransmission signal is the degree to which the received signal agrees with the transmitted signal. A comparative experiment on the number of relay nodes was organized to evaluate the cooperative average gain by. The input of the experiment is the number of relay nodes and the output of the system is the system gain consistency. The cooperative communication simulation process consists of three processes: signal broadcast, signal forwarding and signal retransmission. Figure 5 shows that as the signal-to-noise ratio increases, the  
Figure 4: Simulation comparison of packet error rate and bit error rate of the two algorithms. (**a**) BER amplification comparison with SNR of 5 db. (**b**) BER amplification comparison with SNR of 10 db. (**c**) BER amplification comparison with SNR of 15 db.  
system gain also increases. Since the difference of the system gain is gradually decreasing, the signal-to-noise ratio is nonlinearly and positively correlated with the system gain.  
When the signal-to-noise ratio is 25 db, Figure 5c shows that the curves with relay nodes 1 and 2 fail to obtain the full system gain (0.999 and 0.998), and all other curves obtain the full gain. Additionally, when the signal-to-noise ratio is 20 db, Figure 5b shows that the curve fails to obtain the full system gain (0.999) when R is 3. When the signal-to-noise ratio is 15, the system gain is 0.99 times the amount of transmit signal gain when the relay node is 4 or 5. However, the two curves are very similar and the gain of the system does not increase much. Under the constraint of limited energy, four relay nodes transmit the signal with the maximum gain. The above conclusions are consistent with those of Refs. [34; 35]. According to the conclusions of references [36; 37], the total number of relay nodes and listening nodes cannot exceed five. Since the gain of the forwarding signal is greater than the gain of the retransmitted signal. It can be seen that there are at most three listener nodes and one relay node in coopera-tive communication. At this point, the gain of the communication system is maximum.
###### Abstract  
Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area - despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system.  
Keywords:soil; spectral dimensionality; spectral resolution; spatial scaling; Great Central Valley; California; AVIRIS; Landsat +
Footnote †: journal: Sensors
# Human Activity Recognition Using CSI Information with Nexmon
## 7 CSI Data Preprocessing and Feature Extraction
### Hampel Identifier--Outlier Removal for Experiment 1  
The CSI magnitude extracted contains noise and outliers caused by the various sources such as adaptive loading and AGC of the carrier frequency offset system nonlinearity. The noise causes spikes in the received magnitude, which can mislead as unexpected activities for the classification and result in inadequate machine learning and testing. The Hampel filter was applied to eliminate outliers and make the CSI data more reliable and smooth.  
Using the Hampel identifier, the Hampel filter block detects and excludes the outliers of the input signal. A variance of the three-sigma rule of statistics was implemented in the Hampel identifier, which is effective against outliers. For each input sample, it calculates the median absolute deviation (MAD) of the current sample and neighboring samples of size \((W_{len}-1)/2\) where \(W_{len}\) is the window length in either direction of the current sample [38]. The window length equal to 3 was chosen because this window length gave optimum removal of outliers.  
Let \(n_{\sigma}\) be the threshold value and \(\sigma_{i}\) the standard deviation of current sample. For a current sample \(CSI_{mag}\) (i), the algorithm worked as follows. It adjusts the window of odd length at the current sample \(CSI_{mag}\) (i) to calculate the local median for the current sample window of CSI data as depicted in Algorithm 2.  
```
0:\(CSI_{mod}\leftarrow\) local median of current window \((W_{len}=3)\) of \(CSI_{mag}\) (i)
0: Outliers removed as \(CSI_{ham}\)
1: Compare the current sample (i) with \(n_{\sigma}\times\sigma_{i}\)
2:if\(CSI_{mag}(i)-CSI_{med}(i)>n_{\sigma}\times\sigma_{i}\)then
3:\(CSI_{mag}(i)=CSI_{med}(i)\)
4:endif
5:\(CSI_{ham}\gets CSI_{mag}(i)\)
```  
**Algorithm 2** Outlier Removal  
Thus, when the if condition is true, it recognizes the current sample, \(CSI_{mag}(i)\), as an outlier and replaces it with the median value \(CSI_{med}(i)\) else it keeps it unchanged.  
It can be seen in Figure 6 that the outlier peaks and unsymmetrical magnitude variations are reduced compare to the preprocessed data.
