esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adop Fuzzy AHP Methods for the Forest Fire Risk Assessment and Outbreak Detection
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Fuzzy Set** & **Fuzzy Domain** \\ \hline Temperature (T) & \(\text{FS}_{\text{T}}=\{\text{low, medium, high, extreme}\}\) & [0, 100] \({}^{\circ}\)C \\ Humidity (H) & \(\text{FS}_{\text{H}}=\{\text{low, medium, high, extreme}\}\) & [0, 100] \(\%\) \\ Wind speed (\(\text{W}_{\text{speed}}\)) & \(\text{FS}_{\text{W}}=\{\text{low, medium, high, extreme}\}\) & [0, 240] km/h \\ Rainfall (R) & \(\text{FS}_{\text{R}}=\{\text{low, medium, high, extreme}\}\) & [0, 100] mm/m\({}^{3}\) \\ Oxygen (O\({}_{2}\)) & \(\text{FS}_{\text{O}2}=\{\text{low, medium, high, extreme}\}\) & [0, 30] \(\%\) \\ Carbon dioxide (CO\({}_{2}\)) & \(\text{FS}_{\text{CO}2}=\{\text{low, medium, high, extreme}\}\) & [0, 1000] ppm \\ Carbon monoxide (CO\({}_{2}\)) & \(\text{FS}_{\text{CO}}=\{\text{low, medium, high, extreme}\}\) & [0, 100] ppm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Fuzzy sets and fuzzy domains proposed for linguistic variables. Figure 3: Membership functions of chemical gases, adopted from [57]. The proposed fuzzy inference system has two output variables, i.e., forest fire risk estimation and forest fire outbreak detection for the fire prevention and detection purpose, respectively. In order to prevent fire occurrence, fuzzified values of the last measurement and its average for meteorological variables (temperature, relative humidity, wind speed, and rainfall) are compared with the aim of evaluating the existence and severity of forest fire risks in the particular zone. Therefore, the considered output linguistic variable is the existence of forest fire risks expressed as \"nonexistent\", \"low\", \"high\", and \"extreme\". In order to detect fire occurrence, fuzzified concentrations of chemical gases (oxygen, carbon monoxide, and carbon dioxide) are compared with the respective average values in order to evaluate the probability that the fire outbreak recently occurred. The same output linguistic variables were used to calculate the probability of fire detecting as for fire prevention. After application of the corresponding inference rules, the results obtained for the input linguistic variables are aggregated into two output fuzzy sets and fuzzified into the output membership functions showed in Figure 4.  
Both output fuzzy sets are defuzzified with the centroid method [59], which calculates the center of mass for the obtained output distributions. Hence, a non-fuzzy discrete percentage of the forest fire risks and fire outbreak occurrence represents the result for the fire prevention and detection tasks, respectively.
# MIMO Signal Detection Based on IM-LSTMNet Model
## 4 Conclusions  
In this paper, we propose a novel framework, IM-LSTMNet, for detecting signals in an FrFT-based MIMO-OFDM system. The IM-LSTMNet network, combining CNN and LSTM neural networks with a Squeeze-and-Excitation Network in the attention mechanism module, was trained offline using simulation data from an FrFT-modified MIMO-OFDM system. This approach demonstrates the deep neural network's ability to handle the complexities of wireless channels characterized by severe distortion and interference.  
Our experimental results confirmed that IM-LSTMNet significantly enhances signal detection accuracy in an FrFT-based MIMO-OFDM system, outperforming traditional methods such as zero forcing (ZF), minimum mean square error (MMSE), simple LSTM, and CNN-LSTM networks. This demonstrates the deep learning model's ability to remember and analyze complex wireless channel characteristics, proving its potential for practical applications.  
For real-world deployment, it is critical that the IM-LSTMNet network exhibits robust generalization capabilities, ensuring effective performance even when online conditions differ from those during the training phase. The findings suggest substantial potential for improving the reliability and efficiency of 5G and other advanced wireless networks.  
