esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mathematical Modeling of the System Components  
The concern at this point was to design a dynamic model of the developed DC microgrid, which helped to verify the efficacy of the designed controller. The MATLAB/Simulink mathematical modules were used to build the DC microgrid model, including the battery systems, which used the component-equivalent circuits. Three different power supplies were considered in this model, which include PV, wind, and battery bank.
## 4 Experimental Results and Performance Evaluation
### Flow Table Occupancy Rate  
Flow table occupancy rate refers to the average number of installed forwarding rules in the switch flowtable to guide the routing process. The RSPO showed a small occupancy rate in the first study with ATT topology as shown in Figure 7. In this regard, FSL has a higher number of rules because of the unoptimized route which results in a longer path, and a longer path led to a large number of rules installation [33]. RPF showed a better occupancy rate than FSL. This is because RPF enjoyed share links which contribute to the lower rule's installation. However, the prior study by [PERSON] and [PERSON] [9] indicates that the network size contributes to many sets of paths which in turn increase rule consumption. Toward, this goal, the performance of the RPSO was also tested on a topology (Spain) with relatively high density, average node degree 4, as it tends to have large rules consumption. This way the traffic generation was increased which in turn required corresponding routing rules in the flow table. Interestingly, RPSO is always lower while a higher number of rules can be seen in the RPF and FSL. This will not only save more space but can also speed up flow rules updating time and routing convergence time [PERSON] et al. [38]. Therefore, this demonstrates that RPSO reduces the number of additional rules by 99% against RPF and FSL, respectively.  
Figure 4: Average Packet Delivery Ratio.  
Figure 5: Average Path stretch using ATT topology.  
Figure 6: Average Path stretch using Spain topology.
# SMARTerra, a High-Resolution Decision Support System for Monitoring Plant Pests and Diseases
## 3 Results
### Weather and Risk Maps  
The back-end section of SMARTerra generates several maps available online on the dedicated geoportal and published daily in a PDF report accessible only to internal users, primarily rice farmers and technicians involved in the campaign against locust spread. These maps include hourly visualizations of mean temperature, relative humidity, and rainfall accumulation obtained from the KED interpolation of measurements from the RUR network stations. Additionally, visualizations of daily maximum and mean temperature values, as well as rainfall accumulation, are available. Based on this information, the two risk models currently implemented provide maps of rice blast disease risk for all of Sardinia and specifically for the Oristano and San Gavino rice districts, as well as maps of the expected dates of locust egg hatching. SMARTerra is designed to facilitate the inclusion of new risk models. For example purposes, maps from the report showing daily average and rainfall accumulation are displayed in Figure 6. The thumbnails from the PDF report, available only internally, are shown in Figure 7; their purpose is to provide a quick overview of the produced raster maps along with a synthetic recommendation about field treatments from the LAORE agency (not included here).  
Figure 6: Daily maximum temperature (**left**) and daily rainfall accumulation (**right**) spatially interpolated from measured data with the KED technique.  
For end-users, whether farmers or agronomists, the alert levels are of significant interest and can serve as a decision support system for field operations. Figure 8 illustrates the risk indices computed from interpolated weather measurements and forecasts and the alert level obtained from the risk indices (see Section 2.2.5).  
Figure 8: Risk indices (**top**, **bottom left**) and alert levels (**bottom right**) maps for the rice blast disease obtained from interpolated measured and forecast weather data. See Section 2.2.5 for details.  
Figure 7: Thumbnails from the PDF report. From (**top left**) to (**bottom right**), the hourly mean temperature, mean relative humidity and rainfall from the measured and forecast data, the daily maximum and mean temperature and rainfall from the measured data, the risk indices and alert levels for the rice blast disease, and the threshold dates and accumulation totals for the locust egg hatching prediction.  
As for locusts, Figure 9 shows an example of the prediction model outcome: the cumulative sum of degree days (left) and the dates corresponding to egg hatching (right).
