esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### DNA Extraction, Sequencing and Taxonomy and Functional Assignment  
DNA samples of both agricultural soils were extracted by QIAGEN DNEasy Power-Soil kit, whereas those of forest soil were extracted by NucleoSpin(r) Soil DNA extraction kit. PCR amplification of the bacterial V3-V4 regions was conducted using the bacterial primer pair Bact341F (5\({}^{\prime}\)-CCTACGGGNGG-CWGCAG-3\({}^{\prime}\)) and Bact785R (5\({}^{\prime}\)-GACTACHVGGGTATCTAATCC-3\({}^{\prime}\)) [39]. Amplicons were sequenced using an Illumina MiSeq platform and V3 Chemistry (Illumina). All amplification and sequencing steps were performed at RTL Genomics (Lubbock, TX, USA).  
Bioinformatics was proceeded on MOTHUR 1.33.3 [40] following _Standard Operating Procedure_ (SOP) custom analysis workflow. Raw reads, overlapping more than 20 base pairs, were first assembled to generate paired-end reads, then filtered to get high-quality reads, containing at least 200 bp length and having a minimum average quality of 25 Phred score. Chimeric sequences were checked using UCHIME in de novo mode [41], as implemented in MOTHUR and removed from the datasets. The cleaned sequences were clustered at 97% sequence identity and assigned taxonomy using the SILVA 132 database for bacterial 16S rRNA gene [42]. Ecologically relevant functions were then assigned to all detected OTUs using FAPROTAX [9]. In detail, a taxon (e.g., strain, species or genus) was annotated to certain functions if there was a literature report on a culture representative of the taxon that performed the functions. For example, if all cultured species of a genus were previously reported as sulfate reducers, all detected taxa belonging to the genus were also considered as sulfate reducers. All database and assignment instructions are available at [[http://www.loucalab.com/archive/FAPROTAX/](http://www.loucalab.com/archive/FAPROTAX/)]([http://www.loucalab.com/archive/FAPROTAX/](http://www.loucalab.com/archive/FAPROTAX/)). Lastly, rare OTUs (singletons to tripletons), which could potentially originate from sequencing error, were removed. The remaining reads were normalized to the minimum read count per sample of each dataset. Final OTU tables of all datasets are available in supplementary Tables S1.1-S1.3. The raw sequences are available in the National Center for Biotechnology Information (NCBI) under BioProject accession number: PRJNA646011 for forest and agricultural soils and PRJNA554586 for mangrove soil.
## 4 Results
### Classification Techniques and Performance Evaluation
The study analyzed text inputs from social media platforms, specifically Twitter, to address challenges such as the need for specialized professionals and extensive resources for ASD diagnosis. Using a dataset of 404,627 tweets, with a training and testing subset of 90,000 tweets (45,000 from ASD users and 45,000 from non-ASD users), the researchers employed ML models like decision trees, extreme gradient boosting (XGB), and k-nearest neighbors (KNNs), alongside DL models such as recurrent neural networks (RNNs), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and transformer-based models like BERT and BERTweet. The models demonstrated strong performance, achieving an accuracy of nearly 88% in classifying tweets from users potentially associated with ASD. This approach highlights the potential of integrating AI and NLP in early ASD detection, emphasizing its capability to improve diagnosis timeliness and patient outcomes. [PERSON] et al. (2024) [51] explored the integration of machine learning (ML) and explainable artificial intelligence (XAI) techniques to improve the accuracy and interpretability of diagnostic tools for autism spectrum disorder (ASD). By employing a rigorous data preprocessing pipeline--including outlier removal, handling of missing data, and feature selection guided by clinical expertise--the study enhanced the generalizability of ML models across diverse clinical datasets. Using tools like R and the caret package, the authors validated their models through 10-fold cross-validation and grid search optimization. Neural networks and extreme gradient boosting emerged as the top-performing algorithms in terms of accuracy, precision, and recall (average precision: 0.895; average recall: 0.895; average accuracy: 0.857). XAI methods further revealed that behavioral features significantly influenced model predictions, enhancing clinician trust and understanding of the results. This approach highlights the potential of combining ML and XAI to support early ASD diagnosis, personalize intervention strategies, and improve patient outcomes by bridging advanced computational techniques with practical clinical applications. Together, these studies reveal that advanced machine learning models, including innovative approaches utilizing natural language processing (NLP) techniques, provide significant improvements in diagnostic accuracy and efficiency for ASD detection. By achieving high sensitivity and specificity, these AI-driven tools demonstrate the potential to analyze diverse data sources, such as behavioral patterns, facial features, and even unstructured text, to identify ASD cases effectively. These methodologies not only streamline the diagnostic process but also broaden access to scalable and reliable ASD screening solutions across clinical, educational, and digital settings. By leveraging AI's ability to handle complex and varied data, these tools pave the way for early detection, timely interventions, and better support for individuals with ASD.
