esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 4 Load Forecasting Methodology
### Multiple Linear Regression  
The original power load is decomposed using a VMD optimized by SSA to obtain multiple IMFs, of which the low-frequency ones are fed into the MLR for forecasting to obtain the forecasting values for that component. MLR is a traditional statistical analytics-based forecasting method that, in contrast to back-propagation neural network (BPNN), does not require iterative training and parameter tuning. Compared with the back-propagation neural network (BPNN), it does not require iterative training and parameter adjustment, and thus has a fast execution speed. At the same time, MLR is able to accurately fit highly periodic and smooth load curves with high forecasting accuracy. Among the mode components obtained by the SSA-VMD algorithm, the low frequency component is relatively stable, which contains the periodic and long-term patterns in power load. Considering the accuracy and rapidity of MLR in forecasting smooth sequences, the method proposed in this paper uses MLR to forecast low-frequency components. However, for non-smooth sequences, the forecasting effect of MLR is poor. Therefore, for the high-frequency components obtained from VMD decomposition, this paper uses LSTM for forecasting. The MLR model is expressed as follows:  
\[Y=X\times\beta+\mu \tag{20}\]  
\[\begin{bmatrix}y_{1}\\ y_{2}\\ \vdots\\ y_{n}\end{bmatrix}=\begin{bmatrix}1&x_{11}&\cdots&x_{1n}\\ 1&x_{21}&\cdots&x_{2n}\\ \vdots&\vdots&\ddots&\vdots\\ 1&x_{n1}&\cdots&x_{nn}\end{bmatrix}\times\begin{bmatrix}\beta_{0}\\ \beta_{1}\\ \vdots\\ \beta_{n}\end{bmatrix}+\begin{bmatrix}\mu_{1}\\ \mu_{2}\\ \vdots\\ \mu_{n}\end{bmatrix} \tag{21}\]  
where \(y_{i}\) represents the value of the electrical load; \(x_{ij}\) represents the various factors influencing the load, which are the historical load values in the low-frequency component in this method; \(\beta_{0}\) represents the constant term; \(\beta_{i}(i=1,2,\ldots,n)\) represents the regression coefficient; and \(\mu_{i}\) represents the random fluctuations.  
The regression parameters are evaluated using the least squares method to determine the regression parameters and obtain a forecasting model.  
\[\hat{\beta}=\left(X^{\prime}X\right)^{-1}X^{\prime}Y \tag{22}\]
# Feasibility of LoRa for Smart Home Indoor Localization
## 3 Experiment and Evaluation
### Experimental Setup
#### 3.1.1 Testing Environment  
The research team conducts a data collection in a 114.4 m\({}^{2}\) (1,231 sq-ft) furnished apartment, as shown in Figure 1. We place our LoRa nodes, at a fixed height of 1.06 m (3.48 ft) which is the height of the waist of a person, to cover different scenarios of tracking persons that generally exist in homes. The appliances and the furniture in the apartment that can cause reflections and NLOS for LoRa communication, include walls, refrigerator, sink, kitchen countertop, beds, desk, computer table, computer monitor, washer, and dryer. NLOS and reflections both cause inaccuracies in range measurements between tags and anchors.
_Applied_  
_sciences_  
_Article_  
**Automated Prediction of Extraction Difficulty and Inferior**  
**Alveolar Nerve Injury for Mandibular Third Molar Using a**  
**Deep Neural Network**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{2}\)\({}^{\copyright}\) and [PERSON] \({}^{1,*}\)\({}^{\copyright}\)**  
\({}^{1}\) School of Integrated Technology (SIT), Gwangju Institute of Science and Technology (GIST),  
Gwangju 61005, Korea; [EMAIL_ADDRESS] (J.L.; [EMAIL_ADDRESS] (J.P.)  