## 4 Exploratory Data Analysis
### Stock Market  
Data breaches can have significant implications for the stock market, often leading to fluctuations in the share prices of affected companies. When a data breach occurs, investor confidence may fade due to concerns about the company's ability to protect sensitive information, resulting in a decline in share value.  
As examples, we mention the historical stock price trajectories of three noteworthy companies within the dataset: Citigroup Inc., which endured the highest number of breaches; JPMorgan Chase, the second-most-affected company; and LinkedIn.com, which suffered the most voluminous breach in the dataset. They are represented in Figure 12, and it is possible to observe different market reactions to these data exposures, allowing for the observation of varying stock price responses to these data exposures.  
While, in some cases, a data breach can indeed contribute to a stock price drop, other factors can also be at play. A company's stock price decline can result from various factors, including data breaches, financial crises, regulatory modifications, and broader macroeconomic dynamics. Because of that, it is essential to emphasize that the connection between data breaches and stock price depreciation is not straightforward. In certain instances, a data breach can trigger a decline in share value by diminishing investor confidence in the company. This may be due to concerns that the company is incapable of adequately safeguarding the sensitive information of its customers and employees, or due to the apprehension that the breach might inflict financial or reputational harm.  
Additionally, poor management practices following a data breach, such as inadequate responses or compromised data security measures, can further exacerbate the situation and contribute to declines in share prices.  
Conversely, there are scenarios in which a drop in stock price can be attributed to other factors, such as financial crises or regulatory alterations. Likewise, if a company confronts new regulatory requirements, investors may opt to sell their shares amid concerns that they might struggle to comply with the updated rules. Understanding the nature of these relationships is essential when evaluating the impact of data breaches on a company's financial standing and its effect on share prices.  
Moreover, it is vital to consider that inadequate management practices can set a detrimental cycle that exacerbates crises and precipitates a decline in share values. These decisions, like implementing cost-cutting measures that compromise data security, can potentially catalyze new data breaches, further worsening the financial situation and resulting in a subsequent dip in share prices.  
Figure 12: Historic stock prices (USD) of the two companies with the most incident occurrences (**a**,**b**), and the breach with the most records exposed (**c**). Vertical dashed lines represent a data breach event.
###### Abstract  
This paper aims to demonstrate the effectiveness of photobiomodulation in accelerating orthodontic treatment with clear aligners in correcting an orthodontic malocclusion characterized by severe crowding and agenesis in an adult subject. An adult male patient with Class I malocclusion, a mild Class III skeletal base and severe crowding was treated with 88 Invisalign(@) (Align Technology, Santa Clara, CA, USA) pairs of aligners replaced every 5 days in combination with an Orthopulse(tm) (Biolux Research, Vancouver, BC, Canada) photobiomodulation device for home use by the patient. The total orthodontic treatment time was 440 days. Orthopulse(tm) is a photobiomodulation device that generates continuous beams of near-infrared light (NIR--near-infrared) with a wavelength of 850 nm and a power of 42 mW/cm\({}^{2}\) to produce an average energy density on the surface of the silicone impression of 9.3 J/cm\({}^{2}\). The combined use of clear aligners and the Orthopulse(tm) device allowed the patient to replace the aligners according to an experimental 5-day protocol to speed up the orthodontic therapy and, thus, resolve the malocclusion in less time than the manufacturer's standard.  
photobiomodulation; PBM; LLLT; orthodontic alignment; transparent aligner; accelerate treatment time; Orthopulse(tm); LED intraoral device; malocclusion; lateral incisor agenesis
## 4 Discussion and Conclusions  
Herein, the present work reveals that EPR technique can be successfully applied to the identification of irradiated clam shell, cyclina shell, fingernail, tooth, chitin and ox bone. All the biological samples show a good linearity of the measured EPR signals against the dose, despite the fact that the chitin and ox bone have lower radiation sensitivity. The optimized grain size for maximizing the signal is 1 mm for the clam shell, cyclina shell, and fingernail, 0.5 mm for ox bone and tooth and 0.1 mm for chitin. The fading of EPR signal with the passage of time can be considerably alleviated by control the storage temperature at \(-20\)\({}^{\circ}\)C for tooth, fingernail, ox bone and chitin and at \(45\)\({}^{\circ}\)C for clam shell and cyclina shell. These results preliminarily support that the biological samples have the potential to become an environmental dosimeter for retrospectively giving a fast rough estimate of dose level under the environment when radiation accidents occur.  