## 4 Discussion
### Uncertainties in This Study  
The reliability of the conclusions in this study may be influenced by three key uncertainty factors: (1) Limitations of annual average and national-scale aggregation calculation methods--In order to understand the FDS-LST mechanism at the macro scale and to facilitate the provision of a scientific basis for wannabe policymakers, we chose both the time series (annual mean) and national scales. Time series (annual mean) may blur the mean values over the period of active vegetation. The direct physiological activity of vegetation predominantly occurs during the growing season; non-growing-season climatic conditions (e.g., winter temperatures) may exert lagged effects on subsequent growing seasons (e.g., influencing spring phenology) [62]. Analyzing FDS at the national scale may mask local heterogeneities in vegetation-climate interactions, potentially introducing biases. Future  
Figure 7: Schematic diagram of main processes affected by FDS on LST.  
research should leverage geographically weighted regression (GWR) models to account for spatial non-stationarity, integrated with multi-scale spatial analysis techniques [63; 64; 65]. Sensitivity analyses across varying spatial resolutions are also crucial to validate the robustness of the findings. (2) Limitations in environmental variable representation--The current framework does not fully incorporate dynamic environmental drivers, such as the frequency and intensity of extreme climate events [66], policy interventions, and human activity patterns (e.g., night-time light data, GDP gradients, and population density). Developing dynamic coupled models that integrate socioeconomic factors and establishing a more comprehensive multi-dimensional environmental variable index system would enhance the explanatory power in future studies. (3) Neglecting the biochemical feedback of forest disturbance with climate--This research concentrated on the biophysical regulation of LST by forest cover changes (e.g., albedo-evapotranspiration balance), excluding the biogeochemical feedback associated with rising atmospheric CO\({}_{2}\) (e.g., CO\({}_{2}\) fertilization effects) [5]. This decoupling may lead to the underestimation of the net surface temperature response. Implementing multi-scenario simulations with earth system models is recommended for future work to disentangle and quantify the relative contributions of different climate feedback pathways. (4) In Section 2.7, we rely on human designation for the division of the time series and do not fully show the data trends; this was conducted to balance the sample size to facilitate the development of a forest management policy in Europe. [PERSON] et al. [67] used segmented linear regression for the time series intervals, which provides some reference for our subsequent study.
# An NLOS Ranging Error Mitigation Method for 5G Positioning in Indoor Environments
## 4 Analysis of Simulation and Experiment Results
### Simulation Result Analysis  
In Figure 3, the correlation of the candidate features selected for NLOS observations in this study is presented using a correlation coefficient matrix plot. The correlation matrix in the figure is symmetric about the diagonal, and each color within the square corresponds to the same meaning and value. From the analysis of the figure, it can be observed that there are significant positive correlations between the MPV and TE, MPV and SNR, and MPV and KUR, with the MPV showing a particularly strong correlation with the TE. On the other hand, there are noticeable negative correlations between the RT and SNR, RT and MPV, and RDS and SNR. The correlation among other features is relatively weak.  
Figure 3: Correlation coefficient matrix graph of the selected features.  
