esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of and Experimental Study on Drying Equipment for _Fritillaria ussuriensis_
###### Abstract  
To address the problems of the time consumption, labor intensiveness, easy contamination, uneven drying, and impact on the medicinal efficacy of _Fritillaria ussuriensis_ in the traditional drying method, the hot-air-drying characteristics of _Fritillaria ussuriensis_ were studied. The changes in the moisture ratio and drying rate of _Fritillaria ussuriensis_ under different hot-air-drying conditions (45 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, 65 \({}^{\circ}\)C) were compared and analyzed. Six common mathematical models were used to fit the moisture change law, and it was found that the cubic model was the most suitable for describing the drying characteristics of _Fritillaria ussuriensis_. The R\({}^{2}\) values after fitting under the three temperature conditions were all greater than 0.99, and the maximum was achieved at 45 \({}^{\circ}\)C. Based on the principle of hot-air drying, a drying device for _Fritillaria ussuriensis_ with a processing capacity of 15 kg/h was designed. It adopted a thermal circulation structure of inner and outer drying ovens, with the heating chamber separated from the drying chamber. The structural parameters were optimized based on Fluent simulation analysis. After optimization, the temperature of each layer was stable at 338 K \(\pm\) 2 K, and the pressure field and velocity field were evenly distributed. The drying process parameters of _Fritillaria ussuriensis_ were optimized based on response surface analysis, and the optimal process parameters were obtained as follows: inlet temperature: 338 K (65 \({}^{\circ}\)C), inlet air velocity: 3 m/s, and drying time: 10 h. The simulation results showed that the predicted moisture content of _Fritillaria ussuriensis_ under the optimal working conditions was 12.58%, the temperature difference of _Fritillaria ussuriensis_ at different positions was within 0.8 \({}^{\circ}\)C, and the humidity deviation was about 1%. A prototype of the drying device was built, and the drying test of _Fritillaria ussuriensis_ was carried out. It was found that the temperature and moisture content of _Fritillaria ussuriensis_ were consistent with the simulation results and met the design requirements, verifying the rationality of the device structure and the reliability of the simulation model. This design can significantly improve the distribution of the internal flow field and temperature field of the drying device, improve the drying quality and production efficiency of _Fritillaria ussuriensis_, and provide a technical reference for the Chinese herbal medicine-drying industry.  
drying device; _Fritillaria ussuriensis_; drying test; Fluent simulation; thermal fluid-solid coupling
## 5 Clinical Implications and Future Applications  
Early diagnosis and timely management of pulp exposures in primary and immature permanent teeth are critical to maintaining tooth vitality, supporting continued root development, and preserving natural dentition. According to the latest AAPD Clinical Practice Guidelines (2024) [31,32], vital pulp therapy (VPT) procedures--including indirect pulp treatment, direct pulp capping, partial pulpotomy, and full pulpotomy--are appropriate for primary teeth with normal pulp or reversible pulpitis, and for immature permanent teeth with vital pulp, including some cases with signs of irreversible pulpitis.  
In primary teeth, pulpotomy remains the most commonly indicated VPT technique when caries removal results in pulp exposure. Full pulpotomy with a bioactive material such as MTA or Biodentine is recommended over formocresol due to superior biocompatibility and clinical success. For immature permanent teeth, partial or full pulpotomy is favored to promote continued root maturation (apexogenesis) when pulp vitality is retained.  
For widespread clinical adoption, ideal bioactive materials should offer ease of handling, fast setting times, excellent sealing ability, and high biocompatibility. Recent innovations--such as calcium silicate-based cements, platelet-rich plasma (PRP), and nitric oxide-releasing nanomatrix gels--exemplify materials that not only protect exposed pulp but also actively promote tissue regeneration. These materials enhance healing by releasing bioactive molecules and growth factors that modulate inflammation and stimulate dentinogenesis.  
