esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Pillar Stability of Underground Mines with Random Trees and C4.5 Decision Trees
## 3 Methodology
### C4.5 Decision Tree  
Commercial version 4.5 (C4.5), was proposed by [PERSON] [38], is a widely known machine learning algorithm that is primarily used to develop decision trees. The method uses an entropy-based measure as the selection criteria that yield a good classification accuracy in minimal time. It is is an extension of the ID3 algorithm that also operates the divide-and-conquer policy [38]. The method's main improvements include the pruning methodology and excellent processing of numeric attributes, missing values and noisy data. The C4.5 algorithm usually consists of two processes; preparation of thee decision tree, and making the rules (structure and design). Then measuring the entropy, and calculating the highest attribute for information gain.  
The entropy formula is shown below, where \(S\) is entropy, and \(p\) in the output is a class proportion.  
\[Entropy(S)=\sum_{i=1}^{n}-p_{i}\times\log_{2}p_{i} \tag{1}\]  
In addition, the attribute with the highest gain value is used as the root attribute. \(Gain\) (\(S\), \(A\)) is presented in Equation (2) where, \(S\) is a case set; \(A\) is a case attribute; \(|S_{i}|\) is a number of cases to \(i\) and \(|S|\) is number of cases in the set.  
\[Gain\left(S,A\right)=Entropy(S)\sum_{i=1}^{n}\frac{|S_{i}|}{|S|}\times Entropy (S) \tag{2}\]  
C4.5 algorithm develops a DT using the recursive-splitting and top-down and technique. The tree structure of C4.5 consists of a root node, internal nodes, and leaf-nodes. The root node encompasses all the input data. An internal node usually has two or more branches and is accompanied by a decision function. While the leaf node denotes the output of given input parameters. J48 is an open-source Java implementation of the C4.5 DT algorithm on the WEKA data- mining platform. The simple tree structure of C4.5 DT is as shown in Figure 3. Table 3 outlines the key differences between the two data mining algorithms adopted for the current study.  
Figure 2: Structure of random tree [24].
# An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking
## 3 System Overview
Unlike most multicast methods, simply forward the multicast flow along the shortest path tree from the source to receivers; users can obtain the MJNs of the group they are interested in through NRS and then select the appropriate MJN to join according to the given MJN selection strategy in AHHM. We can design a suitable MJN selection strategy according to the usage scenarios and optimization goals to optimize the multicast transmission path. In the process of establishing the multicast communication from the MJN to the receiver, AHHM preferentially uses the stateful mode and then converts to the stateless mode when encountering the router whose NFT load reaches the threshold. By adaptively using the stateful mode on routers with fewer forwarding states and using the stateless mode on routers with more forwarding states, AHHM can effectively balance the forwarding overhead and forwarding states. In the stateless multicast methods, the root node needs to track destinations and attaches destinations information to each multicast packet, which puts great load pressure on the root. In AHHM, only part of the destinations information needs to be maintained, and the destinations information that needs to be maintained is balanced on root nodes of multiple stateless sub trees. Therefore, AHHM can avoid the problem of root overload faced in other stateless multicast methods. Besides, AHHM can alleviate the problem of multicast packet redundancy faced in BIER because the number of egress routers in each stateless sub tree is much smaller.
## 5 Proposed Predictive Maintenance (PdM) Structure Using Internet of Things (IoT)
### Proposed PdM Structure Using IoT
#### 5.2.1 Data Acquisition, Processing and Analytics  
It is made up by sensors that are installed on the equipment components, different electronic devices, and a microcontroller that is able to convert and manipulate data based on the purpose of its custom application. With purpose of data processing and predictive analytics at edge, microcontroller might have greater capacity and performance than Arduino Uno board which was used to collect data for our predictive model construction. Based on the functionality of the equipment and its components basic information, this part involves initializing, configuring, gathering, processing, and analyzing the data.  
Sensors should be selected, depending on the physical behaviors to be monitored. With purpose of performing the edge data processing and analytics, the microcontroller might have enough storage capacity to store and process data. Figure 11 shows the program executed by microcontroller of proposed structure.  
Figure 10: Proposed PdM structure using IoT.  
The program running into microcontroller of proposed PdM structure to collect, process and transmit data is performed through seven phases:  
1. Ports for sensors and communication module are initialized and configured.
2. Read the voltage corresponding to received data from sensors.
3. Save received data locally.
4. Saved data are fed to a predictive model for analysis and prediction.
5. Try to connect to GSM network.
6. If microcontroller is connected to GSM network, then the data are sent to remote the database. Otherwise, the microcontroller keeps trying connecting to GSM network.
