esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 2 Study Area and Methodology
### Model Evaluation  
Two machine learning performance indicators were used to evaluate the performance of regression models in this study, the Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Lower RMSE and MAE scores indicate better model performance. For classification model, prediction accuracy in percentage (%), precision, recall, and F1 score, and balanced accuracy are used as an indicator. The formulae for calculating each performance indicator are shown in Equations (10)-(15). These are the common indicators to quantify the machine learning model performance.  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}(p_{i}-\hat{p})^{2}}{n}}, \tag{10}\]  
\[\text{MAE}=\frac{\sum_{i=1}^{n}\lvert p_{i}-\hat{p}\rvert}{n}, \tag{11}\]  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}, \tag{12}\]  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{13}\]  
\[\text{Recall}=\frac{TP}{TP+FN}, \tag{14}\]  
\[\text{F1}-\text{Score}=2\times\frac{\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{15}\]  
where \(p_{i}\) is the actual value of an observed data point, \(\hat{p}\) is the predicted value, and \(n\) is the number of observed data points. For the accuracy calculation, \(TP\) is true positive, \(TN\) is true negative, \(FP\) is false positive, and \(FN\) is false negative.
## 3 Communication Technologies and Wireless Sensors Networks
### Wireless Sensors Networks Categorization
#### 3.1.3 Categorized by Applications  
* Health monitoring: WSNs play a crucial role in healthcare, employing advanced medical sensors to monitor patients both in hospital and at home. These WSNs facilitate real-time monitoring of vital signs through wearable hardware. The health applications of WSNs encompass patient-wearable monitoring, home assisting systems, and hospital patient monitoring.
* Urban: WSNs offer diverse sensing capabilities that open the door to obtaining extensive information about a specified area, whether indoor or outdoor. WSNs serve as a versatile tool for measuring the spatial and temporal characteristics of various phenomena within urban settings, presenting numerous applications. In the urban context, WSNs find widespread use in areas such as smart homes, smart cities, transportation systems, and structural health monitoring.
* Flora and fauna: The essential aspects of both plant life (flora) and animal life (fauna) are crucial for any nation. The primaries are greenhouse monitoring, crop monitoring,and livestock farming. The illustration also highlights the prevalent types of sensors commonly employed in these applications.
* Environmental: The use of WSNs can enhance environmental applications requiring constant monitoring in challenging and distant locations. This includes subcategories like water monitoring, air monitoring, and emergency alerting, each involving specific types of sensors. The subsequent subsection delves into the examination of WSNs designed for these environmental applications.
* Military: The military pioneered WSNs, with early research (such as Smart Dust in the late 1990s) aiming at creating minuscule yet efficient sensor nodes for espionage. Subsequent technological advancements expanded WSN applications in the military, with a focus on battlefield surveillance, combat monitoring, and intruder detection. Various sensor types are now commonly employed in these military WSN applications.
* Industrial: Industrial wireless sensor networks (IWSNs) present numerous benefits for facilitating the intricate and dynamic processes within industrial settings. Thanks to their effortless setup, unrestricted mobility, and smart data routing capabilities, IWSNs are emerging as a promising communication option for industrial applications [23; 24; 25; 26].
## Materials and Methods
### Burned Area Mapping Method
#### 2.3.1 Data Pre-Processing  
The MODIS data with 1-km spatial resolution were processed by geometric correction to the map projection of transverse Mercator and to obtain the geographic location information. Then we carried out radiometric calibration [28] and atmospheric correction [47] to obtain the data of reflectance and emissivity using ENVI software. Afterward, brightness temperature (BT) and LST data were generated by the split-window algorithm [48]. Finally, the spectral indices were obtained by combining the data of reflectance, emissivity and LST.