Future work will involve more rigorous analysis and comprehensive experiments to further validate our results. Additionally, we will extend the IM-LSTMNet approach to a larger-scale Massive MIMO-OFDM system, combined with Spatial Modulation and Joint Constellation by additive superposition, which could further enhance communication performance and meet the increasing demands of modern wireless networks [23]. This work presents a promising advancement in MIMO-OFDM signal detection by introducing a deep learning-based solution that effectively addresses the complexities of wireless channels.  
Methodology, J.L.; Writing--original draft, Y.Y.; Writing--review & editing, X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Chun Hui plan project of the Ministry of Education, China (Grant No.2011089), and Graduate Discipline Competition Project of Xihua University (Grant No.RC2400002114).  
Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.9 REC Domain  
Signal receiver (REC)/CheY-like photo-acceptor domains are the widely distributed regulatory domains in bacteria (CheY, OmpR, NtrC, and PhoB), however, they are now also reported in eukaryotes, for example, ETR1 from _Arabidopsis thaliana_. In the bacterial two-component regulatory system, the response regulator typically consists of a receiver domain that is covalently linked to an effector domain (DNA binding or catalytic units), and which is controlled by sensor kinase-catalyzed aspartyl phosphorylation [117]. The role of the REC domain is to receive the input signal perceived and transmitted from the sensor partner in the two-component systems. The REC domain interacts with different proteins to regulate processes like bacterial chemotaxis and some other regulatory pathways [118].
# Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
###### Abstract  
Biochar has recently aroused great interest for the restoration of contaminated soils since it improves soil properties and induces the immobilization of pollutants. This study evaluates the use of biochar from plant pruning, applied as an amendment, for immobilizing metal(loid)s in a highly contaminated soil as well as for reducing the phytotoxicity of these pollutants by promoting natural revegetation. For this purpose, a bioassay with _Trifolium pratense_ L. was used to test the effectiveness of the soil amendment in greenhouse conditions. Three treatments were carried out including soil contaminated with metal(loid)s (RA), and this soil was amended with biochar at different dosage: 4% (RA4B) and 8% (RA8B). A non-contaminated soil (NC) from a nearby area not affected by contamination was used as a control. The results show that biochar increased soil pH by several units depending on the dose used, 8% being the most effective one. Biochar treatments also reduced soluble and bioavailable forms of Zn and Cu. Likewise, phytotoxicity was significantly reduced, promoting seed germination and biomass with plant growth values similar to the non-polluted soil. In light of the results obtained, the evaluation of the bioremediation potential of biochar under field conditions can be considered.  
biochar; phytotoxicity; metal immobilization; _Trifolium pratense_; soil bioremediation Article  
Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
# Formal Safety Assessment and Improvement of DDS Protocol for Industrial Data Distribution Service
## 6 New Scheme of DDS Protocol
### Scheme for Protocol New Reinforcement  
As shown in Figure 14, the improved message flow diagram is as follows:  
Step 1: Participant [PERSON] sends its own certificate CertM, a random number Nm and a timestamp to participant [PERSON]. [PERSON] verifies the validity of the certificate after receiving the request message from [PERSON];  
Step 2: After [PERSON] verifies that the certificate information is legal, [PERSON] sends its own random number Ns and its own certificate CertS to [PERSON], and signs the random number Ns with the private key; at the same time, it generates a pre-shared secret key and encrypts it with the public key. After [PERSON] receives the message from [PERSON], it verifies the validity of the certificate and signature information;  
Step 3: [PERSON] decrypts the received message and generates a pre-shared key and compares it with the pre-shared key generated in the previous step. If it is not equal, it indicates that it is safe and combines to generate a shared key K. The message includes public key encryption shared key K, signature information, a counter Num and a timestamp information. After receiving the information from [PERSON], [PERSON] verifies the validity of the message. If the message is valid, reply to the message; otherwise, end the communication.  
Step 4: [PERSON] receives the message from [PERSON] and verifies whether the shared key K is the same. If K is the same, it means that the message is legal, and the counter is incremented by 1 to complete the identity authentication process.  
Figure 14: Model of the new scheme message flow.