7. If the result shows an equipment's component critical condition, then a SMS alert is sent to the user.  
Based on the real time data, time dependence has great influence on predictive data analytics due to the fact that the present time points are likely to be related to the previous time point or a time point in the long past. Such independency can be helpful in detecting a feature for the present abnormal occurrence, which may be mapped to the previous one.  
The prediction of the health status of mechanical equipment relies on the unstable long time dependence performance data. In order to propose the fitting predictive model, the Seasonal Autoregressive Integrated Moving Average (SARIMA) and LSTM models were used to learn and predict from our real time collected data, where LSTM performed well with a low root mean square error (RMSE) of 22.89 and 2.9 when compared to SARIMA, with 29.97 and 3.68 RMSE, respectively, for different component's univariate data.  
On top of the predictive model results, the microcontroller could be programmed, depending on results displayed, such as client access limit, reports generation, alerting message, like a short notification message or email sending, customized dashboard outlook, etc., all for the purpose of monitoring.  
Figure 11: Program running in Microcontroller of the proposed structure.
## 3 Results
### mRNA Expression Levels of Cu/Zn-SOD, Mn-SOD, and CAT in Small-Intestine Tissue  
As shown in Figure 4, the expression of Cu/Zn-SOD, Mn-SOD, and CAT is the highest in the normal group. After constipation is induced by activated carbon, their expression was significantly reduced (\(p<0.05\)). LB, LF-CQPC10-L, and LF-CQPC10-H significantly up-regulated the expression of Cu/Zn-SOD, Mn-SOD, and CAT (\(p<0.05\)), particularly LF-CQPC10-H.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **MPO (mU/mg)** & **NO (\(\mu\)mol/gprot)** & **MDA (nmol/mg)** & **GSH (\(\mu\)mol/mg)** \\ \hline Normal & 3.3 \(\pm\) 0.4 \({}^{\mathrm{e}}\) & 0.4 \(\pm\) 0.1 \({}^{\mathrm{e}}\) & 0.5 \(\pm\) 0.1 \({}^{\mathrm{e}}\) & 11.
## 2 Satellite and Data  
The main scientific objective of the CSES is to monitor ionospheric perturbations associated with natural hazards (mainly with strong earthquakes) in the quest for possible earthquake forecasting. To serve the need of emergence response to disastrous earthquakes, such as the 2008 Mw 7.9 Wenchuan earthquake, the CSES was designed to provide real-time data over China's territory via a direct downlink to the ground segment.  
The CSES completes 15.2 orbits around Earth per day, with an orbital period of -94.6 min and a five-day recursive period over the same geographic area with the ascending/descending node local time of 02 a.m./02 p.m., respectively. Its payloads allow for the measurement of the electromagnetic field, energetic particles, and in-situ and profile ionosphere parameters. The diverse physical parameters obtained by the CSES can support the comprehensive research of geo-/space physics and radio science.  
The payloads involved in this study are briefly introduced here. The background geomagnetic field is measured by a high precision magnetometer (HPM) ([PERSON] et al., 2018), which includes two fluxgate sensors ([PERSON] et al., 2018) and a coupled dark-state magnetometer ([PERSON] et al., 2018), providing both vector and scalar values of the total geomagnetic field in a frequency range from DCto 15 Hz and a sampling rate of 1 Hz for the scalar geomagnetic field detection. The variant magnetic field from 10 Hz to 20 kHz is measured by a tri-axis search-coil magnetometer (SCM) mounted on the end part of a 4.5-m-long boom to avoid the artificial electromagnetic interferences induced by the satellite platform itself [38,39]. The SCM provides data in the three frequencies bands of ULF (10 to 200 Hz), ELF (from 200 Hz to 2.2 kHz), and VLF (1.8 to 20 kHz) with sampling rates of 1024 Hz, 10.24 kHz, and 51.2 kHz, respectively. The electric field is detected by an electric field detector (EFD), which consists of four spherical sensors mounted at the near-end part of four booms (4.5 m long) [40], measuring the electric field in four frequency channels: ULF (DC to16 Hz), ELF (from 6 Hz to 2.2 kHz), VLF (1.8 to 20 kHz), and HF (from 18 kHz to 3.5 MHz), with sampling rates of 128 Hz, 5 kHz, 50 kHz, and 10 MHz, respectively. The energetic particles are recorded by the high energetic particle package (HEPP), with three sub-detectors (HEPP-H, HEPP-L, and HEPP-X) providing the energy spectrum and pitch-angle distribution of charged particles (protons: from 2 MeV to 200 MeV; electrons: from 100 keV to 50 MeV) and soft X-ray emission from solar events [41,42]. In this study, HEPP-L was selected to measure electron fluxes at energies from 0.1 to 3 MeV and corresponding pitch-angle distributions. HEPP-L contains nine silicon-slice units and one anti-coincidence detector with an angular resolution of 5\({}^{\circ}\) and a maximum field-view of 100\({}^{\circ}\)\(\times\) 30\({}^{\circ}\)[42].