## 3 Methodology  
The proposed methodology is shown in Figure 5. In general, it consists of four stages. In the first stage, the signal segmentation in 1 min intervals of both databases is carried out as described in Section 2.1. In the next stage, one EMD-based method, i.e., EMD, EEMD, or CEEMD, is applied to each 1 min segment. Each of these algorithms is set to generate 6 IMFs, since after different tests it was observed that the last IMFs (i.e., the seventh IMF or superior) showed redundant (or nonsignificant) information, mainly in the EMD method due to its inherent operation; in addition, the number of IMFs had to be the same for the three methods in order to keep the same reference/size. At the image representation, the six IMFs are used to make an image through a surface graph; thus, each 1 min segment generates three images, i.e., one per each EMD-based method. Finally, in the fourth stage, the obtained images are used to train and validate the CNN for the prediction of an SCD event. In order to provide a suitable solution in terms of computational complexity and accuracy, different configurations of the CNN by including the input image size, number of filters, size of filters, number of epochs, and batch size were constructed and tested. In order to do so, a simple but complete CNN structure with random parameters is first used for the SCD prediction; then, each parameter was tested individually by using different values. The parameter with the best results is selected and considered for the next tests. This procedure is repeated up to reviewing all the previously mentioned parameters. In the end, the resulting CNN structure becomes an efficient structure in terms of accuracy and complexity. The equipment where these tests were carried out has the following hardware specifications: CPU @ 2.30 GHz, 16 GB RAM, and 64 bits operating system. The implementation software for the overall methodology is Matlab version 2022a.
# Enhanced Efficient EMT-Type Model of the MMCs Based on Arm Equivalence
## 6 Conclusions  
This paper proposed an enhanced computationally efficient model for MMC based on arm equivalence. The proposed AEM could accurately reproduce the dynamic responses in both de-blocked and blocked modes, and has significant computational efficiency, especially for the large-scale MMC-HVDC grids. Based on a two-terminal monopolar MMC-HVDC system built on the PSCAD/EMTDC program, the accuracy and efficiency of the proposed AEM was verified against the DEM and AVM, with several simulation scenarios, where both de-blocked and blocked modes were considered. The simulation results demonstrated that:  
1. The proposed model can accurately represent the dynamics of the arm currents and SM capacitor voltages compared to AVM. In terms of simulation efficiency, the proposed AEM is only about 45% slower than the AVM, and is more than three times faster than the DEM.
2. Compared to the DEM, the proposed AEM very accurately reproduces the dynamic behaviors of the DEM under different scenarios, and affords a substantial acceleration in simulation speed, with no loss of accuracy. Except for the dynamics of individual SMs, the proposed AEM could be suitable for various simulation scenarios with no loss of accuracy.
3. The proposed AEM consumes the same amount of execution time regardless of the number of the SMs per arm, thus it can be efficiently adopted for large-scale MMC-HVDC grids with high computational speed.
4. Although the simulation efficiency of AVMs is satisfactory, they lose accuracy compared to DEMs, especially for the blocked mode. Thus, the AVMs are suitable for system-level studies, which mainly focus on the steady-state dynamics and responses of AC fault.  
Thereby, the proposed AEM has a higher computational efficient with no loss of accuracy, which is desirable for studies of large-scale MMC-HVDC grids with large numbers of SMs.  
Investigation, writing: X.L.; Supervision: Z.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Number of SMs per Arm**} & \multicolumn{3}{c}{**CPU Time (s)**} \\  & **AEM** & **DEM** & **AVM** \\ \hline
150 & 8.1 & 18.4 & 5.6 \\
200 & 8.1 & 21.7 & 5.5 \\
250 & 8.2 & 25.1 & 5.5 \\
300 & 8.2 & 28.5 & 5.6 \\
350 & 8.1 & 31.8 & 5.5 \\
400 & 8.2 & 35.5 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Execution times for the three models.
# Fault Reconstruction for a Giant Satellite Swarm Based on Hybrid Multi-Objective Optimization
## 5 Conclusions  
In this paper, the problem of reconstructing a satellite swarm after faults occur is analyzed, and a reconstruction optimization algorithm based on hybrid multi-objective optimization is proposed. Taking the health state of the satellite swarm, the total energy consumption of reconstruction, and the balance of the fuel consumption as the optimization indicators, the optimal strategy of the fault reconstruction is solved using mixed multi-objective weighting and a genetic algorithm; then, simulation verification is carried out on a typical case. Finally, the effectiveness of the reconstruction algorithm is verified by Monte Carlo simulation. The simulation results show that as the number of population iterations increases, the fitness value will gradually approach the global optimum, and the curve shows convergence. Therefore, the reconstruction algorithm proposed in this paper provides the optimal reconstruction scheme for satellite swarms with different fault degrees. At the same time, the Monte Carlo simulation shows that the iteration of the algorithm has high precision, and the change in the health state of the satellite swarm conforms to the ponytail curve of the faulty spacecraft. When the number of faulty satellites exceeds 38%, the health state of the entire swarm becomes unstable. Therefore, a fault threshold can be given for faulty satellite swarms, which can be used to effectively manage satellite swarms.  