In line with AAPD guidelines, selection of VPT materials and techniques should be guided by pulpal diagnosis, bleeding control, restoration integrity, and the tooth's strategic value. Integrating regenerative strategies into routine clinical practice can transform the management of pulpal-involved teeth. A shift toward biologically based, minimally invasive therapies prioritize tissue preservation and patient-centered care. Continued advancements in biomaterials and delivery systems will further support this transition, improving long-term treatment success in both pediatric and adult populations.
# Neural Network-Based Cost-Effective Estimation of Useful Variables to Improve Wind Turbine Control
## 2 Wind Turbine and Wind Speed Modelling and Wind Turbine Control
### Full Envelope Control  
Wind turbine control normally consists of two components: control synthesis (including control regulation in each operating mode) and determination of the operating strategy in the speed-torque plane (such as the one shown in Figure 3). The former is related to designing a linear controller in each mode and the latter includes switching smoothly at appropriate wind speeds between different modes. Here, MPC is selected as the control algorithm; however, others, such as linear quadratic Gaussian and H\({}_{\infty}\) controllers, are equally pertinent. As shown in Figure 3, in modes 1 and 3, which could be considered buffering zones, constant speeds are maintained. In mode 2, the \(C_{pmax}\) curve is tracked to extract as much energy as possible from the wind and, in mode 4, the wind turbine stalls to maintain the rated power in high wind speeds. The green curves represent wind speeds increasing from 4 to 24 m/s with an increment of 0.5 m/s. Detailed information about wind turbine controllers is available in the literature [19; 21].  
Figure 3: Control strategy in the speed-torque plane; the green curves represent wind speeds increasing from 4 to 24 m/s with an increment of 0.5 m/s.  
The frequency of each mini-grid can be adjusted via a centralised AC-DC-AC power converter, allowing a variable-speed control strategy, as opposed to the constant-speed control strategy, to be adopted here even though the turbines are constant speed machines. Further details on the type of wind turbines adopted here can be found in the literature [16].
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 3 Analysis of the Positioning Solution and Observation Data Quality  
The availability of carrier phase signals (in two frequencies) and correction streams as well as the signal quality determine the solution type, namely fixed, float, code and navigated solutions, which are introduced in this section.  
Figure 1: Overview of the kinematic experiment. (**a**) Eight-shaped trajectory driven in an urban environment of Hannover near Leibniz University campus; A to K are the way-points which show the direction of the drive; this trajectory includes parts with quite open sky situations in the parking lot (J to A), and some parts with very difficult sky visibility (D to F). Different colors of way-points are merely chosen for better visibility of the image. (**b**) Experimental set-up with RTK receivers mounted on top of the van. (**c**) Installation of the navigation-grade IMU (iMAR iNAT-RQT-4003) inside the van.
# Computationally Efficient Robust Color Image Watermarking Using Fast Walsh Hadamard Transform
## 4 Performance Evaluation and Simulation Results  
### Performance Measures  
The performance of the algorithm is measured and compared based on the following metrics. Mean Square Error (MSE), is the measure of the deviation of watermarked image from the original cover image, which is given by,  
\[\text{MSE}=\frac{1}{mn}\sum_{i=1}^{n}\sum_{j=1}^{m}(C(i,j)\,-WI(i,j))^{2} \tag{6}\]  
where \(C\) is the original cover image intensity, \(WI\) is watermarked image Intensity, \(m\) and \(n\) are the width and height of the cover image. Peak Signal to Noise Ratio (PSNR) is the measure of similarity of the original cover image and the watermarked image. It is the direct measure of perceptual quality.  
\[\text{PSNR}=20\log\frac{(\max(\max(C)))}{\sqrt{\text{MSE}}} \tag{7}\]  
To quantify the quality of the extracted watermark the Bit Error Rate (BER) is used as indication of the similarity of the original binary watermark and extracted watermark. The BER counts the bit by bit difference between the original watermark and the extracted watermark, thus indicating the efficiency and robustness of the watermarking algorithm against image distortions and attacks.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 6 Conclusions  
This paper presents a systematic analysis of the performance of hybrid signaling SWIPT systems with nonlinear PAs. Using PA efficiency and SNDR as performance metrics, we have presented a quantitative analysis of the conflicting design objectives in SWIPT. Our study has taken into account the variations in PA types, PA models, and SNDR metrics. Theoretical and simulation results have revealed a concave-shaped tradeoff relationship between the SNDR and PA efficiency. Furthermore, a nearly linear relationship has been revealed between the energy-to-information signal power ratio (in dB) and the SNDR. It has been shown that, for the SNDR value, the PA efficiency differences between Class A and Class B PAs are around 20 dB. Our study reveals a fundamental tradeoff in SWIPT systems and provides useful guidelines for the design of practical SWIPT systems with hybrid signaling schemes.  
Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]); software, L.C.; validation, [PERSON] ([PERSON]), L.C. and X.W. ([PERSON]); formal analysis, [PERSON] ([PERSON]) and L.C.; investigation, L.C.; resources, L.C.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, W.Q.; writing--review and editing, L.C.; visualization, [PERSON]; supervision, [PERSON]; project administration, J.H.; funding acquisition, K.Y. 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 (Grant No. U1705263) and the Science and Technology Key Project of Fujian Province, China (No. 2019 HZ020009).  
**Acknowledgments:** We express our thanks to the editors and anonymous reviewers for their valuable comments.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Conceptual Design  
### Aerodynamic Assessment  
The wing load calculation was performed by the CIRA aerodynamic team by using the in-house (Unsteady Zonal Euler Navier Stokes) RANS (Reynolds-Averaged Navier Stokes) flow solver (compressible RANS equations on structured multi-block domains). As explained in [22,23], the aerodynamic assessment was obtained by considering as a preliminary assumption a constant load distribution along the span of the wing. The 2D profile for the calculation was selected as the one corresponding to the peak of the wing load. The aerodynamic loads evaluation was achieved under two conditions:  
* A flapped (high-lift) condition corresponds to the maximum extended speed for the flap (VF) of 90 m/s and a load factor equal to 2.
* A clean (high-speed) condition related to the dive speed (VD) and load factor equal to 2.5.  
The high-lift condition was selected for the sake of the MF DEMO conceptual design, depicted in Figure 4 in terms of 2D aerodynamic pressure distribution (orange curve) and reference airfoil profile in morphed configuration (blue curve).  
The MF trailing edge presented here is part of the high-lift system, which also includes a morphing droop nose (designed by Politecnico of Milan, PoliMi, Milan, Italy) derived  
Figure 4: 2D Aerodynamic pressure distribution (orange curve) and reference airfoil profile (blue curve) in morphed configuration at VF and load factor = 2.  
Figure 3: Reference wing (HERA) for the MF DEMO design development, with tabular details on the geometrical features.  
entirely from [24]. The same reference wing was also equipped with morphing ailerons developed by PoliiMi [25], whose architecture is described in [26].
## Materials and Experimental Program
### Tests  
The compressive strength of all the hardened paste samples was measured using a universal testing machine (1500 HDX; INSTRON, Norwood, MA, USA) at a loading rate of 0.4 mm/min. The XRD measurement was performed on powder samples using a high power X-ray diffractometer (D/MAX 2500 V/PC; Rigaku, Tokyo, Japan) with an incident beam of Cu-K\(\alpha\) radiation (\(\lambda\) = 1.5418 A) and a 5\({}^{\circ}\) to 60\({}^{\circ}\) 2\(\theta\) scanning range. The TG measurement was performed in a nitrogen gas environment on powdered samples at a temperature range from 30 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min using a thermal analyzer (SDT Q600; TA instruments, New Castle, DE, USA). The MIP for examining pore size distributions of the hardened samples was conducted using a mercury porosimeter (AutoPore IV 9500; Micromerities, Norcross, GA, USA), which was capable of pressure ranging from 0.0007 to 414 MPa (60,000 psi).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Group**} & **Sample** & \multicolumn{2}{c}{**Activator Type**} & \multicolumn{2}{c}{**GGBFS Source**} & \multicolumn{2}{c}{**CaSO\({}_{4}\) Type**} & \multicolumn{2}{c}{**Tests**} \\ \cline{3-13}  & **Label** & **CaO** & **NaOH** & **Type A** & **Type B** & **Anhydrite** & **Gypsum** & & **S** & **X** & **T** & **M** \\ \hline \multirow{8}{*}{CaO-activation} & C-A & 5 & - & 95 & - & - & - & 40 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & C-B & 5 & - & - & 95 & - & - & 40 & \(\bullet\) & \(\bullet\) \\  & CA-A & 5 & - & 90 & - & 5 & - & 40 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & CA-B & 