## 1 Introduction
To overcome this limitation, several differential correlation based PSS detection schemes have been presented using the frequency-domain PSS [15], wherein the IFO is estimated in parallel with the search process of PSS. As for the IFO, it can be detected in a joint or sequential way. The works in [15; 16] estimate the IFO jointly during the PSS-matching process, which is accomplished performing the frequency-domain shift of the received PSS. Although the maximum likelihood (ML) estimator can achieve optimal performance in detecting the IFO [17], the complexity in realizing the ML solution is prohibitively high. The differential correlation based methods with reduced complexity were proposed in [18], wherein the IFO detection and SID recovery are performed in a decoupled manner using the central-symmetric nature of the PSS. Such a design can divide joint search space of a number of hypotheses into two reduced search spaces, while its performance is vulnerable to residual STO. The synchronization task is one of the most complex parts of the baseband processing load in a typical LTE-A device [19]. Specifically, the LTE-MTC introduces new challenging tasks to the system such as narrow-band operation, low-cost operation, and CE. To satisfy these requirements, existing cell search and initial synchronization schemes have to be modified [9]. Thus, it is crucial to develop a reduced-complexity cell search method that can minimize the processing power of the MTC UE during the initial cell search process. To address this issue, a reduced-complexity joint cell search and frequency detection scheme are proposed for MTC over the LTE network. The conjugate property of the PSS sequence facilitates reducing joint search space to a small number of hypotheses. In the proposed method, a combination of the conjugate property and average estimate over multiple subframes achieves enhanced joint IFO and SID detection for the MTC UE in poor coverage conditions. It is demonstrated via simulation results that the proposed scheme performs initial cell search with significantly lower computational complexity while having almost similar detection performance to the existing estimation method. The reminder of the paper is organized as follows. Section 2 presents the signal model in the LTE-MTC system. The existing joint estimation schemes adopting differential correlation are introduced in Section 3. In Section 4, the proposed joint estimation method is presented for LTE-MTC, and its performance is analyzed. The usefulness of the proposed method is verified via computer simulations in Section 5, and a conclusion is given in the last section. The notations used in this paper are listed as follows: \((\cdot)^{*}\), \(|\cdot|\), \((\cdot)^{I}\), and \((\cdot)^{Q}\) denote the operations taking the complex conjugation, absolute value, real part, and imaginary part of the enclosed complex quantity, respectively. The notation \(\mathcal{M}\sim\mathcal{N}(\mu,\sigma^{2})\) means that \(\mathcal{M}\) is a Gaussian random variable (RV) with mean \(\mu\) and variance \(\sigma^{2}\).
###### Abstract
Other than the prediction of a fault, the diagnostic system should also give an estimate of how serious it is for the appropriate action to be taken. For this reason, the so-called Anomaly or Outlier Scores extracted by the selected methods are exploited. Moreover, for a specific diagnostic task, many models may fit and perform well, so a combination of them may yield to better results, improving the overall performance of the diagnostic system. For this reason, simple ensemble approaches, like Majority Voting and Mean Ensemble [18], which combine independently the predictions and scores from the used models, are employed.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 4 Simulation Results and Discussion  
### Simulation Setup  
The study implemented a modified MATLAB(r) function that uses a controlled elitist genetic algorithm variant of NSGA-II. This algorithm is a commonly used optimization algorithm for non-linear optimization problems. In this study, two of the optimization variables, the number of orbital planes and the number of satellites in each orbital plane, are integer variables. We must customize the algorithm to handle integer optimization variables. Due to these integer variables, the initial population and offspring generation functions, namely the mutation and crossover functions, are modified in the library implementation of the algorithm. The NSGA-II population size is set to 100, with a maximum generation of 500. Figure 4 shows the overall system simulation flow chart. The simulation time is assumed for three days starting from 1 April 2020 at 00:00:00.000.  
Various perturbation sources can affect the trajectory of the low Earth orbit (LEO) satellite. A perturbation can be defined as a deviation from normal or expected motion. Perturbation sources in LEO include Earth's nonspherical symmetry, atmospheric drag, Sun and Moon gravity, and solar radiation. The J2 perturbation value in Earth's nonspherical perturbation is much larger than other perturbations in magnitude [25; 26]. As the constellation is in lower Earth orbit, the J2 propagator model is used for the satellite propagator.  
