esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance Indices and Confusion Matrix  
The proposed model exhibited average performance indices (precision, recall, F1-score, Area Under the Curve) over 0.84 and 0.88 for detecting periodontitis compromised and healthy teeth, respectively. The null hypothesis H\({}_{0}\) was rejected because the Area Under the Curve values for detecting all three classes were over 0.88 (Table 2). All cross-validation data are listed in Supplementary Table S1.  
Figure 2 shows the confusion matrix and Area Under the Curve of the first cross-validation result, which showed the highest Area Under the Curve value. Among 9804 validation Regions of Interests (ROIs), 92% were classified correctly, while 19% of periodontitis, 8% of normal, and 4% of edentulous cases were misclassified.  
When excluding the Area Under the Curve values for normal and periodontitis in the anterior region, the four performance indices showed a tendency to be high in the order of edentulous, normal, and periodontitis for all regions. Additionally, the total value of the four performance indices in each region was over 0.89, and each performance index value showed a similar tendency across regions (Table 3). All cross-validation data with detailed region and class information are listed in Supplementary Table S2.  
Two representative cases of the Faster R-CNN object detection method were randomly selected to demonstrate the performance of the model. The ground truth bounding box and raw image are presented in Figure 3A,C, respectively. The results obtained from the Faster R-CNN object detection deep learning method are illustrated in Figure 3B,D, along with the corresponding prediction probabilities. As shown in Figure 3B, the periodontal state of all 12 segments was correctly detected. However, in Figure 3D, the model misclassified the periodontal state of the Lt. maxillary anterior, bilateral mandibular anterior, and Lt. mandibular pre  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **AUC** \\ \hline
**whole** & 0.90 & 0.91 & 0.90 & 0.91 \\ \hline
**edentulous** & 0.97 & 0.98 & 0.97 & 0.99 \\ \hline
**healthy** & 0.88 & 0.89 & 0.89 & 0.88 \\ \hline
**periodontitis** & 0.86 & 0.84 & 0.85 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average performance indices of the 5-fold cross-validation result.  
Figure 2: Confusion matrix (**A**) and AUC (**B**) curve of the first cross-validation result.
## 3 Results and Discussions
### Reproducibility of Radiation-Induced Coloration  
Though the radiation-induced red color of PAISiN was smoothly cleared and reverted to the initial white state at room or higher temperature, it was unclear whether its dosimetric properties were well recovered after repeated irradiation or long-term storage. Thus, after performing the experiments at 3 different temperatures (totally about 40 days after the PAISiN samples were synthesized), the same set of PAISiN cells were irradiated again with \({}^{137}\)Cs source \(\gamma\)-rays in the same way (see the Materials and Methods section), and then time changes of the green component of the scanned PAISiN images were examined.  
The results of the 2 nd irradiations are shown in Figure 6. As recognized from a comparison of both graphs, the sensitivity of radiation-induced coloration notably reduced in the case of 20 Gy irradiation, while few changes were observed for 5 Gy irradiation. It was seen that the time needed for erasing the color became longer for all dose levels. These results indicate that some degradation of chemical properties of PAISiN in relation to coloration/decolorization occurred following repeated ionizations or thermal stimulations at certain temperatures. As the weight of any sample during about 1 month after the material synthesis was less than 0.5%, the increase of density caused by the loss of water was unlikely to have brought such changes in its dosimetric property. More studies to clarify the mechanism of these dose-dependent changes of sensitivity are desirable to be performed.
# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 2 Material and Methods
### Satellite Derived Observations
This study makes use of the GEOV2, 1 km spatial resolution and 10-day steps in near real time product. Its development has followed several steps including (1) applications of a neural network for providing instantaneous estimates from SPOT-VGT reflectances, (2) a multi-step filtering approach to eliminate contaminated data (e.g., affected by atmospheric effects and snow cover), and (3) temporal techniques for ensuring consistency and continuity as well as short termprojection of the product dynamics [73] (LAI Product User Manual, [[https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/GIOGL1_PUM_LAI300m-V1_I1.60.pdf](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/GIOGL1_PUM_LAI300m-V1_I1.60.pdf)]([https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/GIOGL1_PUM_LAI300m-V1_I1.60.pdf](https://land.copernicus.eu/global/sites/cgls.vito.be/files/products/GIOGL1_PUM_LAI300m-V1_I1.60.pdf)), last access January 2019).