5 & - & - & 90 & 5 & - & 40 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & CG-A & 5 & - & 90 & - & - & 5.7 & 39.3 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & CG-B & 5 & - & - & 90 & - & 5.7 & 39.3 & \(\bullet\) & & \\ \hline \multirow{8}{*}{NaOH-activation} & N-A & - & 5 & 95 & - & - & - & 40 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\  & N-B & - & 5 & - & 95 & - & - & 40 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \cline{1-1}  & N-A & - & 5 & 90 & - & 5 & - & 40 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \cline{1-1}  & NA-A & - & 5 & - & 90 & 5 & - & 40 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \cline{1-1}  & NA-B & - & 5 & - & 90 & - & 5.7 & 39.3 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \cline{1-1}  & NG-A & - & 5 & 90 & - & - & 5.7 & 39.3 & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \cline{1-1}  & NG-B & - & 5 & - & 90 & - & 5.7 & 39.3 & \(\bullet\) & & \\ \hline \hline \end{tabular} Note: S: compressive strength test; X: X-ray diffraction (XRD), T: thermogravimetry (TG); M: mercury intrusion porosimetry (MIP); \(\bullet\): test conducted. Strength tests were conducted at 1, 3, 7, and 28 days; XRD was taken at 7 and 28 days; TG was performed at 28 days, and MIP was conducted at 7 and 28 days. The same wt.% of CaSO\({}_{4}\) is contained in 5.00% of anhydrite and 5.70% of gypsum.  
\end{table}
Table 2: Mixture proportions of prepared paste samples in wt.% and conducted tests.
## 2 Methodology and Data
### CHAID Decision Tree  
CHAID (Chi-squared Automatic Interaction Detection) is a decision tree algorithm suitable for supervised classification or regression learning with categorical target variables. It was introduced in the study by [49]. In this study, we used it for classification tasks. Compared with other decision tree algorithms, CHAID has some unique features; for example, it allows splitting the node into more than two subsets, leading to more accurate and interpretable models [50].  
The algorithm begins with the entire dataset and then selects the predictor variable and category that best separates the data into two subsets based on the chi-squared statistic. The data are split into two or more subsets based on the values of that variable. The significance of each split is evaluated using the chi-square statistic, which measures the independence between the response variable and each predictor variable. In more detail, the chi-squared statistic is used to evaluate the statistical significance of the differences in the target variable across the categories of the predictor variable. If the chi-squared statistic is significant at a given significance level, the split is considered valid, and the algorithm proceeds to split the data further. If it is insignificant, the algorithm stops and considers the current node a leaf node [51].  
The process is repeated until a stopping criterion is met, such as a minimum number of observations per leaf or a maximum tree depth. In this study, we used the following stopping criteria: a maximum tree depth of 5; minimum records in the parent branch at 2% and in the child branch at 1% of cases in the training sample; a minimum change in the expected call frequencies for the chi-square test of 0.001; maximum iterations for convergence of 100. Similarly, as in the CART method, the tree can be pruned to reduce overfitting and improve generalisation performance, as the possibility of overfitting belongs to its main disadvantages.
# Nonsurgical Correction of Anterior Crossbite with Extra-Radicular Miniscrews--A Case Report
## 3 Case 2  
### Diagnosis and Etiology  
A 24-year-old female patient requested orthodontic treatment with the chief complaint of mandibular prognathism and anterior crossbites. Her frontal facial photograph showed mild facial asymmetry with a slight chin deviation to the right. Her vertical proportions were within normal limits. Maximly anterior malalignment was evident in her smiling facial photograph. The lateral profile view showed a concave profile because of mandibular prognathism (Figure 9).  