In Figure 4, the overall performances of the different methods are compared in terms of the MSE. From the graph, it can be observed that SVM-REM exhibits a significant performance drop as the number of features increases, while the VBS method performs the best. The performance of the NM-SLE algorithm proposed in this paper is close to that of the VBS algorithm, with both methods showing lower errors compared to the VTL and IRUKF algorithms. The average positioning errors for the proposed method and SVM-REM, VTL, VBS, and IRUKF algorithms are 1.56 m, 8.16 m, 1.91 m, 1.52 m, and 1.85 m respectively.  
Figure 5 presents the cumulative distribution function (CDF) of the positioning errors for the NM-SLE method proposed in this paper, as well as those for the SVM-REM, VTL, VBS, and IRUKF methods. It can be observed that the performances of the NM-SLE method and VBS are significantly better than those of the other three methods. At a 60% probability level, both NM-SLE and VBS achieve positioning accuracy better than 2 m. The 90% positioning accuracy for NM-SLE, SVM-REM, VTL, VBS, and IRUKF methods are 3.31 m, 4.59 m, 4.57 m, 3.42 m, and 4.87 m respectively.  
Figure 4: Comparison of the different NLOS error elimination methods with different features.  
Figure 5: The cumulative distribution function of the positioning error.  
The average execution time of different NLOS suppression methods is presented in Figure 6. The data shown in the graph were obtained by running the methods on a laptop. Although the absolute values may not be directly applicable to resource-constrained embedded systems, the relative performance differences between different NLOS suppression methods still reflect their varying computational complexities.  
Although this section has demonstrated the effectiveness of the proposed NM-SLE method through simulations, there are still some limitations that need further investigation. Firstly, due to hardware limitations, the simulations only considered data obtained in static scenarios. Further validation is required in dynamic scenarios. Additionally, different shapes and materials may have significant effects.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 4 Data Analysis Result
### Log-Distance  
For the log-distance model parameters, the path loss exponent (\(n\)) derived as 3.9, and \(L_{E}\) was found as 3.4 dB. The mean and standard deviation of shadow fading (\(X_{g}\)) were found as \(\mu=0.25\) and \(\sigma=6.8\) dB, respectively. These extracted parameters resemble the propagation in an \"office with hard partitions\" at 1.5 GHz [35]. It is necessary to emphasize that these tests were conducted at 867 MHz.
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 4 Numerical Simulation
### None of the Satellites Are Scheduled (Ns)
From Figure 13a, we know that most of the satellites are seen when half of the links are in the same orbit, and the availability of links in the same orbit are much higher than those in different orbits. From Figure 13b, we can see that multiple-fold availability is nearly zero if the altitude is higher than 22,000 km, since only one of the two beams in a link can be received by users that are above the navigation satellites. The highest single-fold availability is approximately 40%, while the highest four-fold availability is only 1%. The availability as a function of beamwidth is simulated below. \begin{table}
\begin{tabular}{c c c} \hline \hline
**In the Same Orbit** & **In Different Orbits** & **Half in the Same Orbit** \\ \hline
11-13, 12-15, 14-17, 16-18 & 11-21, 13-23, 15-25, 17-27 & 11-21, 15-25, 13-18, 14-17 \\
21-23, 22-25, 24-27, 26-28 & 22-32, 24-34, 26-36, 28-38 & 22-32, 26-36, 23-28, 24-27 \\
31-33, 32-35, 34-37, 36-38 & 12-31, 14-33, 16-35, 18-37 & 12-31, 16-35, 33-38, 34-37 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Link plans used in the numerical simulation. Figure 14 shows that the availability increases with beamwidth, and there are two peaks at 6000 km and 14,000 km. The availability probability of users under 30,000 km may reach 30% or even to a maximum of 70% if the beamwidth is increased to 20\({}^{\circ}\). Note that the beamwidth cannot be arbitrarily increased because this negatively affects the gain of the antenna. Therefore, both the availability and the received power should be considered when choosing the beamwidth.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 3 Human Activity Signatures Measured by Using a SISO FMCW Radar System
### Measurement Setup  
In this paper, we used a commercially available MIMO radar system called Ancortek SDR-KIT 2400T2R4, which is an FMCW millimeter wave (mm-Wave) radar operating at 24 GHz. We configured the radar in SISO mode of operation and used a single transmitter antenna \(A_{1}^{T_{x}}\) and a single receiver antenna \(A_{1}^{R_{x}}\). We used a monostatic configuration of the antennas to capture the micro-Doppler information of a human body moving in the _xy_-plane. In a monostatic configuration, the transmitter and receiver antennas of the SISO radar system are collocated. The SISO FMCW radar system was configured according to the parameters listed in Table 1. The pulse repetition frequency (PRF) of the SISO FMCW radar system is equal to the inverse of the chirp interval \(T_{sw}\), i.e., \(\text{PRF}=1/T_{sw}\). This is due to the fact that we do not require the TDMA mode of operation for the SISO radar system. In other words, the transmitter antenna \(A_{1}^{T_{x}}\) transmits the chirp waveform \(c_{i}(t^{\prime})\) in (1) continuously in time.