In the future, more reconstruction indicators will be introduced into the system to improve the overall efficiency of the satellite swarm. In addition, the efficiency of the multi-objective reconstruction planning algorithm will be improved, thereby increasing the practicability of on-orbit calculations.  
Conceptualization, G.K. and X.Y.; methodology, X.Y. and Z.Y.; software, X.Y. and Z.Y.; validation, J.W.; writing--original draft preparation, X.Y.; writing--review and editing, G.K. and Z.Y.; visualization, X.Y.; project administration, G.K.; funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# A Dynamic-Routing Algorithm Based on a Virtual Quantum Key Distribution Network
## 3 Routing Algorithms
### Routing Algorithm Design  
In the design of the trusted QKD network and the QKD routing algorithm, this paper sets the quantum key pool with adjustable size, that is, updates the size of the QKP corresponding to each link according to the link status and requirements and establishes the remaining quantum key mathematical model to ensure that there is always enough key to be used in the quantum key pool. Secondly, this paper uses the random walk model to monitor the blocking degree of each node, collects the blocking information of each node in the network, and estimates the link-blocking probability. The continuity and availability of QKD communication thus improves the performance of the QKD network. The procedure of the routing algorithm is shown in Figure 5.
# An Overview of GEO Satellite Communication Simulation Systems
## 3 Resource Management and Scheduling
### Spectral Efficiency  
One prominent class aims to maximize spectral efficiency, focusing on the optimal allocation of limited frequency resources to enhance overall system throughput. Considering the multi-beam GEO-LEO coexisting satellite networks, beam design and power allocation were jointly optimized to maximize the spectral efficiency of the LEO system under tolerable interference on the GEO system [19]. Specifically, a fast deep reinforcement learning (DRL) algorithm, integrating fuzzy logic, post-decision state, and deep deterministic policy gradient, was developed to solve the optimization problem. Considering the traffic offloading in integrated terrestrial 5G and GEO satellite networks, a traffic offloading algorithm was developed in [20] by leveraging the reference signal received power and celledge width criteria. Power allocation and user resource block scheduling were jointly optimized to maximize the network's spectral efficiency. In addition, in [21], a transmission capacity allocation algorithm based on Q-learning was proposed to optimize the long-term utility in a three-layer heterogeneous satellite network (i.e., GEO, MEO, and LEO).
# Action of Antimicrobial Peptides against Bacterial Biofilms
## 3 Biofilm Resistance to AMPs  
### Interaction with EPS  
It is thought that biofilm mediated resistance to AMPs is mainly due to their interaction with EPS, however the exact mechanism of interaction remained unknown in large number of cases [102]. Although most of the substances in EPS are negatively charged, the positively charged exopolymer PIA (which is composed of poly-\(N\)-acetyl glucosamine) can cause electrostatic repulsion of the cationic AMPs [103]. PIA protects _S. epidermidis_ and _S. aureus_ from the bactericidal actions of cationic AMPs such as LL-37 and human \(\beta\)-defensin [103]. PIA can also protect bacteria in biofilm from anionic AMP such as dermcidin (a human epithelial secreted) [102]. So, the role of PIA in protection of bacterial biofilms may be due to sequestration of AMPs along with electrostatic repulsion [102].  