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
###### Abstract  
Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter \(\sigma\), non-sensitive loss function parameter \(\epsilon\), and penalty parameter \(C\). We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.  
Keywords:Support Vector Machine; semantic technology; travel time; intelligent transportation system; artificial fish swarm algorithm; big data +
Footnote †: journal: Applied Sciences
###### Abstract  
Mining activities can trigger geological disasters, including slope instability and surface subsidence, posing a serious threat to the surrounding environment and miners' safety. Consequently, the development of reasonable, effective, and rapid deformation monitoring methods in mining areas is essential. Traditional synthetic aperture radar(SAR) satellites are often limited by their revisiting period and image resolution, leading to unwrapping errors and decorrelation issues in the central mining area, which pose challenges in deformation monitoring in mining areas. In this study, persistent scatterer interferometric synthetic aperture radar (PS-InSAR) technology is used to monitor and analyze surface deformation of the Jinchuan mining area in Jinchang City, based on SAR images from the small satellites \"Fucheng-1\" and \"Shenqi\", launched by the Tianyi Research Institute in Hunan Province, China. Notably, the dual-star constellation offers high-resolution SAR data with a spatial resolution of up to 3 m and a minimum revisit period of 4 days. We also assessed the stability of the dual-star interferometric capability, imaging quality, and time-series monitoring capability of the \"Fucheng-1\" and \"Shenqi\" satellites and performed a comparison with the time-series results from Sentinel-1A. The results show that the phase difference (SPD) and phase standard deviation (PSD) mean values for the \"Fucheng-1\" and \"Shenqi\" interferograms show improvements of 21.47% and 35.47%, respectively, compared to Sentinel-1A interferograms. Additionally, the processing results of the dual-satellite constellation exhibit spatial distribution characteristics highly consistent with those of Sentinel-1A, while demonstrating relatively better detail representation capabilities at certain measurement points. In the context of rapid deformation monitoring in mining areas, they show a higher revisit frequency and spatial resolution, demonstrating high practical value.  
Fucheng-1; Shenqi; PS-InSAR; deformation monitoring; coherence coefficient 2025  
28 May 2025  
9 July 2025
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## 3 Methodology
### Simulation Environment  
Simulations are performed using Python 3 on a computer system with the following features.  
* CPU: Intel Core i5-1345U, 1.60 GHz (Intel Corporation, Santa Clara, CA, USA)
* RAM: 16 GB  
In addition, the model assumes the perfect channel state information (CSI) at the receiver, ensuring optimal decoding performance and mitigating errors caused by uncertain channel conditions. To enable fair comparison, the classical MIMO system uses complex baseband binary phase shift keying (BPSK) modulation with rate 1/2 polar coding, matching the bandwidth usage of the proposed quantum system. For statistical robustness,the evaluation is conducted by subjecting each video to 1000 transmission trials. The results presented reflect the average performance across all trials, measured using the PSNR, SSIM, and VMAF metrics.
## 3 Periodontal Disease and Obstructive Sleep Apnea
### Clinical Relevance  
The co-occurrence of PD and OSA has significant implications for cardiovascular, metabolic, and inflammatory health, as the combined impact of these conditions exacerbates risks beyond those posed by each individually. Both conditions independently contribute to systemic inflammation, cardiovascular disease, and metabolic disorders; however, their coexistence appears to intensify these effects [44]. Integrating the management of both conditions in clinical practice is essential to mitigate complications and improve patient outcomes. Recent evidence underscores the importance of early periodontal evaluation in OSA patients, particularly those with cardiovascular risk, as PD may exacerbate systemic inflammation and accelerate the progression of cardiovascular disease [30; 45; 46; 47].  
Emerging research suggests a potential dose-response relationship between the severity of OSA and the progression of PD, although heterogeneity among studies complicates this conclusion [46; 48]. For example, a cross-sectional study by [PERSON] et al. (2013) demonstrated a significant association between clinical attachment loss (CAL) and probing pocket depth (PPD)--key markers of PD--and increasing OSA severity, as measured by higher AHI scores and greater oxygen desaturation [32]. This indicates that more severe OSA may correspond with advanced periodontal deterioration. [PERSON] et al. (2023) similarly reported a tendency for patients with periodontitis to have severe OSA [45]. However, studies such as those by [PERSON] et al. (2015) found no statistically significant associations, highlighting variability in the findings [49]. A meta-analysis by [PERSON] et al. (2015) corroborated the general association but noted considerable heterogeneity across included studies [36].  