# An Intelligent Control Strategy for a Highly Reliable Microgrid in Island Mode
## 3 Results and Discussion
### Simulating Results and Analysis
#### 3.1.5 Operating Model without Reconfigured Structure  
Inverter 1 and inverter 2 have completely the same structure and control strategy in this simulating model. There is not reconfigured structure and corresponding pulse-resetting in inverter 1. \(V_{1}\) in the inverter 1 is faulty at t = 0.5 s. Inverter 2 works in the normal state. The simulation results are shown in Figure 13.  
According to Figure 13, the output current waveforms of inverter 1 and inverter 2 are distorted and three-phase imbalanced when inverter 1 is faulty. The circulating current is increased greatly. That is to say, the microgrid can't work in its normal state because inverter 1 is faulty. The proposed reconfigured structure and corresponding control strategy can improve greatly the reliability of the microgrid.  
Figure 11: Operating mode 3. (**a**) The polarities of the inverter 1 midpoint voltages; (**b**) Output voltages of inverter 1; (**c**) Output currents of inverter 1.  
From all the simulation results, we can see that the output voltages and currents in operating modes 2 and 3 are similar, because inverter 1 is reconfigured to have the  
Figure 12: Operating mode 4. **(a)** The polarities of the inverter 1 midpoint voltages; **(b)** The output voltages of inverter 1; **(c)** The output currents of inverter 1; **(d)** The output voltages of inverter 2; **(e)** The output currents of inverter 2; **(f)** The circulating currents between inverter 1 and inverter 2.  
Figure 13: Simulation results without reconfigured structure and pulse-resetting. **(a)** The output currents of inverter 1; **(b)** The output currents of inverter 2; **(c)** The circulating currents between inverter 1 and inverter 2.  
fault-tolerant function. The output voltages and currents in operating modes 1 and 4 are also similar. It means that if inverter 1 is reconfigured after one or two power switches are in open circuit fault, there is at least one inverter operating continuously in the off-grid microgrid. In operating mode 1, this is a normal operating mode. Because all parameters of the two inverters are same, there is no circulation current between them. In operating modes 2 and 3, only inverter 1 is working after fault, there is no need to discuss circulation currents. In operating mode 4, Figure 12f shows the circulating currents hardly run between the two inverters, whereby the intelligent control strategy is verified. The simulation results without the proposed reconfigured structure and corresponding control strategy are given, which are distorted and imbalanced if inverter 1 is faulty. Compared these results, it is obvious that the reliability of the off-grid microgrid is greatly improved under the proposed reconfigured structure and control strategy.
# Loosely Coupled GNSS and UWB with INS Integration for Indoor/Outdoor Pedestrian Navigation +
## 1 Introduction
Furthermore, in order to further improve positioning, the inertial data acquired during pedestrian motion can be integrated with the GNSS data in a loose coupling integration, typical in kinematic positioning. The data fusion algorithm exploits the similarity between GNSS and UWB to perform a state estimation based on a loosely coupling architecture and a geofence trigger for switching between indoor spaces and the outdoor environment. The GNSS receiver used is a single-frequency, multi-constellation u-blox Neo M8T [15] while the UWB system is the Pozyx accurate positioning system with an integrated Micro Electro-Mechanical IMU (MEMS IMU) [16]. The main contributions of this study are the following:  
* the integration of different low-cost commercial sensor in an integrated multi-sensor platform, able to manage the data stream from different sources, their storage and their use in real time positioning estimation algorithm;
* the development of an algorithm of seamless navigation both in term of continuity of the solution provided, thanks to a geofence approach, and in term of coordinate consistence between outdoor and indoor positioning module;
* the quantitative and qualitative evaluation of the performance of the proposed method in comparison with stand-alone solutions. The remainder of this paper is organized as follows. Section 2 introduces a review of works related to UWB hybridization for positioning. In Section 3 the Material and Methods used to perform the seamless navigation are provided. The theoretical approach of the present work is described together with the hardware and software used to acquire real-time data. Section 4 describes the experimental setup, the geoferencing procedure of the test area and the acquisition of the reference solution. Section 5 analyses in depth the UWB measurements acquired during the tests, in particular the rangesbetween the fixed anchors and the moving tag. In Section 6 the results of the positioning estimation and its validation is discussed. Finally, Section 7 presents the conclusions and future work.