Figure 8: Case 1, Profile comparisons. (**A**) pretreatment (**B**) nonextraction approach (**C**) posttreatment with the four premolar extraction approach.  
Figure 7: Case 1, Cephalometric superimpositions show the correction of anterior crossbite and Class III malocclusion with four premolar extraction approach.  
Anterior crossbites and a deep overbite can be seen in the intraoral frontal photograph. There was no functional shift of the anterior crossbite to achieve an edge-to-edge relationship. Dental midline discrepancy was also noted. The maxillary dental midline was shifted to the right by 2 mm relative to the facial midline. The mandibular dental midline was further shifted to the right by another 2 mm relative to the maxillary dental midline. She showed severe crowding (approximately 6.5 mm) in the maxillary arch, and her maxillary right second molar was missing. The mandibular arch had mild crowding (approximately 1.5 mm). End-on, Class III dental relationships were noted.  
A panoramic radiograph showed horizontal impaction of the mandibular third molars and evidence of endodontic treatment of the maxillary right central incisor and maxillary right second premolar (Figure 10). A lateral cephalogram revealed a skeletal Class III pattern (ANB, \(-4^{\circ}\); Wits, \(-12.7\) mm). The mandibular plane angle was within the normal range (SN-MP, \(28.5^{\circ}\)). The maxillary incisors showed normal inclination, while the mandibular incisors were proclined (U1-SN, \(105.7^{\circ}\); L1-MP, \(82.0^{\circ}\)) (Figure 10, Table 2).  
Figure 10: Case 2, Pretreatment panoramic radiograph and lateral cephalogram.  
Figure 9: Case 2, Pretreatment facial and intraoral photographs.  
The patient reported that others in her family had prognathic mandibles. She first noted the anterior crossbite when her permanent incisors erupted at about age 6. Both genetic and environmental factors contributed to her Class III malocclusion.
## 2 Subject and Methods
### Assessment Methods
#### 2.7.2 Secondary Outcome  
Secondary outcome measures included inspiratory muscle activity and pulmonary function. Wireless surface electromyography (Wave-Wireless EMG, Cometa, Italy) was used for the evaluation of inspiratory muscle activity. UT and LD muscles on the paretic side were selected for the measurement based on previous studies [25]. EMG measurements were performed in a sitting position. To measurethe activity of UT, the electrode was attached to the center of the line joining the acromion of the scapula and the C7 vertebra. To measure the activity of LD, an electrode was attached to the inferior angle of the scapula corresponding to the position of the T10 vertebra. Since it is difficult to calculate accurate maximal voluntary contraction values in stroke patients, normalization was performed using percentage reference voluntary contraction (RVCs) [26; 27]. All subjects underwent a voluntary inspiratory contraction in a sitting position. Subjects maintained a voluntary inspiratory contraction for 5 s at the end of forced inspiration. For data analysis, assessor collected 3 s in the middle of 5 s contractions (excluding the initial 1 s and the final 1 s). Between trials, all subjects were given a 1 min break. The average of three trials for each muscle activity was taken as RVC. The EMG signals collected during involuntary breathing (automatic, unconscious process) were expressed as a percentage of the calculated RMS of RVCs (%RVC). To remove noise, band pass filtering was performed at 10-200 Hz of raw data and rectification was performed to root mean square 20 ms. For the removal of ECG artifacts, the evaluator manually selects EMG signal parts in between QRS complexes. The EMG sampling rate used in the present study was 2000 Hz. After removing foreign substances from the skin surface before EMG measurement, the electrode was wiped with medical alcohol and attached to the muscle belly.  
Pulmonary function measures included FVC, FEV1, and PEF measured by a spirometer (MicroLab ML3500 MK6, CareFusion, Chatham, UK). In the preparatory phase of the measurement, the subjects were asked to wear a nose clip and a disposable mouthpiece. After breathing at rest for three times, maximum possible inspiration was performed followed by a strong exhalation for 6 s.