On the other hand, our results were obtained by sample preparation of the biological materials before radiation; this can be considered as one of main uncertainties of the presented analysis. In an actual event, real tissue will have been irradiated in situ before the samples are collected and analyzed, but the biological samples in the study are prepared before irradiation. The procedure for  
Figure 5: EPR signal fading for the six biological samples stored at \(-20\), 4 and \(45\)\({}^{\circ}\)C.  
sample preparation may affect the nature of the materials (such as water content) and its behavior to radiation, leading to any effect of ionizing radiation to be undependable as a surrogate for retrospective studies [29; 30]. This may need further calibration work to overcome the issues. Nevertheless, our study has shown the potential use of the six materials as environmental dosimeters and justify continued efforts to develop the method of environmental dose estimation.  
Conceptualization, C.-C.L.; methodology, J.-P.L.; validation, C.-C.L. and L.-H.L.; formal analysis, H.-H.L.; writing--original draft preparation, C.-C.L. and H.-H.L; writing--review and editing, L.-H.L.; supervision and project administration, F.-N.W. and C.-H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology of Taiwan (Project Nos. 108-2314-B-182-029-MY2). H.-H.L. was supported by the Institute for Radiological Research of Chang Gung University/Chang Gung Memorial Hospital (Project No. CMRPD1K0441, DMRP14). We would like to thank the Particle Physics and Beam Delivery Core Laboratory of the Institute for Radiological Research, Chang Gung University/Chang Gung Memorial Hospital, Linkou for their assistance.  
The authors declare no conflict of interest.
# A Compact Rectifier Design Method Utilizing Harmonics
## 3 Experimental Validation  
The proposed rectifier utilizing both the 2 nd and 3 rd harmonics was fabricated and measured. Figure 6 shows the schematic diagram of the measurement system. The signal source is Keysight E8257D, and the microwave power from the signal source is shunted by a coupler (the coupling degree is 20 dB). One way feeds the rectifier, while another way feeds the power meter to observe the input power of the rectifier. The spectrum analyzer is used to measure the amplitudes of the utilized harmonics.  
Figure 5: Simulated rectifying efficiency and the harmonic amplitude of different rectifiers at different input powers: (**a**) the rectifier utilizing both the 2 nd and 3 rd harmonics; (**b**) the rectifier utilizing the 2 nd harmonic; (**c**) the rectifier utilizing the 3 rd harmonic; (**d**) the harmonic suppression rectifier.  
Harmonic amplitude varying with the input power is shown in Figure 7a. When the input power varies from 10 to 18.3 dBm, the measured amplitude of the 2 nd harmonic varies from \(-10.6\) to \(-2.4\) dBm, and the 3 rd harmonic varies from \(-8.3\) to \(0.1\) dBm. Compared with the simulated results, the amplitudes of the 2 nd and 3 rd harmonics reduce about 1.5 dB and 1.4 dB, respectively. The difference between simulation and measurement may be caused by the errors of the SPICE model, the permittivity, and loss tangents of the substrate at the 2 nd and 3 rd harmonics.  
Figure 6: The schematic diagram of the rectifier efficiency measurement system.  
Figure 7: The measured and simulated result of the rectifier utilizing the 2 nd and 3 rd harmonics: (**a**) the measured and simulated harmonic amplitude varies with input power; (**b**) the measured and simulated rectifying efficiency varies with input power; (**c**) the measured and simulated rectifying efficiency varies with the load; (**d**) the measured and simulated output voltage varies with input power.  