## 5 Results and Discussions
### Temperature Susceptibility of THFS Modified Asphalt Binder
#### 5.5.3 Resistance to Thermal Cracking at Low Temperature  
Figure 6 reports the \(S\) values of asphalt binders over test temperatures.  
Figure 6 demonstrates that the \(S\) values of asphalt binders increased, compared to SK-90 under an identical test temperature. It showed that THFS modified asphalt binder was more prone to thermal cracking at low temperatures compared to SK-90. A possible explanation for this tendency was THFS with a higher asphaltene and lower malthene content reduced the flexibility and elasticity of asphalt binder at low temperatures.  
Figure 4: Relationships between \(lg(G^{*}\)/\(sin\delta)\) of Asphalt Binder and Temperature.  
Figure 5: \(G^{*}\)-\(sin\delta\) Values of Asphalt Binders over Temperatures.  
Under the identical conditions of THFS content, the \(S\) values of asphalt binders decreased as the test temperature increased and had a good linear correlation with temperature with a high correlation coefficient \(R^{2}\) ranging from 0.9646 to 0.9853. If taking the logarithm of \(S\) and temperature \(T\), it was found that the \(lg(S)\) had a good linear correlation with test temperature, and is given as Equations (4) and (5).  
\[lg(S)=STS\cdot T+C \tag{4}\]  
\[STS=\frac{lg(S_{1})-lg(S_{2})}{T_{1}-T_{2}} \tag{5}\]  
in which: \(S\) is creep stiffness (MPa);  
\(T\) is the test temperature (\({}^{\circ}\)C);  
\(C\) is the regression coefficient;  
\(STS\) is the slope of \(lg\) (\(S\)) to the test temperature.  
The \(STS\) is the slope of \(lg\) (\(S\)) to the test temperature and is followed in Equation (5), which can be used to detect the temperature susceptibility of asphalt binder at low temperatures. A higher absolute \(STS\) value indicates that asphalt binder is more sensitive to thermal cracking at low temperatures. The \(STS\) values of asphalt binders are listed in Figure 7.  
According to the test results in Figure 7, it could be found that the absolute \(STS\) values of asphalt binders were lower than that of SK-90 and 10% THFS modified asphalt binder had the lowest absolute  
Figure 6: \(S\) Values of Asphalt Binders over Temperatures.  
Figure 7: Relationships between \(lg(S)\) of Asphalt Binders and Temperatures.  
_STS_ value. It showed that the addition of THFS resulted in a decrease in the temperature susceptibility of asphalt binder at low temperatures. The asphalt binder with a higher THFS content was less sensitive to thermal cracking at low temperatures.
###### Abstract  
In this paper, we present a robust containment control design for multi Unmanned Aerial Vehicle Systems (UAVs) based on the Data Distribution Service (DDS) middleware and \(\mathcal{L}_{1}\) adaptive controller. The Data Distribution Service middleware, \(\mathcal{L}_{1}\) adaptive controller and graph theory technique are utilized for the navigation of the UAVs. The \(\mathcal{L}_{1}\) controller is utilized as a local controller for each UAVs and the graph theory approach is utilized to constitute the followers inside their leaders. Finally, the DDS Middleware is used to exchange data between the followers and their leaders. Robust adaptation of the \(\mathcal{L}_{1}\) controller makes the system robust with a high level of performance. Matlab simulation verified the robustness of the \(\mathcal{L}_{1}\) controller. We provide stability proofs using Lyapunov analysis for the UAVs framework.  