Gram negative bacteria such as _P. aeruginosa_ secrete an anionic extracellular polysaccharide known as alginate which is made up of the uronic acid D-mannuronate and C-5 epimer-L guluronate [104; 105]. Alginate can interact with positively charge AMPs and protect _P. aeruginosa_ biofilm embedded cells from attack of AMPs [106]. Wild-type strains such as PAO1, PA14 (a mucoid cystic fibrosis strain), and FRD1 (a mutant which lacks alginate producing ability) can be easily killed by human leukocytes and their peptides within 4 h of exposure, [107] but became resistant in the presence of alginate [107]. Alginate can bind and induce an \(\alpha\)-helical conformation for AMPs such as magainin II and cecropin P1 which is similar to their interaction with cytoplasmic membranes, suggesting that alginate can mediate hydrophobic interactions with AMPs despite its hydrophilic nature [106]. Alginate can trap AMPs in hydrophobic microdomains which consist of pyranosyl C-H groups that are inducible upon formation of AMPs-alginate complexes due to charge neutralization between the two species [108]. However, with the exception of cystic fibrosis, mucoid strains of _P. aeruginosa_ account for only 1% of isolates from infections [109] so the role of mucoid strains in medical device related infections is limited. In contrast to mucoid strains, non-mucoid strains contain low levels of alginate [110] but can use either Pel or Psl (a structural cationic exopolysaccharideacharide) to develop biofilms [111].
###### Abstract  
The objective of this study is to model backscattering coefficients over bare soils at intra-plot spatial scales (from almost 80 to 2800 m\({}^{2}\)), in a context where the plot is the reference spatial scale in most past studies. A statistical modeling approach, based on a random forest algorithm, is proposed to overcome the limits of semi-empirical or physical models pointed out in the literature and to reduce discrepancies observed between the satellite-derived backscattering coefficients and the predicted values. The experimental device was set up on a network of agricultural plots located in southwestern France during the Multispectral Crop Monitoring (MCM) experiment. The dataset combines high spatial resolution satellite images (acquired by TerraSAR-X and Radarsat-2) together with synchronous geo-located measurements of key soil parameters (i.e., top soil moisture, surface roughness, and soil texture) on consistent spatial areas. Backscattering coefficients are estimated at six intra-plot spatial scales (from -80 to -2800 m\({}^{2}\)), showing an exponential increase in modeling performance, and reaching higher levels of accuracy than previous work performed at the plot spatial scale (i.e., 50% of variance explained in the literature, in the best cases). The increase in signal quality with the spatial scale mainly explains the higher performance observed in the 2800 m\({}^{2}\) area, with a correlation of 0.91 and RMSE of 0.83 dB in the X-band (for backscattering coefficients acquired with the HH polarization state). In the C-band, the values of correlation range from 0.74 to 0.80, and the RMSE from 1.65 to 1.85 dB (depending on the considered polarization state). The results also showed that the developed statistical algorithm is mainly influenced by the surface roughness and the top soil moisture, as semi-empirical or physical-based models. Soil texture does not significantly affect the algorithm.  
s 202515827 Article
## 6 Simulation Evaluation
### Simulation Results
#### 6.2.3 Computational Time  
Furthermore, we evaluated the computational time. The experiment results show that our proposed method outperforms the existing algorithms, i.e., Greedy, Always-Migrate, and Random, as in Figure 7. From Figure 7, we can see that the computational time is lower than that of the existing scheme due to the lower complexity of our proposed method. As we can see, when the total size of the workload is 300 MBs, the computational time is 15 s in our proposed method. At 300 MBs, the computational time of Always-Migrate is approximately 24 s, while that of the Random method has a computational time of 22 s. The computational time of the Greedy algorithm is 18 s. Hence, it is proved that, due to the coalition game theory, the cooperation of BSs, and the ANN method, the computational time of our proposed scheme is less than that of the existing schemes.
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 3 Proposed AICHE Transformation
### Adaptive Local Grayscale Correction  
The input image is divided into two sub-histograms according to the algorithm above, and histogram equalization is performed on each of the two sub-histograms to improve the image brightness offset. However, a new histogram assignment algorithm is used in this study to solve the image over-enhancement and gray level merging problems. The algorithm is mainly divided into two parts: image over-enhancement suppression and local gray level correction.