## 3 Results and Discussion
### Identification of Aroma-Active Compounds by OAV  
The OAVs have been widely used to assess the aroma potency of foods by considering the balance between the food matrix and the surrounding air, as reported in previous studies [28; 29]. In this study, the OAVs of 83 volatile compounds with documented odor threshold values, as shown in Table 1, were calculated to evaluate their contribution to the overall aroma of dried jujube slices [30]. Thirty-one volatile compounds were excluded from the analysis due to insufficient odor threshold data. Among the remaining compounds, 17 were identified as aroma-active based on their OAV values, with a relatively high contribution to the overall aroma. The OAV contribution was dominated by esters (94.03%) in the B1 sample, with hexyl acetate, which had a strong fruity aroma, having the highest OAV of 278.48. Ketones (33.88-42.86%) dominated their OAV contribution in B3-B6 samples, three of which, with the strong roasted sweet-like 2,3-Butanedione aroma, had the highest OAV of 2.3-12.7. Furthermore, furanones and pyranones, typically produced by lipid peroxidation and Maillard reactions of carbonyl and amino compounds, can significantly impact the aroma profile of food [31]. However, in identifying aroma-active compounds, it is also essential to consider detection frequency analysis findings.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 2 Corrosion Mechanism and Experimental Study of Anchor Bolts
### Corrosion Theoretical Model of Anchor Bolts
#### 2.1.1 Electrochemical Corrosion Principle  
Due to the slow corrosion rate in natural environments, electrochemical corrosion methods are commonly used to accelerate the corrosion process [15]. The following reactions describe the principles of electrochemical corrosion.  
Anodic reaction:  
\[Fe\uparrow\to Fe^{2+}+2e^{-} \tag{1}\]  
Cathodic reaction:  
\[O_{2}+2H_{2}O+4e^{-}\to 4 OH^{-} \tag{2}\]  
The equation for the secondary reaction occurring in the anodic region is  
\[Fe^{2+}+2 OH^{-}\to Fe(OH)_{2} \tag{3}\]  
Unstable \(Fe(OH)_{2}\) will continue to oxidize in a humid environment and form \(Fe(OH)_{3}\) in the following reaction:  
\[Fe(OH)_{2}+O_{2}+2H_{2}O\to 4 Fe(OH)_{3} \tag{4}\]  
The presence of chlorides increases the hygroscopicity of anchoring agents, decreases their resistivity, and accelerates the corrosion of anchor bolts. Free chloride ions combine with the passive film on the surface of steel reinforcement to form water-soluble complexes, which react with alkaline substances to generate iron hydroxides and release chloride ions.  
Figure 1: Schematic of anchor bolt corrosion.  
Chloride ions act as catalysts in this process. The chemical reaction can be represented by the following equations:  
\[Fe^{2+}+2 Cl^{-}=FeCl_{2} \tag{5}\]  
\[FeCl_{2}+2 OH^{-}=Fe(OH)_{2}+2 Cl^{-} \tag{6}\]
## 3 Methods and Model
### The Architecture of MIER-MO-DQN  
The Deep Q-Network (DQN) was proposed by [PERSON] et al. [36]. It shares many similarities with the Q-Learning algorithm, but differs in that DQN simulates the action-value function \(Q(s,a)\) using a neural network. Real-world continuous control problems usually have enormous state spaces and action spaces, making it challenging to find solutions using the Q-Learning algorithm. However, the advent of DQN offers a fresh approach to dealing with such problems. Whether the action-value function \(Q(s,a)\) is a scalar or vector determines the difference between the the single-objective DQN method  
Figure 1: Reinforcement learning schematic.  
and the multi-objective DQN algorithm. When \(Q(s,a)\) is a scalar, the action with the largest action-value function is carried out by the greedy operator. Selecting the best action becomes a challenge when there are several objects to be optimized. \(Q\) will take on the form of a vector, and the action-value function is specified as \(\hat{Q}(s,a)\), \(\hat{Q}(s,a)=\{Q(s,a,o_{1}),Q(s,a,o_{2}),\cdots,Q(s,a,o_{m})\}\), where \(o_{i}\) and \(m\) stand for object \(i\) and the number of objectives, respectively.  