**Keywords:** containment control; leader-follower control; unmanned aerial vehicle system; data distribution service middleware; quality of service +
Footnote †: journal: Applied Soft. Sci. 2020, 10, 4572; doi:10.3390/app10134572
# Ultrasound for Meat Processing: Effects of Salt Reduction and Storage on Meat Quality Parameters
## 3 Results and Discussion
### Colour Values  
Different studies have reported that ultrasound treatment can cause changes in meat appearance due to the dissipation of ultrasound energy on the upper surface of the meat [14; 34]. Results of the colour parameters and total colour difference in this study are presented in Table 2. No significant interactions (\(p>0.05\)) were found between the independent variables. The only significant effect on the colour parameters was caused by the storage days: the main effect on the mean of lightness (\(L^{\star}\)) and redness (\(a^{\star}\)) of the samples over storage time, showed no statistically significant differences among the treatments at \(p=0.078\) and \(0.275\), respectively. Significant differences (\(p<0.001\)) were however observed for the \(b^{\star}\) (yellowness) values, which increased during storage in all samples, regardless of the processing treatment used. As some studies suggest, the lack of colour changes after US treatment can happen if the heat generated by US processing is insufficient to denature proteins and pigments [35]. Given the conditions used in this study where the system was constantly maintained cool, this factors could have played an important role. Similarly, a recent study [36] reported no statistically significant effect of ultrasound intensity, ultrasound duration, and storage time on meat redness, lightness and yellowness of bovine _Longissimus dorsi_. The total colour difference (\(\Delta\)E) parameter is used to indicate the degree of colour difference between samples; values can be ranked as not noticeable (0-0.5), slightly noticeable (0.5-1.5), noticeable (1.5-3.0), well visible (3.0-6.0) and great (6.0-12.0) [37]. According to this scale, noticeable colour differences (\(\Delta\)E > 2) could be observed between all the samples processed with US, becoming well visible (\(\Delta\)E > 5) after storage when compared to the initial colour of the pork primed by immersion (Table 2). However, well visible changes (\(\Delta\)E > 3) were also measured for the control samples after 60 days storage, suggesting that natural changes in colour happen during storage but, if ultrasound is used for processing, the total colour changes could be more easily identified by a visual assessment.
# Effect of Temperature on the Modal Variability in Short-Span Concrete Bridges
## 4 Discussion
### Analysis of Modes Considering Temperature Dependency
In view of Figure 12a, Seolmun Bridge has a lower elastic modulus than Gajwa Bridge, which indicates that Seolmun Bridge has suffered more damage. In Figure 12b showing the change in the rotational stiffness of the elastomeric bearings, the rotational stiffness is seen to decrease with higher temperatures. Figure 11: FE models for model updating: (**a**) Seolmun Bridge; (**b**) Gajwa Bridge. Figure 13 compares the temperature-dependent modes identified from the measured data with the modes of the updated models. In the case of Gajwa Bridge for which model updating was conducted by varying the elastic modulus of concrete only, relatively good agreement between the measured modes and the model modes is observed for the first and fourth modes. However, disagreement occurs for the second and third modes. Such disagreement can be related to the compatibility of the FE model as well as to the accuracy of the modal identification. However, the consistency of the extent of the change in the natural frequencies related to the temperature change proves that the variation of the elastic modulus of concrete in the rahmen bridge is one main factor influencing the temperature dependency of the bridge modes. Figure 13a compares the modes for Seolmun Bridge. Compared to the model without elastomeric bearings, the model reflecting the variations of both elastic modulus of concrete and rotational stiffness of the elastomeric bearings provides better agreement with the measured modes. However, the absence of significant difference between the two models proves once again that the variation of the elastic modulus of concrete is one main factor influencing the temperature dependency of the bridge modes.
## 3 Optimal Service Provisioning Framework
### Security and Privacy Consideration  
ML-RASPF focuses on optimizing latency and service delivery rate, but smart healthcare deployments must also ensure data security and patient privacy. To support secure and privacy-preserving service provisioning, the framework can be extended with FL or differential privacy mechanisms to ensure that sensitive patient data are processed locally at the edge or mist layer. Furthermore, blockchain-based technologies, such as privacy-preserving blockchain \(P^{2}\)B-Trace [22], can be integrated to enable tamper-proof service logging and decentralized trust management. These enhancements would allow ML-RASPF to ensure both operational performance and regulatory compliance in highly sensitive healthcare environments.  
The proposed framework can be extended with security-aware ML techniques to further strengthen the resilience of ML-RASPF against malicious behavior and unauthorized access. In particular, integrating differential privacy into LSTM-based traffic forecasting can help prevent inference attacks and protect sensitive healthcare patterns. Moreover, FL can be adopted to collaboratively train prediction models across edge nodes without exposing raw data, mitigating risks from data filtration. Future versions of ML-RASPF may also incorporate anomaly detection mechanisms to identify credential abuse (e.g., traffic mimicry or credential sharing) based on deviations from established traffic behaviors.