\({}^{2}\) Department of Oral and Maxillofacial Surgery, College of Dentistry, Chosun University,  
Gwangju 61452, Korea; [EMAIL_ADDRESS]  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract: Extraction of mandibular third molars is a common procedure in oral and maxillofacial surgery. There are studies that simultaneously predict the extraction difficulty of mandibular third molar and the complications that may occur. Thus, we propose a method of automatically detecting mandibular third molars in the panoramic radiographic images and predicting the extraction difficulty and likelihood of inferior alveolar nerve (IAN) injury. Our dataset consists of 4903 panoramic radiographic images acquired from various dental hospitals. Seven dentists annotated detection and classification labels. The detection model determines the mandibular third molar in the panoramic radiographic image. The region of interest (ROI) includes the detected mandibular third molar, adjacent teeth, and IAN, which is cropped in the panoramic radiographic image. The classification models use ROI as input to predict the extraction difficulty and likelihood of IAN injury. The achieved detection performance was 99.0% mAP over the intersection of union (IOU) 0.5. In addition, we achieved an 83.5% accuracy for the prediction of extraction difficulty and an 81.1% accuracy for the prediction of the likelihood of IAN injury. We demonstrated that a deep learning method can support the diagnosis for extracting the mandibular third molar.**  
**Keywords:** mandibular third molar; extraction difficulty; inferior alveolar nerve (IAN) injury; deep neural network; panoramic radiographic image +
Footnote †: journal: Applied Sciences
# Age Assessment through Third Molar Teeth Developmental Stage in Children
###### Abstract  
Age determination is important for forensic evaluation and for determining criminal responsibility. Based on the development of permanent teeth, the Demirjian staging evaluation key is commonly used in research for estimating age using teeth developmental stage. The purpose of the current study was to correlate the dental developmental age of third molar teeth with the chronological age of adolescent patients. This study evaluated 324 panorex images of Israeli patients aged 8-21 years with known birth dates and genders. The dental developmental stage was compared to the patient's chronological age by gender and side (right/left and upper/lower jaws). This study also attempted to predict the patient's age using the third molar developmental stage. The study included 179 male and 145 female patients. There was no statistical difference between the right and left sides of the upper or lower teeth. A strong correlation was found between third molar development and chronological age for both the maxilla and mandible. In 100% of cases, patients in developmental stage >5 were over 12 years old in the maxilla and in 97% of cases, were over 12 years old in the mandible. Patients in developmental stage >6 were over 14 years old in 100% of cases in both the maxilla and mandible. Patients in developmental stage >7 were over 16 years old in 100% of cases in both the maxilla and mandible. This study found that third molars can be used to accurately estimate age in a narrow age range, with a strong correlation between third molar development and chronological age. Further research may be needed to understand the utility of this method and its possible usage in different countries around the world.  
age estimation; Demirjian staging; third molars 2023  
1
# Development of Digital Training Twins in the Aircraft Maintenance Ecosystem
## 4 Discussion
### Integrated Evaluation of Orchestration Efficiency
Simultaneously, the knowledge graph curriculum structure enforces prerequisite mastery constraints and dynamically manages dependency relationships within complex aviation maintenance training domains. The simulation results confirm not only the convergence of learner competence vectors but also the robustness and adaptability of the orchestration engine under diverse training conditions. One important implication is that the system consistently reduces the competence gap even when learners begin with varying skill levels and responsiveness rates. This supports the suitability of digital training twin systems for scalable deployment in environments where learners exhibit heterogeneous profiles, such as in airline maintenance academies or vocational institutions with mixed-experience cohorts. Moreover, the low variance in final competence outcomes--supported by the confidence intervals and boxplot visualizations--demonstrates the effectiveness of algorithmic orchestration in reducing instructor dependency and supporting semi-autonomous training ecosystems. This has implications for instructor workload optimization and the operational flexibility of training centers, especially in distributed or resource-constrained environments. The modular design of the reinforcement learning and federated learning layers also enables extensibility. For example, institutions could incorporate additional constraints such as certification timelines, economic costs, or regulatory constraints into the orchestration logic. Future real-world deployments could leverage this flexibility to dynamically adjust training plans in response to aircraft maintenance events, technician availability, or changing safety requirements, aligning digital twin ecosystems more closely with operational realities. Scalability is an important consideration for real-world deployment. The modular architecture of the proposed system supports horizontal scaling across larger learner populations by distributing orchestration logic to edge nodes and using containerized cloud services for model aggregation and analytics. The use of MQTT for low-bandwidth communication and Apache Kafka for high-throughput data streaming ensures that system performance remains stable even with increased concurrency and data flow. The competence modeling framework is domain-agnostic and can be extended to support multiple skill domains by augmenting the vector dimensionality and updating the gain profiles of new training resources. The orchestration logic, cost function, and reinforcement learning models are generalizable, provided that domain-specific taxonomies (e.g., Bloom levels, fidelity scores) are appropriately defined. These features position the system for use not only in aviation maintenance but also in other structured training environments such as medical simulation, logistics, or industrial equipment maintenance.