This study considers two simulation cases, regional and multi-mission. For the first simulation case, regional optimization, the inclination is assumed to be 40\({}^{\circ}\) considering the target area. Moreover, three altitude values at 400 km, 450 km, and 500 km were considered for the constellation to see the effect of altitude change. The second multi-mission optimization simulation case includes both regional and global optimization objectives. The altitude is kept at 450 km for this simulation case to simplify the optimization problem. Table 2 shows the optimization parameters used for the regional and multi-mission simulation cases.  
Figure 4: Overall system flow chart for simulation.
# Characteristics of Collimators Based on the Large-Mode-Area CMCF for Coupling Laser Beam
## 2 Theoretical Analysis of the Coupling Loss Mechanism
### Angular Deviation  
Figure 5 shows the collimator coupling with only angular deviation. When \(\lambda\) = 1550 nm, \(\sqrt{A}\) = 0.326, and \(n_{0}\) = 1.5946, the angular deviation is converted into off-axis deviation by the transfer matrix, and the coupling loss would be obtained, which is shown in Equation (6). The relationship between the angular deviation and coupling loss under  
Figure 4: Coupling loss due to off-axis deviation.  
Figure 3: Off-axis deviation.  
different \(\omega_{0}\) is shown in Figure 6. The coupling loss \(\eta_{\theta}\) increases as the angular deviation enlarges; meanwhile, the larger the mode field diameter of the tail fiber is, the lower the loss of off-axis deviation under the same angular tolerance is [29].  
\[\eta_{\theta}=4.343\bigg{(}\frac{\tan\theta}{\pi n_{0}\sqrt{A\omega_{0}}}\bigg{)} ^{2} \tag{6}\]
## 5 Experiment and Results
### Evaluation of Level-2
#### 5.2.2 Accuracy of Images Processed  
The accuracy of the processing of images captured by drones was proposed in our previous work [65]. We processed the images received from the drones using this algorithm to detect the approximate number of vehicles from any given image. Figure 9 shows the accuracy of the algorithm designed to detect the level of congestion from the images captured using drones. The results indicate that the number of vehicles detected by the algorithms was close to the actual number of vehicles in all the experiment runs, yielding an average accuracy of 95%.  
Figure 8: Evaluation of the accuracy of tweet classification and detection of traffic congestion.  
Figure 9: Evaluation of the accuracy of the processing of images captured using drones for congestion detection.
## 1 Introduction
These single-joint movements may be considered boring and monotonous by some people during rehabilitation, as they may not relate to meaningful tasks [24]. Functional movements, on the other hand, involve multiple joints and muscle groups and are designed to mimic everyday movements and activities. Examples include throwing, reaching for objects, and walking [3]. In rehabilitation, functional movement training is preferred as it improves the ability to perform daily tasks and enhances recovery after stroke [25]. Furthermore, single-joint movement training during ePAS delivery has been shown to limit engagement and acceptability [26]. Therefore, for ePAS to be applied within a stroke rehabilitation setting, it needs to be incorporated into the functional movement tasks used in clinical practice, such as sit-to-stand and stepping [25], rather than being restricted to single-joint movements. The ePAS intervention has the potential to be applied during functional movements; however, there is limited knowledge about how the timing of the CNV might change under different movement conditions and how this might alter intervention efficacy. Thus, there is a need to investigate the characteristics of the CNV during functional movement tasks in preparation for testing the ePAS intervention within a stroke rehabilitation setting. In this study, the characteristics of the CNV are examined for two movement types: (1) single-joint movement (ankle dorsiflexion), and (2) functional movement (sit-to-stand). The second limitation of the clinical translation of ePAS is the visual cue used to prompt the participant to perform the required movement (e.g., ankle dorsiflexion). The current cue is entirely visual, designed for laboratory-based studies, and requires the participant's continuous attention. Although such visual cues are helpful in guiding and improving the execution of exercises during rehabilitation, some individuals may find visual cues dull and tedious if they are the only form of instruction provided. Furthermore, in our experience, many people with stroke find it challenging to focus on the visual cue for an extended period. Therefore, a more user-friendly audio-visual cue has been developed following iterative usability testing with physiotherapists and people with stroke [27]. This audio-visual cue provides multiple sources of information, and can help to keep an individual engaged and motivated during the exercise. However, the characteristics of the CNV elicited in response to this novel audio-visual cue are unknown. Thus, there is a need to quantify the properties of the CNV in relation to this novel audio-visual cue so that it can be used in future investigations of the ePAS intervention. Based on the above-mentioned limitations, further research is needed to understand how the CNV might change under different movement conditions relevant to rehabilitation. Therefore, this study aimed to investigate the characteristics of the CNV during (i) a functional sit-to-stand movement compared with a simple ankle dorsiflexion movement, and (ii) using a novel audio-visual cue compared with a simple visual cue.