## 3 Proposed Approach: Quantum Key Distribution (QKD)
### Analysis and Verification of the Proposed Method
#### 3.3.2 Security Analysis: Resistance to Cyber Attacks
Future sensitivity analyses will also address factors such as UAV vibration-induced misalignment and variations in the receiver aperture size, with preliminary studies suggesting that these parameters can significantly affect both the communication range and error probability. The expected outcomes of this study are anticipated to confirm the viability of the proposed approach for ensuring data security in UAV-based GIS applications for agricultural monitoring. Error correction will be achieved via hybrid LDPC and turbo codes, which allow for adaptive error thresholds responsive to atmospheric conditions. Initial simulations reveal a 30% reduction in the reconciliation time compared to fixed-threshold methods, thereby supporting the real-time performance. Moreover, the developed encryption algorithm is projected to offer high data transfer speeds, low computational overhead, and robust resistance to various cyber-attacks, ultimately enhancing the efficiency and security of agricultural operations. The simulation results based on the BB84 protocol--accounting for factors such as atmospheric attenuation, geometric loss, and receiver characteristics--indicate that the key generation rate is sufficient for secure, real-time data transmission, even under UAV constraints such as limited power and size. The experimental outcomes closely align with the theoretical predictions, exhibiting deviations of less than 5% in both the key generation and error rates under simulated low Earth orbit (LEO) conditions. This consistency underscores the robustness of the simulation models and their relevance to real-world applications. Table 7 summarizes the expected simulation results for different scenarios. These values, derived from extensive simulations that consider atmospheric attenuation, geometric loss, and detector efficiency, are based on realistic models and parameters extracted from the existing literature and experimental data [3,16,17]. Specifically, the communication range estimates incorporate free space loss and atmospheric attenuation--which depend on the laser wavelength (1550 nm, as noted in the abstract), weather conditions, and atmospheric aerosol concentrations. The key generation rate is computed based on the laser intensity, detection efficiency, and quantum channel data rate, while the quantum channel error rate accounts for detector noise, dark counts, and environmental influences.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 3 Study Methods
### Determination of Absorbed Photosynthetically Active Radiation
#### 3.2.1 Determination of Total Solar Radiation  
The direct calculation of the total solar radiation was not included in this study because the small number of meteorological stations that provide total solar radiation. Therefore, the daily solar radiation was calculated by using the hours of sunshine, and the total solar radiation at 5-day intervals is incorporated separately by using  
\[SOL(x,t)=\Big{(}a+b\frac{n}{N}\Big{)}R_{a}, \tag{3}\]  
where \(SOL(x,t)\) is the same as for Equation (2), and \(a\) and \(b\) are empirical coefficients that are determined from the measured solar radiation. In this study, the method of [PERSON] et al. (2008) was used to calculate \(a\) and \(b\). For the Beijing area, \(a=0.22\) and \(b=0.72\). The ratio \(\frac{n}{N}\), where \(n\) is the hours of sunshine and \(N=24\) h, gives the fraction of sunshine time for one day. Finally, \(R_{a}\) [MJ m\({}^{-2}\)] is the solar intensity at ground level.  
The quantity \(R_{a}\) can be calculated from technical documents published by Food and Agriculture Organization of the United Nations (FAO) (Food and Agriculture, 1998), which is related to the distance \(d\) between the Sun and Earth, the latitude \(\varphi\), the solar sunset angle \(\omega_{0}\), the solar constant \(S_{0}=0.082\) MJ m\({}^{-2}\) min, the solar declination delta \(\delta\), and the number \(J\) of days. \(R_{a}\) is calculated by using  
\[R_{a}=\frac{24*60}{\pi}\times S_{0}d(\omega_{0}sin\phi\sin\delta+cos\varphi cos \delta sin\omega_{0}), \tag{4}\]  
In Equation (4), the parameters are given by  
\[\text{d}=1+0.033\cos\Big{(}\frac{2\pi}{365}\text{J}\Big{)}, \tag{5}\]  
\[\omega_{0}=arccos(-tan\delta), \tag{6}\]  
\[\delta=0.409\sin\Big{(}\frac{2\pi}{365}\text{J}-1.39\Big{)}, \tag{7}\]