# An Artificial Intelligence-Based Algorithm for the Assessment of Substitution Voicing
## 5 Conclusions  
Convolutional neural networks were utilized to analyze speech audio signals. A corpus of digitized voice recordings from 309 male individuals was used to train the system. The acoustic substitution voicing index (ASVI) model was developed using regression analysis. This model was then evaluated with an additional 70 speech samples that had not been included in the algorithm's training, and compared to the auditory-perceptual SV rating supplied by medical practitioners. After laryngeal oncosurgery, the ASVI provided a quick and efficient solution for SV and speech in patients. The ASVI results were equivalent to medical specialists' auditory-perceptual SV evaluations and may be called cutting-edge. The ASVI and the SV assessment conducted by qualified laryngologists had a statistically significant, strong connection with \(r_{8}\) = 0.863 (\(p\) = 0.001). ASVI differences in the control, cordectomy and partial laryngectomy, and whole laryngectomy patient groups were statistically significant (\(p\) = 0.001). The refined, lightweight ASVI algorithm achieved reaction times of 3.56 ms.  
The present study is the first work describing the use of AI-based algorithm and originally elaborated ASVI in patients' SV and speech evaluation after surgical treatment of laryngeal cancer, and comparing it to auditory-perceptual speech evaluation provided by medical professionals. Moreover, the use of ASVI enables the quantification of SV assessment based on acoustic speech parameters. These features represent the novelty and originality of this work.  
Conceptualization, V.U. and R.M.; methodology, V.U., R.M. and K.P.; investigation, K.P. and S.V.; resources, K.P., A.K. and R.D.; software, R.M., A.K. and R.D.; data curation, K.P.; supervision, V.U. and R.M.; project administration, V.U. and R.M.; funding acquisition, V.U. and R.M. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from European Regional Development Fund (project No. 13.1.1-IMT-K-718-05-0027) under grant agreement with the Research Council of Lithuania (LMTLT). Funded as European Union's measure in response to COVID-19 pandemic.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Kaunas Regional Ethics Committee for Biomedical Research (No. BE-2-49 2022 04 20).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Restrictions apply to the availability of data used in this article. Data was obtained from LUHS and is available through the Lithuanian University of Health Sciences database with permission.  
The authors declare no conflict of interest.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 3 Case Study
### Grey Relational Analysis  
The Grey Relation Degree (GRD) is a metric used to assess the correlation between the predicted outcomes and the observed data. A higher GRD value corresponds to superior predictive performance of the model [48]. The specific operation process is as follows.  
The comparison time series:  
\[X_{i}=(X_{i}(1),\ X_{i}(2),\cdots X_{i}(n)) \tag{3}\]  
The reference time series:  
\[X_{0}=(X_{0}(1),\ X_{0}(2),\cdots X_{0}(n)) \tag{4}\]  
The two-time series are standardized by  
\[x_{i}(t)=\Bigg{(}X_{i}(t)-\frac{1}{n}\sum_{t=1}^{n}X_{i}(t)\Bigg{)}/\sqrt{\frac {1}{n-1}\sum_{t=1}^{n}\Bigg{(}X_{i}(t)-\frac{1}{n}\sum_{t=1}^{n}X_{i}(t)\Bigg{)}} \tag{5}\]  
The correlation coefficient between \(x_{0}\) and \(x_{i}\) is  
\[\xi(k)=(\min_{i}\min_{k}|x_{0}(k)-x_{i}(k)|+\rho\max_{i}\max_{k}|x_{0}(k)-x_{i} (k)|)/\]  
\[(|x_{0}(k)-x_{i}(k)|+\rho\max_{i}\max_{k}|x_{0}(k)-x_{i}(k)|),\rho\in(0,\infty) \tag{6}\]  
The GRD is  
\[r_{i}=\frac{1}{n}\sum_{k=1}^{n}\xi_{i}(k) \tag{7}\]  
Table 5 displays the GRD values for the constructed and the comparison models. The analysis indicates that the constructed prediction model achieves a GRD value of 0.919, surpassing all comparison models. This demonstrates a strong correlation between the predicted results of the constructed model and the observed data.
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 4 Numerical Simulation Results and Analysis
### Simulation Cloud Image Analysis
The point with the highest dust concentration on the side wall appears at the side wall of the driven impeller region. After passing through the driving impeller blade, the dust concentration at the side wall of the exit section decreases significantly. However, after passing through the swirl guide vane, the dust concentration at the side wall of the outlet section gradually increases. This is because the water film on the side wall of the dust removal fan has a dust trapping effect, leading to a large amount of dust being captured in the outlet section.