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 2 Medical Imaging Applications
### Nonlinear Microscopy
#### 2.3.1 Multiphoton Microscopy  
Multiphoton microscopy is based on the use of two or more photons to simultaneously excite a tissue surface. The contributing photons excite the molecules of the sample to its transitional state, which is unstable. The molecule comes to its stable ground state by releasing energy in the form of a fluorescent wave. In two-photon excitation, two photons are simultaneously absorbed in a single event; while in three-photon excitation, three photons are absorbed at the same time. Since the process requires the simultaneous absorption of more than one photon, absorption is most likely to occur in the focus where the photons' flux is maximized [10].  
To excite a molecule with multiple photons, very short (femtosecond to hundreds of femtoseconds) high-energy laser pulses are focused on the sample at a repetition rate of up to 80 MHz [10]. Usually, the excitation of a tissue surface requires an ultraviolet radiation to produce fluorescence. Consequently, near-infrared (NIR) light can be used for two-photon microscopy, and infrared (IR) light can be used for three-photon absorption. The longer wavelength light penetrates deeper in the tissue, permitting the possibility of imaging thick sections. Moreover, infrared light causes less scattering compared to employing UV laser light in a single-photon excitation. This characteristic, with no scattering from the out-of-focus zone, gives high contrast in the image without using a pinhole [26].  
The resolution of multiphoton microscopy is of the same order as confocal microscopy, but the possibility of a greater penetration depth makes it a popular method for medical imaging. [PERSON] et al. presented a two-photon exciting fluorescence (TPEF) microscope for detection of ovarian cancer [27]. In this case, \(\beta\)-Galactosidase (\(\beta\)-gal) enzyme is used as a biomarker to detect the ovarian cancer cells (Figure 6[27]). Similarly, two-photon microscopes are used for investigating the biological functions and disorders in tumor cells [28] and to image the neural tissues of the brain [29]. Some of the multiphoton-based microscopes presented in the literature are listed by [PERSON] et al. in [25].  
Figure 6: Two-photon exciting fluorescence (TPEF) image of ovarian cancer cells: (**a**) Fluorescence TPEF image using \(\beta\)-Galactosidase (\(\beta\)-gal) probe; (**b**) image using Lyso Tracker Red probe; (**c**) overlay of images a and b; (**d**) intensity profile (taken from [27] with permission).
# Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
## 5 Future Directions and Opportunities  
### Emerging Trends in Quantum Hardware and Algorithm Development  
Continuous advancements in quantum hardware, including the development of more stable qubits and efficient error correction techniques, are paving the way for practical applications in medical research [105]. Simultaneously, the evolution of hybrid quantum-classical algorithms is enabling researchers to leverage QC for specific tasks while relying on classical systems for others [106]. These innovations hold the potential to tackle complex problems, such as simulating molecular interactions or optimizing treatment protocols.