The paper creatively proposes a Multi-Objective Deep Q-Network based on multi-indicator experience replay to better resolve the problem of DQN applied to multi-objective optimization, whose network architecture is depicted in Figure 2. To simulate the action-value functions of various objects, we employ a set of DQNs, and each DQN only corresponds to one object. The environment sends the current state \(s\) to this group of DQNs, and each of them feeds the Mixed Evaluation Model with the action-value function, in order to determine which action to carry out and to add the experience to the Replay buffer. To reduce the discrepancy between the output of DQN and the true value, the batch of experiences is taken from the Replay buffer, and the Adam optimizer modifies the weights of DQNs.
# A Hybrid Multi-Agent Reinforcement Learning Approach for Spectrum Sharing in Vehicular Networks
## 2 Related Work
To address this, we propose a distributed system where each agent (vehicle) independently selects the optimal sub-band and power level for communication. This distributed approach aims to maximize network performance by eliminating the need for constant information exchange.
## 3 Hierarchical State-of-the-Art Control
### Overview of the Inner-Loop Control Methods
* _H-infinity control (\(H_{\infty}\))_: The primary task of the \(H_{\infty}\) controller is to inject a pure sinusoidal current to the main network in both linear and nonlinear loads even during grid voltage distortions. Reducing the disturbance effect and computation delay is vital to avoid instability in the system caused by the digital control delay. Therefore, active damping is presented in [65] by applying a capacitor current feedback into the controller design process. It not only increases the tracking efficiency but also is able to reduce the harmonic noise in current output. In [66], the study distinguishes the performance of reference signal tracking by showing a minimal number of errors. It is capable of adjusting to the system voltage and frequency equivalent to their nominal valuesafter system load variations. As a result, the \(H_{\infty}\) scheme offers a robust dynamic performance even in model uncertainties and unbalanced conditions, reduces tracking error, and has an easy implementation. According to the aforementioned, the comprehensive review on various power, current and voltage control strategies are summarized as advantages and shortcomings in Table 1. A microgrid designer needs to identify which potential method is adequate to suit the application. An efficient control application should provide robust dynamic performance.
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 1 Introduction
Various papers about 802.11 ax divide the scheduling problem into several steps [8]: splitting of the frequency band into resource units (RUs), assignment of these RUs to the STAs, and selection of the MCSs that the STAs shall use for data transmission in the allocated RUs. While performing these steps, an AP should consider the amount of traffic that the STAs can transmit, the channel quality between the AP and the STAs, and the utility function that determines service priority. Moreover, the AP should abide by the standard limitations related to the positions and sizes of RUs [4] and with the requirement that the power of signals from transmitting STAs should be equalized with an OFDMA transmission [9]. An important feature of the wireless channels that complicates the scheduling problem is the frequency-selective fading. With frequency selectivity, various parts of the frequency band experience different attenuation. Although frequency selectivity is neglected in many papers that study the schedulers for 802.11 ax, the lessons learnt from LTE optimization confirm that it is essential to design an OFDMA scheduler that can deal with it, assigning RUs with the most significant channel gain to each STA. Another problem often disregarded by the existing research is that the AP should know CSI for every STA and every sub-band to make proper resource allocation. However, perfect channel knowledge is not available in the real world, and a proper resource scheduler for OFDMA in Wi-Fi should consider that the AP might have outdated CSI. At the same time, most research related to OFDMA scheduling for 802.11 ax assumes that the AP has ideal CSI knowledge. The contribution of this paper is threefold. First, we develop a resource scheduler for OFDMA in 802.11 ax networks, which is efficient for frequency-selective channels. Furthermore, the scheduler considers the implementation-conditioned restriction that the power of the signals from the transmitting STAs should be the same at the AP. Second, unlike existing research on OFDMA scheduling for 802.11 ax, we consider the case when the CSI is updated at the AP with some period, investigate the impact of outdated CSI on network performance, and propose an algorithm for tuning available CSI estimates to match real channel conditions. Third, we evaluate the performance of the proposed solutions in a dense network scenario with multiple interfering APs. The rest of the paper is organized as follows. In Section 2, we observe the OFDMA basics in the 802.11 ax. Section 3 reviews the works related to the OFDMA scheduling in Wi-Fi. Section 4 contains a statement of the scheduling problem. In Section 5, we propose a greedy approach to solve the noted problem. In Section 6, we examine the power allocation issue and describe a CSI adaptation algorithm. Section 7 presents and discusses numerical results. Section 8 concludes the paper.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 4 Experiment
### Data Collection  
Different types of faults are introduced into the remote-controlled robotic arm system, with data recorded from three sensors in the follower. Real-time data are collected via Simulink, with a sampling interval of 0.02 s. In each fault scenario, real-time data from the follower robotic arm were collected over a period of 30 s, resulting in 1500 data samples per scenario. In total, 13,500 data samples were obtained across 9 different fault scenarios. Among these, 80% of the datasets were randomly selected for training, while the remaining 20% were used for testing in the experiments.