## 4 Results and Discussion
### FCAM-Based Spatio-Frequency Explainability Results  
To provide deeper insight into the pain detection results, Figures 12 and 13 present the FCAM spatio-frequency relevance analysis results (see Equation (12)). Spatio-frequency topomaps are presented using Subjects 15 and 29 as a representative male and female case, respectively. Frequency bands include theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (30-60 Hz), and the full band (4-60 Hz). The FCAM relevance vectors are scaled from 0 to 1, which lets them pick out patterns that are different between the no-pain and high-pain detection classes.  
Finally, Figure 14 presents the corresponding KCS-FCnet-derived Gaussian functional connectivity patterns for representative subjects. As observed, these visualizations are consistent with the prior findings reported in [53], which revealed a direct association between pain perception and decreased activity in the alpha and beta frequency bands among male participants. Similarly, our results align with those in [54], where increased activation in the pregenual anterior cingulate cortex was strongly linked to the subjective unpleasantness of pain in female subjects. Collectively, these observations, supported by our FCAMs, underscore the relevance of specific EEG frequency bands in encoding pain-related neural signatures, thereby reinforcing their critical role in achieving accurate and interpretable pain classification.  
Figure 12: FCAM-based spatio-frequency results. We present Subject 15 as a representative male subject. Theta (4-8 Hz), alpha (8–13 Hz), beta (13–30 Hz), gamma (30–60 Hz), and full-band (4–60 Hz) are shown. FCAM relevance vector is normalized between 0 and 1, considering both no-pain and high-pain pain detection classes.  
Figure 13: FCAM-based spatio-frequency results. We present Subject 29 as a representative female subject. Theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz), gamma (30–60 Hz), and full-band (4–60 Hz) are shown. FCAM relevance vector is normalized between 0 and 1, considering both no-pain and high-pain pain detection classes.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
###### Abstract  
Real-time kinematic (RTK) positioning of the global navigation satellite systems (GNSS) is used to provide centimeter-level positioning accuracy. There are several ways to implement RTK but a Kalman filter-based RTK is preferred because of its superior capability to resolve GNSS carrier phase integer ambiguities. However, the positioning performance of the Kalman filter-based RTK is often compromised by various factors when it comes to determining a precise relative position vector between moving unmanned aerial vehicles (UAVs) equipped with low-cost GNSS receivers and antennas, where the locations of both GNSS antennas are not accurately known and change over time. Some of the critical factors that lead to a high rate of incorrect resolutions of carrier phase integer ambiguities are measurement time differences between GNSS receivers, frequent cycle slips with high noise in code and carrier phase measurements, and an improper Kalman filter gain due to a newly risen satellite. In this paper, effective methods to deal with those factors to achieve a seamless Kalman filter-based RTK performance in moving UAVs are presented. Using our extensive 45 flight tests data sets, conducted over a duration of 3 to 12 min, the RTK positioning results showed that the root-mean-square position error (RMSE) decreased by up to 95.13%, with an average of 65.31%, and that the percentage of epochs that passed the ratio test, which is the most common method for validating double differenced carrier phase integer ambiguity resolution, increased by up to 130%, with an average of 23.54%.  
Global Navigation Satellite System (GNSS); Kalman filter; real-time kinematic (RTK); moving baseline RTK