The measured rectifying efficiency is calculated by  
\[\eta\ =\ \frac{P_{out}}{P_{in}}\ =\ \frac{V_{L}^{2}}{R_{L}\cdot P_{in}} \tag{5}\]  
where \(R_{L}\) is the load value, \(V_{L}\) is the DC output voltage across the load which can be read by a multimeter, and \(P_{in}\) is the input power of the rectifier.  
The measured rectifying efficiency varies with the input power and is shown in Figure 7b. The rectifying efficiency increases as the input power increases. When the input power increases from 10 to 18.3 dBm, the rectifying efficiency is greater than 50% and reaches the highest value of 71.3% at 18.3 dBm. When the input power is greater than 18.3 dBm, the output DC voltage approaches the reverse breakdown voltage of the diode, and the efficiency drops rapidly. Figure 7c shows the measured rectifying efficiency versus the load, and the peak rectifying efficiency is obtained at 350 \(\Omega\). Figure 7d shows the measured output DC voltage at different input power, and the maximum output voltage is about 4.5 V.
# A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis
## 5 Conclusions  
This paper proposes a hierarchical sparse discrimination autoencoder method for the intelligent diagnosis of rotating mechanical component faults. Through the experimental verification and analysis of the CWRU data sets and the private data sets, the following conclusions can be drawn:  
1. The proposed hierarchical sparse strategy is used to optimize the SSAE, giving different sparse activation and sparse regularization weights to the neurons in each layer of SSAE, which enhances the randomness of the network sparseness and achieves better the diagnostic effect. Using the PSO to obtain the best sparse parameters adaptively driven by data can improve network sparseness and avoid the complexity of manual parameter selection;
2. The class aggregation and class separability strategy can effectively enhance the classification ability of the autoencoder network. It is proved from the side that the proposed method can optimize the feature extraction performance of the autoencoder;
3. Compared with other methods, the standard deviation of the HSDAE method proposed in this paper is small, and the fault diagnosis accuracy is high, which reflects the effectiveness, reliability and stability of the HSDAE method in fault diagnosis.  
Regarding the future work direction, in this study, the hierarchical sparse strategy and discriminative distance are combined as the loss penalty term of the loss function. Therefore, specifying a better strategy is an important direction of subsequent research.  
Figure 16: Confusion matrix of the 20 th experiment of HSDAE model on data set H. (**a**) The confusion matrix of the HSDAE model in the training set; (**b**) the confusion matrix of the HSDAE model in the testing set.  
Figure 15: Comparison of experimental results of different methods in data set E.  
In addition, the method proposed in this paper can only perform accurate fault diagnosis for the same distributed data set. In the future, the domain adaptive method will be further studied based on this method to realize the fault diagnosis for different distributed data sets.  
Conceptualization, M.Z.; methodology, M.Z.; software, M.Z.; validation, M.Z.; formal analysis, K.X.; investigation, J.L.; resources, S.L.; data curation, R.L. and K.X.; writing--original draft preparation, M.Z.; writing--review and editing, M.Z. and S.L.; visualization, M.Z.; supervision, R.L. and X.L.; project administration, S.L. and J.D.; funding acquisition, K.X., S.L. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (51975276).  
Not applicable.  
The authors declare no conflict of interest.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 4 Results
### Accuracy Evaluation  
The traffic data completion error values for the MATRIX, LSTM, and IDTD models under various missing data scenarios are presented in Table 7, with a comparative analysis depicted in Figure 10.  
On the basis of the above results, it can be seen that IDTD outperformed the other two algorithms for all types of traffic data completion under different missing data situations:  
1. All three algorithms increased the completion error with an increase in the missing rate, among which MATRIX had the greatest effect on the completion with the missing rate, and IDTD has the least effect with the missing rate and could better adapt to severe missing data;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Missing Data & \(<\) 5\% & 5-10\% & 10–15\% \\ \hline Number of days & 7 & 12 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Overview of missing ETC gantry data in January.  
Figure 10: Traffic data completion comparison under different missing data scenarios.  