# A Novel Shape-Adjustable Surface and Its Applications in Car Design
## 1 Introduction
[35], a virtual model stretched from 2D graphics was simulated in CFD to modify the statistical methods. To improve the Delayed Detached-Eddy Simulation algorithm, [PERSON] et al. [36] used a simplified car model consisting of all plane surfaces, and the CFD result showed that the simplified CAD model can still qualitatively express the influence of surface slope and dimension on CFD simulation. However, in all the studies and CFD simulations above, the CAD models are not parametric shape-adjustable, which means that the shape optimization process would be inefficient. And this would be another key problem for this research. To analyze these references above, the following summaries could be made:  
(1) Car modeling could be defined by CAD model, and its style characteristics could be adjusted by the parameters in the models. As the most important aesthetic component, feature lines have an important impact on vehicle modeling. (2) Various Bezier-like surfaces have strong adjustability, however, the non-interpolation at boundaries and too many control parameters (control points) would render difficult the application to the shape design of cars. (3) Originally, all Coons surfaces and Coons-like surfaces could be classified as the first and second type of Coons surface (described above). They are all suitable for shape design because their shape could be controlled directly by the boundary curves. The shape of the first surface type is determined only by the boundary curves because, in the car's shape design, the feature lines could be taken as the boundary curves. Considering the compatibility conditions of boundaries, the second surface type increases the flexibility of regulation, but it is difficult to generate for its mathematical definition. (4) The existing literature supports that the simplified automotive body model could be used for qualitative CFD analysis and also reflect the flow characteristics of airflow on the car body. In response to the current challenges, this paper attempts to create a vehicle CAD model template based on feature lines, which are extracted from dozens of commercially available car models. All the surfaces in the template are novel type with adjustable shapes under the simplest construction conditions (only interpolating at the boundary). Through these measures, a multi-level design method and adjustment to the appearance of the car model is proposed. By considering the result of CFD from the CAD model template, a set of theoretical methods to optimize the body shape is finally established. This paper is organized as follows. The definition and properties of the new type of surface are described in Section 2. In Section 3, we build a simplified vehicle model template, and present the adjustment mode and the shape parameter constraints. In Section 4, the hood surface of the template with the new surface is optimized in CFD. Some brief conclusions are given in Section 5.
## 3 Theoretical Derivations
### Comparative Analysis of Theory and Numerical Simulation  
As shown in Figure 7, MIDAS GTS was used for modeling and FLAC3D 5.0 software was imported for numerical simulation; this improves the modeling speed and computational efficiency. Considering the symmetry of the knife model, only 1/4 of the actual model was established, thus improving the computational efficiency. The buried depth of the tunnel is 60 m, and the soil weight is 25.7. The bolt lengths of 6 m, 8 m, and 10 m were taken as an example to verify the accuracy of the method proposed in this study. The lining thickness is 0.3 m, and the bolts were simulated using the beam element. In order to improve computational efficiency, the grid sizes of excavated soil, near end soil, and far end soil were distinguished and presented in different colors during model construction.  
The comparison of the theoretical calculation and numerical simulation results is shown in Table 6.  
The calculation results indicate that the method proposed in this paper has smaller errors and higher accuracy compared with the numerical simulation results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(E_{b}\) (GPa) & \(G_{b}\) (GPa) & \(t_{b}\) (m) & \(d_{b}\) (m) & \(L_{b}\) (m) & \(D_{b}\) (m) & \(\theta\) (\({}^{\circ}\)) \\ \hline
210 & 1.15 & 0.009 & 0.032 & 6 & 1.6 & 45 \\
210 & 1.15 & 0.009 & 0.032 & 8 & 1.6 & 45 \\
210 & 1.15 & 0.009 & 0.032 & 10 & 1.6 & 45 \\ \hline \end{tabular}
\end{table}
Table 3: Bolt parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \(E_{f}\) (GPa) & \(A_{f}\) (GPa) & \(D_{b}\) (m) & \(I\) (m\({}^{\ddagger}\)) \\ \hline
206 & 0.003955 & 1.6 & 0.000025 \\ \hline \end{tabular}
\end{table}
Table 4: Steel frame parameters.  
Figure 7: Numerical simulation modeling.