# Development of Additive Fibonacci Generators with Improved Characteristics for Cybersecurity Needs
## 4 Methods of AFG and MAFG Statistical Characteristics Research
### Research of MAFG Models  
MAFG models: Figure 1 shows generator structure scheme with using logic circuit LC. The hardware model of the MAFG, operating according to the following algorithm:  
\[A=2^{n}-p,\quad x_{i-5}=x_{i-4},\quad x_{i-4}=x_{i-3},\quad x_{i-3}=x_{i-2}, \quad x_{i-2}=x_{i-1},\quad x_{i-1}=x_{i},\quad x_{i}=x_{mp},\]  
\[a=b_{0\oplus}b_{1\oplus}\dots\oplus b_{s}\]  
\[x_{ad1}=(x_{i-5}+x_{i-4}+a)\bmod 2^{n},\quad\text{if}\quad(x_{i-5}+x_{i-4}+a)<2^ {n}\quad\text{then}\quad P_{ad1}=0\quad\text{else}\quad P_{ad1}=1,\]  
\[x_{ad2}=(x_{ad1}+A)\bmod 2^{n},\quad\text{if}\quad(x_{ad1}+A)<2^{n}\quad\text{then }\quad P_{ad2}=0\quad\text{else}\quad P_{ad2}=1,\]  
\[\text{if}\quad(P_{ad1}=0)\quad\text{and}\quad(P_{ad2}=0)\quad\text{then}\quad x _{mp}=x_{ad1}\quad\text{else}\quad x_{mp}=x_{ad2},\]  
where: \(b_{i}\)--values of the number xi binary bits.  
Abstract model of the MAFG operating according to the following equation:  
\[x_{i-5}=x_{i-4},\quad x_{i-4}=x_{i-3},\quad x_{i-3}=x_{i-2},\quad x_{i-2}=x_{ i-1},\quad x_{i-1}=x_{i},\quad x_{i}=x_{ad1},\]  
\[a=b_{0\oplus}b_{1\oplus}\dots\oplus b_{s}\]  
\[x_{ad1}=(x_{i-5}+x_{i-4}+a)\bmod p.\]  
Figure 4 shows the dependences of the current values of pseudorandom numbers, \(X\), that were generated by the MAFG on the iteration step number, \(i\), for the hardware and abstract model with the same initial value, \(X(0)\).  
Figure 4: Current values of pseudorandom numbers \(X\) (for MAFG): (**a**) hardware model: \(p=7\), \(n=3\), \(A=2^{n}-p=1\), \(a=b_{0\oplus}b_{1\oplus}b_{2}\), \(X(0)=1\); (**b**) abstract model: \(p=7\), \(a=b_{0\oplus}b_{1\oplus}b_{2}\), \(X(0)=1\).  
The results (Figures 4 and 5) indicate complete identity hardware and abstract models for forming the pseudorandom numbers sequence. Similar results were obtained for other \(p\) values, in particular for \(p\) values that are primes.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
###### Abstract  
This work presents an integrated thermal-electrical simulation model, capable of taking into account the thermal and electrical effects of the battery and photovoltaic panels for each instant of time in a given orbit and attitude. Using the physical equations that govern the thermal and electrical models involved during a CubeSat operation, the proposed integrated model can estimate the temperature and energy conditions of the battery, not only in an isolated way but also in considering the mutual effects on the system. Besides, special attention is given to photovoltaic panels used in the energy harvesting process, whose performance is affected by irradiance and temperature along the orbit. The integrated model can be useful for engineers when developing the subsystems of their CubeSats, taking into account, for example, the battery temperature control through a heater. Simulations were performed to illustrate the functioning of the proposed model with variations in the power requirements of its modules and the temperature of the battery throughout the orbit, and a heater's influence on it.  
cubesat; spacecraft energy harvesting; power management; heat transfer; thermal requirements; numeric simulation +
Footnote †: journal: Applied Sciences