* Among the selected traffic data, the time headway completion error was the largest, whereas the traffic volume and interval average speed completion errors were similar. The time headway was influenced by the driving environment and independent choices of the driver. It was less effective for the low-dimensional MATRIX and only useful for the time-series LSTM.
# Animal Movement Prediction Based on Predictive Recurrent Neural Network
## 3 Methods
### Movement Interpolation  
Despite the high quality of tracking data, GPS location accuracy and positioning success (ratio between the observed and expected number of locations) are negatively influenced by a number of factors, including the terrestrial atmosphere, satellite constellation, environment of the transmitters (habitat, topography, and weather), and behavior pattern and movement intensity of the tagged animal [29]. When analyzing animal trajectories, lack of observation or erroneous geo records lead to inaccurate results. Hence, it is essential to estimate missing or erroneous animal locations based on correct geo records, which is called movement interpolation. Various interpolation methods, such as linear, cubic spline, and polynomial interpolation, have been widely used in studies on animal movement. Table 1 shows the major interpolation methods.  
All these methods perform interpolation using a few observation points. Since they do not consider the environmental features such as weather and terrain, the interpolation accuracy could be lowered. To alleviate this problem, we use the random forest method, which can consider both observation points and environmental factors. The random forest method works by constructing several weak decision trees, which are trained by a random subset of features, and producing the result by averaging or voting all individual trees. The basic principle of random forest is bootstrap aggregating (Bagging) to reduce the variance of predicted values. Bagging selects the specific number of training set randomly and fits for optimizing tree construction. For this reason, the random forest has excellent performance in classification and regression problems.  
Figure 3 shows an example of movement interpolation process. Two normally observed points are connected by a solid line. If there are any missing points, then they are connected by a red dotted line. For instance, in the figure, we can see that observation records are missing from 6:00 am to 8:00 am. To interpolate these missing records, we first split the geo records into training sets and test sets.  
trees. Once the training is over, we are given an optimized random forest model, which can be used for interpolating missing observation points. After training, we perform inbuilt cross-validation and calculate the prediction accuracy using mean absolute percentage error (MAPE) and root mean square error (RMSE) to obtain an optimal random forest. In particular, we used the grid search method to find optimal parameters for the random forest. To construct and train the model, we used scikit-learn [34], a python machine learning package.
# PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography
## 5 Discussion
### Limitations and Challenges  
Despite the promising outcomes, this study has several limitations that warrant discussion. Although the dataset used is considerably larger and more diverse than in many prior works, it does not fully encompass the variability encountered in real-world clinical populations. For instance, the curated dataset may underrepresent certain demographic groups, age brackets, and comorbid conditions, which can influence radiographic presentation. Furthermore, only frontal PA-view images were used, excluding lateral views or follow-up studies that might offer additional diagnostic value. Future research should focus on evaluating the model's performance across more heterogeneous, multi-institutional, and longitudinal datasets.  
Another critical challenge lies in the interpretability of deep learning models. While ensemble methods improve classification accuracy, they often increase architectural complexity and obscure the decision-making process. In clinical practice, understanding the rationale behind a model's prediction is essential for building trust among healthcare providers. To this end, integrating explainable AI (XAI) tools--such as saliency maps,Grad-CAM, or layer-wise relevance propagation--will be vital for providing transparent and interpretable outputs that can be validated by clinicians.  
In addition, artificially balanced class distributions (1:1 pneumonia vs. non-pneumonia) and the exclusion of low-quality radiographs--such as those with motion artifacts, low contrast, or suboptimal positioning--may introduce bias and limit the model's robustness in real-world clinical scenarios. While improving training stability, these pre-processing steps reduce the representation of actual clinical variability where pneumonia prevalence is often lower, and image quality may be compromised.  
Finally, although PELM demonstrates strong performance under experimental conditions, further research is needed to evaluate its real-time deployment feasibility, integration into clinical workflows, and validation through prospective studies in actual healthcare environments.