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 4 Localization Schemes and MAC Considerations
### Time Difference of Arrival
#### Advantages  
* Tags need to send only one packet per position estimate, decreasing energy consumption.
* The server has more computational power than the infrastructure/mobile nodes, resulting in faster position estimation.
* All anchors in range can be used for positioning, so we obtain more accurate results.
## 4 Data Analysis
### Temporal Patterns  
Probability distribution graphs were generated, and the distribution of service time of Mobikes for accessing rail transit was obtained, as illustrated in Figure 2a. The most significant variation exists between weekdays and weekends (holidays). In general, on weekdays (blue lines), there is a distinct peak in the morning and evening rush hour, which is more prominent in the morning, i.e., the trips during morning rush hour are more centralized. The evening rush-hour traffic extends from 17:00 to 22:00. The common phenomenon of extra work and long commuting times are the likely causes of this result. At weekends (red lines), a different trend is observed: the line becomes flatter as the commuter peaks diminish, indicating that more people's time becomes available for travel, and leisure trips are the primary trip purpose. The two lines tend toward 0 from 0:00 to 5:00 because subways are closed during this period.  
Similarly, the probability distribution graphs for the service starting time of Mobike trips from origin and trips to the destination on weekdays are shown separately, as illustrated in Figure 2b. In the morning rush hour, trips from origins (blue line) peak at approximately 7:00. According to the 2017 Beijing Transport Annual Report published by Beijing Transport Institute, the average commute time for Beijing is as high as 62 min. Longer commute times prompts commuters to start earlier. In the first half of the morning rush hour, the right shifts of the red line, compared with the blue line, illustrate that commuters spend approximately 1 h on average on the subway. Similarly, the blue line peaks at around 17:00 in the evening. The right shift of the red line is also detected, indicating that commuters typically spend around 1 h on the rail network after work to reach their home.
## 3 Fundamental Technologies for RegTech
### Policies and Standard Operating Procedures  
Over time, RegTech, particularly FinTech, has developed several well-known and tested policies and standard operating procedures. These are mainly aimed at acquiring the correct data from the correct source in a timely manner. They are based on the tools mentioned above. Here are some ways in which blockchain can be used in RegTech:  
* _Know Your Customer_: Blockchain can be used to create a decentralized and secure identity verification system that allows customers to prove their identity without the need for intermediaries. This can streamline the KYC process for financial institutions while maintaining data privacy and security [53; 54].
* _Regulatory Reporting_: Blockchain can be used to create a secure and transparent audit trail of regulatory reports, making it easier for regulators to monitor compliance and identify potential issues. This can potentially reduce the burden of regulatory reporting for financial institutions and improve the overall quality of the data [54].
* _Supply chain compliance_: Blockchain can be used to create a secure and transparent record of transactions in supply chains, enabling companies to track the movement of goods and ensure compliance with regulations. This can aid in reducing the risk of fraud and improving the overall efficiency of supply-chain management [55].