Collectively, the evidence suggests that OSA-related hypoxia and inflammatory responses likely contribute to the accelerated breakdown of periodontal tissue. Nevertheless, the exact nature and consistency of the dose-response relationship remain unclear, emphasizing the need for further research to clarify these mechanisms and better inform clinical strategies.
## 2 Materials and Methods
### Climate and Flux Monitoring  
Microclimate and eddy covariance data have been recorded continuously at the site since December 2003 ([25; 26]) following the Euroflux methodology [27]. This study covers an 11-year period, from 2004 to 2014. Global and infrared incident and reflected radiation (CNR1, Kipp and Zonen, Bohemia, NY, USA) were measured above the canopy. Temperature sensors (CS107, Campbell Scientific Inc., Logan, UT, USA) were used to measure soil temperature (Ts, \({}^{\circ}\)C). A rain gauge (ARG100, EM lmt, Sunderland, UK) measured bulk rainfall above the canopy. Rainfall data were incorporated into a soil water balance model, developed and validated for the study site [28], to estimate relative extractable water (REW) for trees from the soil surface down to 3-m in depth. REW is a dimensionless variable that characterizes the water status of the soil at a given date relative to its maximum potential soil water content.  
Dataloggers (CR23X, CR1000 or CR3000, Campbell Scientific Inc., Logan, UT, USA) were used to collect meteorological data at 1 min intervals and to compile them as 30 min averages or sums. The 30-minute data were summed for rainfall and averaged for the other parameters to obtain daily values. Our objective was to focus on the impact of these meteorological data on evapotranspiration and water use efficiency while the canopy was fully active and under rather stable climate conditions. We thus eliminated nighttime, early morning or early evening periods. We thus only considered meteorological data taken during the period from 10:00 am to 16:00 pm, except for daily rainfall and REW, which were summed or averaged for 24 h a day.  
We used a 3-D sonic anemometer (R3-50; Gill Instruments, Lymington, UK) to measure wind velocities and sonic temperature at 20 Hz. We used to an open-path infrared gas analyzer (Li7500, LI-COR Inc., Lincoln, NE, USA) to monitor CO\({}_{2}\) and H\({}_{2}\)O concentrations at 20 Hz. As the open-path gas analyzer proved to be very sensitive to rain, a closed-path infra-red gas analyzer (Li7000, LI-COR Inc., Lincoln, NE, USA) was added to the system in June 2005 to gap-fill the missing data from the Li7500 analyzer. Eddy covariance data were saved with the EDDYLOGP software (Alterra, Wageningen, The Netherlands) on 256 MB memory cards inserted into an HP200 LX laptop (Hewlett Packard, Palo Alto, CA, USA) and downloaded weekly to another computer.
## 3 Results and Discussion
### CFD Simulations
#### 3.2.2 Simulation of the 3-Bar L-Box Test  
Once adjusted using the Bingham equation (Figure 14), the L-box test was simulated, as shown in Figure 16. The error rates for the blocking ratio derived from this test are listed in Table 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mix** & **Error Rate (\%)** \\ \hline \(RW\) & 20.5 \\ \(W1.5\) & 48.2 \\ \(W3.0\) & 107.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error rates from the CFD simulation of the blocking ratio from the 3-bar L-box test.  
Figure 14: CFD simulation of the slump-flow test: (**a**) Bingham equation; (**b**) viscosity law.  
Figure 15: Linear simple regression between plastic viscosity and: (**a**) slump flow; (**b**) \(t_{500}\) time.  
The CFD simulations adjusted the blocking ratio from the 3-bar L-box test more accurately the lower the content of RCWTB and, therefore, of the GFRP fibers in SCC. Thus, the error rate went from 20.5% in the \(RW\) mix to 107.4% in the \(W3.0\) mix. CFD simulations always obtained higher blocking ratios than the experimental ones and, therefore, a better SCC flow under these conditions. This behavior could be due to the fact that the CFD simulations did not allow for introducing the individual effect of the GFRP fibers [63], which are thought to be partially retained in the bars simulating concrete reinforcement, which hindered the SCC flow. The plastic viscosity did not show a poor relationship with the experimental results of the 3-bar L-box blocking ratio, as can be seen in Figure 17. Therefore, it is considered that the Bingham parameters obtained were also in agreement with the results of the 3-bar L-box test, although the simulation of the individual effect of the GFRP fibers would provide greater accuracy [48]. On the other hand, the use of smaller meshes could also improve the simulation accuracy [46].