* _Smart contracts_: Blockchain can be used to automate compliance processes through the use of smart contracts, which are self-executing contracts that automatically enforce the terms of an agreement. This lowers the impact of errors and improves the overall efficiency of compliance processes [56].
## 3 Results and Discussion
### Coatings Characterization by Means of SEM and EDX  
The coatings characterization in the as-cladded conditions are shown in Figure 9. All of them present the homogeneous microstructure of fusion-deposited metallic coatings with visible interface between substrate and coating material. The microstructures of the interfaces in all researched coatings indicate a low dilution of coating material with the substrate. Continuous and quite sharp interface were revealed in all researched issues. No cracks, pores or other structural defects were visible. The fine-grained matrix is composed of primary dendrites. The matrix microstructure is a characteristic state formed in the conditions of nonequilibrium cooling of liquid alloys, which was also obtained in the arc surface-melting process. Table 4 compares the chemical composition of filler materials (as nominal state) and the chemical composition of deposited coatings researched by the EDX procedure. The chemical composition value of the deposited coating is the average value of three tests performed at half the thickness of the coating. The results of chemical composition comparison confirm a very low dilution of Inconel 625 and Alloy 33 and a little bit higher in the case of Inconel 718 and Stellite 6. This is clearly indicated by the increase in iron concentration on the coatings relative to the nominal material. The calculated dilution (Table 3) value of the coatings with the substrate is clearly correlated with the results of the chemical composition (Table 4) of coating materials in the nominal state and deposition by means of arc cladding.  
Figure 8: Diagram showing geometrical parameters of a CMT cladded single bead with operating parameters from Table 1: height (h), depth (d), width (w).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coating Material** & **Coating Thickness, h (mm)** & **Dilution, D (\%)** \\ \hline Inconel 625 & 2.2 (0.15) & 1.47 \\ Inconel 718 & 1.7 (0.22) & 6.55 \\ Alloy 33 & 2.06 (0.17) & 3.74 \\ Stellite 6 & 2.1 (0.13) & 5.51 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average of coating’s thickness value (with standard deviation) and dilution rate calculated on the base of macroscopic research.
## 6 Simulation and Ocean Experiments
### Ocean Experiments  
The practical ocean experiments were performed for evaluating the BER performance of the proposed method based on the D-LSTM and the conventional CNN based method. The learning process of the proposed D-LSTM was the same as the simulation subsection, which was different from the practical ocean channel. For the ocean experiments, the location and depths and the delay profile of the transmitter and the receiver are shown in Figure 14. The transducer was Neptune-D17B with a bandwidth from 12.5 kHz to 19.5 kHz, and the hydrophone was TC4032. The location of the ocean experiments was at a point in the west Sea of South Korea, which was 4.2 km apart from Sinzindo. In Figure 14c, the UWA channel of the practical ocean is shown, and the UWA channel was estimated by the Linear Frequency Modulation (LFM)-chirp that was attached before the data transmission only for the observation of the UWA channel. Note that the LFM-chirp was not used when the proposed signal was demodulated, and the practical ocean UWA channel is different from those of the simulations in Figure 12a,b.  
The parameters of the transmission modulator and the machine learning networks used for the ocean experiments were the same as those of computer simulations. The spectrogram examples of the received signal in the practical ocean experiments are displayed in Figure 15.  
For the BER performance calculation, 5000 whistles were transmitted, i.e., 15,000 bits were transmitted. The BER results of the ocean experiment are calculated in Table 2.  
In Table 2, the BERs of the whistle-by-whistle detection methods of the ML and the DNN are shown to be 0.36 and 0.37, respectively, which were large and not useful to the practical communications. The DAG-net1 and the DAG-net2 displayed better BER performances than the ML and the DNN, but the BERs of the DAG-nets themselves are still large. However, the proposed D-LSTM1 and D-LSTM2 that utilized the link information, demonstrated lower BER values than other algorithms. These results were well matched with those of the computer simulations. In addition, the fact that the proposed learning process is enough to obtain a low BER without learning of the practical ocean UWA channel is proven.  
Figure 13: Simulation bit error rate (BER) results (**a**) AWGN; (**b**) learning UWA channel; (**c**) test UWA channel. ML: maximum likelihood.  
Therefore, the proposed D-LSTM2 showed the best BER performance compared to other algorithms, and the whistle-by-whistle detection scheme was not used for the whistle based biomimetic communications, and the additional link information played an important role in increasing the BER performance. In addition, the proposed algorithm utilizes all whistles and preserves the maximum DoM, which is crucial to the UWA covert communications.
## 5 Conclusions  
In this current investigation, practical implementations are reexamined, primarily focusing on the disk-cone device utilized in industrial settings. We examine a unique blend of hybrid nanofluid containing copper (\(Cu\)) and (\(Al_{2}O_{3}\)) nanoparticles, which can be either in motion or stationary. In both scenarios, they can rotate in either a counter- or co-directional manner under the influence of a magnetic field. The effects of various physical parameters on velocity and temperature are elucidated through graphical representations. The key discoveries reached are as follows:  
* The rate of heat transfer and the velocity of the carrier fluid are boosted by increasing amounts of solid nanoparticles.
* Conversely, when it comes to the magnetic parameter, \(M\), an opposite trend is noticed. An increase in \(M\) leads to a decrease in fluid velocity and an increase in temperature, \(\theta\), due to the Lorentz force effect, which acts as a retarding force.
* The radial velocity profile, \(G\), is positively impacted by the local Reynolds numbers, \(Re_{\omega}\) and \(Re_{\Omega}\), which are based on the angular velocity of the disk and cone, respectively.
* The conclusion drawn is that the momentum boundary layer improves when the cone and disk spin in the same direction, whereas a decrease in the momentum boundary layer is observed when they rotate in opposite directions.
* It is evident that the temperature experiences a slight increase across the thermal layer under normal tip angles. However, there is minimal impact observed for minor gap angles due to the emergence of a critical power index \(n=-1\), \(0\), \(2\). Consequently, heat transfer from the disk surface ceases, rendering the fluid at the disk surface to act as an insulator, as there is no heat transfer occurring.
* This study employs an artificial neural network (ANN) approach to model and predict the complex flow dynamics of hybrid nanofluids in the conical gap between a rotating disk and cone surface, a novel application in fluid mechanics research.
* The research investigates the enhanced thermal and flow properties of hybrid nanofluids, providing new insights into their behavior in a rotating system, which has not been extensively explored before.  
* The combination of advanced computational techniques (ANN) with experimental fluid dynamics offers a unique perspective and potentially more accurate predictions in the analysis of hybrid nanofluid flows.  
Conceptualization, R.G., Z.A.B. and J.M.H.B.; methodology, Z.A.B. and J.M.H.B.; software, Z.A.B. and J.M.H.B.; validation, Z.A.B. and J.M.H.B.; formal analysis, R.G., Z.A.B. and J.M.H.B.; investigation, R.G., Z.A.B. and J.M.H.B.; resources, Z.A.B. and J.M.H.B.; writing--original draft preparation, Z.A.B. and J.M.H.B.; writing--review and editing, R.G. and Z.A.B.; supervision R.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 2 Study Area and Datasets
### Satellite Data Collection and Preprocessing
#### 2.2.3 Sentinel-2 Optical Data  
Sentinel-2 is a two-satellite constellation with 13 spectral bands. It offers three kinds of optical data with the spatial resolutions of 10-m, 20-m and 60-m and provides full and systematic coverage of land surfaces.  
Considering the coincident sampling with Sentinel-1 and clear-sky condition, the Sentinel optical data on 11 September 2019 were selected to monitor the scattering effects of vegetation with the spatial resolution of 10 m and resampled 3 m (See Table 1). The image was preprocessed for radiometric calibration and atmospheric correction using the atmospheric correction module (Sen2 Cor).  
Figure 1: Study area located in Wimmera, Victoria, Australia.